diff --git a/week06/hw/kuvaldin_anton.ipynb b/week06/hw/kuvaldin_anton.ipynb
new file mode 100644
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--- /dev/null
+++ b/week06/hw/kuvaldin_anton.ipynb
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\n",
+                  "text/plain": "<Figure size 1152x360 with 2 Axes>"
+                },
+                "metadata": {
+                  "needs_background": "light"
+                }
+              }
+            ],
+            "_view_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_view_count": null,
+            "_view_module_version": "1.0.0",
+            "layout": "IPY_MODEL_5de403e1051b486f850552af9e0caa27",
+            "_model_module": "@jupyter-widgets/output"
+          }
+        },
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+          "model_name": "OutputModel",
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+          "state": {
+            "_view_name": "OutputView",
+            "msg_id": "",
+            "_dom_classes": [],
+            "_model_name": "OutputModel",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
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+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "38SUY3Ydoy9X"
+      },
+      "source": [
+        "Кувалдин Антон  \n",
+        "М05-014в  \n",
+        "Домашнее задание 2. Spotter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DLqUa76aAHIg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bb2333c7-8098-4479-a1a3-e132b5787105"
+      },
+      "source": [
+        "!apt install sox"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Reading package lists... Done\n",
+            "Building dependency tree       \n",
+            "Reading state information... Done\n",
+            "The following additional packages will be installed:\n",
+            "  libmagic-mgc libmagic1 libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa\n",
+            "  libsox-fmt-base libsox3\n",
+            "Suggested packages:\n",
+            "  file libsox-fmt-all\n",
+            "The following NEW packages will be installed:\n",
+            "  libmagic-mgc libmagic1 libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa\n",
+            "  libsox-fmt-base libsox3 sox\n",
+            "0 upgraded, 8 newly installed, 0 to remove and 37 not upgraded.\n",
+            "Need to get 760 kB of archives.\n",
+            "After this operation, 6,717 kB of additional disk space will be used.\n",
+            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libopencore-amrnb0 amd64 0.1.3-2.1 [92.0 kB]\n",
+            "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libopencore-amrwb0 amd64 0.1.3-2.1 [45.8 kB]\n",
+            "Get:3 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libmagic-mgc amd64 1:5.32-2ubuntu0.4 [184 kB]\n",
+            "Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libmagic1 amd64 1:5.32-2ubuntu0.4 [68.6 kB]\n",
+            "Get:5 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox3 amd64 14.4.2-3ubuntu0.18.04.1 [226 kB]\n",
+            "Get:6 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox-fmt-alsa amd64 14.4.2-3ubuntu0.18.04.1 [10.6 kB]\n",
+            "Get:7 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox-fmt-base amd64 14.4.2-3ubuntu0.18.04.1 [32.1 kB]\n",
+            "Get:8 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 sox amd64 14.4.2-3ubuntu0.18.04.1 [101 kB]\n",
+            "Fetched 760 kB in 1s (614 kB/s)\n",
+            "Selecting previously unselected package libopencore-amrnb0:amd64.\n",
+            "(Reading database ... 155047 files and directories currently installed.)\n",
+            "Preparing to unpack .../0-libopencore-amrnb0_0.1.3-2.1_amd64.deb ...\n",
+            "Unpacking libopencore-amrnb0:amd64 (0.1.3-2.1) ...\n",
+            "Selecting previously unselected package libopencore-amrwb0:amd64.\n",
+            "Preparing to unpack .../1-libopencore-amrwb0_0.1.3-2.1_amd64.deb ...\n",
+            "Unpacking libopencore-amrwb0:amd64 (0.1.3-2.1) ...\n",
+            "Selecting previously unselected package libmagic-mgc.\n",
+            "Preparing to unpack .../2-libmagic-mgc_1%3a5.32-2ubuntu0.4_amd64.deb ...\n",
+            "Unpacking libmagic-mgc (1:5.32-2ubuntu0.4) ...\n",
+            "Selecting previously unselected package libmagic1:amd64.\n",
+            "Preparing to unpack .../3-libmagic1_1%3a5.32-2ubuntu0.4_amd64.deb ...\n",
+            "Unpacking libmagic1:amd64 (1:5.32-2ubuntu0.4) ...\n",
+            "Selecting previously unselected package libsox3:amd64.\n",
+            "Preparing to unpack .../4-libsox3_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
+            "Unpacking libsox3:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
+            "Selecting previously unselected package libsox-fmt-alsa:amd64.\n",
+            "Preparing to unpack .../5-libsox-fmt-alsa_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
+            "Unpacking libsox-fmt-alsa:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
+            "Selecting previously unselected package libsox-fmt-base:amd64.\n",
+            "Preparing to unpack .../6-libsox-fmt-base_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
+            "Unpacking libsox-fmt-base:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
+            "Selecting previously unselected package sox.\n",
+            "Preparing to unpack .../7-sox_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
+            "Unpacking sox (14.4.2-3ubuntu0.18.04.1) ...\n",
+            "Setting up libmagic-mgc (1:5.32-2ubuntu0.4) ...\n",
+            "Setting up libmagic1:amd64 (1:5.32-2ubuntu0.4) ...\n",
+            "Setting up libopencore-amrnb0:amd64 (0.1.3-2.1) ...\n",
+            "Setting up libopencore-amrwb0:amd64 (0.1.3-2.1) ...\n",
+            "Setting up libsox3:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
+            "Setting up libsox-fmt-base:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
+            "Setting up libsox-fmt-alsa:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
+            "Setting up sox (14.4.2-3ubuntu0.18.04.1) ...\n",
+            "Processing triggers for libc-bin (2.27-3ubuntu1.3) ...\n",
+            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
+            "\n",
+            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
+            "Processing triggers for mime-support (3.60ubuntu1) ...\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eX_UvnL9FOB2"
+      },
+      "source": [
+        "### Baseline commands recognition (2-5 points)\n",
+        "\n",
+        "We're now going to train a classifier to recognize voice. More specifically, we'll use the [Speech Commands Dataset] that contains around 30 different words with a few thousand voice records each."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FciA_-ckZvyv"
+      },
+      "source": [
+        "import os\n",
+        "from IPython.display import display, Audio\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "import numpy as np\n",
+        "import librosa\n",
+        "import torch\n",
+        "from torch.utils.data import TensorDataset, DataLoader\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from itertools import chain\n",
+        "from tqdm.notebook import tqdm\n",
+        "import joblib as jl\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "import torch.optim as optim"
+      ],
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FvHkw2rfY9k7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c46f882d-035e-442c-f9af-c56dc6593f61"
+      },
+      "source": [
+        "datadir = \"speech_commands\"\n",
+        "\n",
+        "!wget http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz -O speech_commands_v0.01.tar.gz\n",
+        "# alternative url: https://www.dropbox.com/s/j95n278g48bcbta/speech_commands_v0.01.tar.gz?dl=1\n",
+        "!mkdir {datadir} && tar -C {datadir} -xvzf speech_commands_v0.01.tar.gz 1> log\n",
+        "\n",
+        "samples_by_target = {\n",
+        "    cls: [os.path.join(datadir, cls, name) for name in os.listdir(\"./speech_commands/{}\".format(cls))]\n",
+        "    for cls in os.listdir(datadir)\n",
+        "    if os.path.isdir(os.path.join(datadir, cls))\n",
+        "}\n",
+        "print('Classes:', ', '.join(sorted(samples_by_target.keys())[1:]))"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2021-10-26 18:37:22--  http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz\n",
+            "Resolving download.tensorflow.org (download.tensorflow.org)... 74.125.135.128, 2607:f8b0:400e:c01::80\n",
+            "Connecting to download.tensorflow.org (download.tensorflow.org)|74.125.135.128|:80... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 1489096277 (1.4G) [application/gzip]\n",
+            "Saving to: ‘speech_commands_v0.01.tar.gz’\n",
+            "\n",
+            "speech_commands_v0. 100%[===================>]   1.39G   242MB/s    in 5.8s    \n",
+            "\n",
+            "2021-10-26 18:37:28 (243 MB/s) - ‘speech_commands_v0.01.tar.gz’ saved [1489096277/1489096277]\n",
+            "\n",
+            "Classes: bed, bird, cat, dog, down, eight, five, four, go, happy, house, left, marvin, nine, no, off, on, one, right, seven, sheila, six, stop, three, tree, two, up, wow, yes, zero\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ME4cVShQ916w",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ca3f74c4-571e-4100-a5a3-296e4f9c0e60"
+      },
+      "source": [
+        "!sox --info speech_commands/bed/00176480_nohash_0.wav"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Input File     : 'speech_commands/bed/00176480_nohash_0.wav'\n",
+            "Channels       : 1\n",
+            "Sample Rate    : 16000\n",
+            "Precision      : 16-bit\n",
+            "Duration       : 00:00:01.00 = 16000 samples ~ 75 CDDA sectors\n",
+            "File Size      : 32.0k\n",
+            "Bit Rate       : 256k\n",
+            "Sample Encoding: 16-bit Signed Integer PCM\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
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+          ]
+        },
+        "id": "cvF5l-PCyd8z",
+        "outputId": "162c4645-c420-4e8e-83af-365f10c1c2b8"
+      },
+      "source": [
+        "classes = (\"left\", \"right\", \"up\", \"down\", \"stop\")\n",
+        "\n",
+        "def preprocess_sample(filepath, max_length=160):\n",
+        "    amplitudes, sr = librosa.core.load(filepath)\n",
+        "    spectrogram = librosa.feature.melspectrogram(amplitudes, sr=sr)[:, :max_length]\n",
+        "    spectrogram = np.pad(spectrogram, [[0, 0], [0, max(0, max_length - spectrogram.shape[1])]], mode='constant')\n",
+        "    target = classes.index(filepath.split(os.sep)[-2])\n",
+        "    return np.float32(spectrogram), np.int64(target)\n",
+        "\n",
+        "all_files = chain(*(samples_by_target[cls] for cls in classes))\n",
+        "spectrograms_and_targets = jl.Parallel(n_jobs=-1)(tqdm(list(map(jl.delayed(preprocess_sample), all_files))))\n",
+        "X, y = map(np.stack, zip(*spectrograms_and_targets))\n",
+        "X = X.transpose([0, 2, 1])  # to [batch, time, channels]\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "607c0647169f4e25a0f98dc914a43249",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/11834 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GBLCjoNpbHBu"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ke9CCSf1JPDu",
+        "outputId": "eb58b7e2-0c82-46d9-8532-c0a8cdf498a9"
+      },
+      "source": [
+        "print('Train before: {}'.format(X_train.shape))\n",
+        "print('Test before: {}'.format(X_test.shape))\n",
+        "X_train = np.expand_dims(X_train, axis=1)\n",
+        "X_test = np.expand_dims(X_test, axis=1)\n",
+        "print('Train after: {}'.format(X_train.shape))\n",
+        "print('Test after: {}'.format(X_test.shape))"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train before: (8875, 160, 128)\n",
+            "Test before: (2959, 160, 128)\n",
+            "Train after: (8875, 1, 160, 128)\n",
+            "Test after: (2959, 1, 160, 128)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7Ol6sywTG_Y9"
+      },
+      "source": [
+        "if torch.cuda.is_available:\n",
+        "  device = 'cuda'\n",
+        "else:\n",
+        "  device = 'cpu'\n",
+        "\n",
+        "batch_size = 16\n",
+        "\n",
+        "tensor_x = torch.Tensor(X_train)\n",
+        "tensor_y = torch.LongTensor(y_train)\n",
+        "\n",
+        "train_dataset = TensorDataset(tensor_x, tensor_y)\n",
+        "\n",
+        "tensor_x = torch.Tensor(X_test) # transform to torch tensor\n",
+        "tensor_y = torch.LongTensor(y_test)\n",
+        "\n",
+        "test_dataset = TensorDataset(tensor_x, tensor_y)\n",
+        "\n",
+        "\n",
+        "trainloader = DataLoader(train_dataset, batch_size=batch_size,\n",
+        "                         shuffle=True, num_workers=2)\n",
+        "testloader = DataLoader(test_dataset, batch_size=batch_size,\n",
+        "                        shuffle=False, num_workers=2)"
+      ],
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ic-Qhx3gDv57",
+        "outputId": "455bd2f0-8e0c-406c-ed92-e97d54a7c9f5"
+      },
+      "source": [
+        "X_train.shape"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(8875, 1, 160, 128)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-qr8t6wCF8vT"
+      },
+      "source": [
+        "class Net(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        # TODO: define your layers here\n",
+        "        self.conv1 = nn.Conv2d(1, 64, 5)\n",
+        "        self.bn1 = nn.BatchNorm2d(64)\n",
+        "        self.dropout1 = nn.Dropout(0.2)\n",
+        "        self.conv2 = nn.Conv2d(64, 128, 5)\n",
+        "        self.bn2 = nn.BatchNorm2d(128)\n",
+        "        self.pool1 = nn.MaxPool2d(2)\n",
+        "        self.conv3 = nn.Conv2d(128, 256, 5)\n",
+        "        self.bn3 = nn.BatchNorm2d(256)\n",
+        "        self.pool2 = nn.MaxPool2d(4)\n",
+        "        self.conv4 = nn.Conv2d(256, 256, 5)\n",
+        "        self.bn4 = nn.BatchNorm2d(256)\n",
+        "        self.pool3 = nn.MaxPool2d(6)\n",
+        "        self.flatten = nn.Flatten()\n",
+        "        self.dense1 = nn.Linear(512, 256)\n",
+        "        self.dense2 = nn.Linear(256, 5)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # TODO: apply your layers here\n",
+        "        x = self.conv1(x)\n",
+        "        x = self.bn1(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.dropout1(x)\n",
+        "        x = self.conv2(x)\n",
+        "        x = self.bn2(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.pool1(x)\n",
+        "        x = self.conv3(x)\n",
+        "        x = self.bn3(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.pool2(x)\n",
+        "        x = self.conv4(x)\n",
+        "        x = self.bn4(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.pool3(x)\n",
+        "        x = self.flatten(x)\n",
+        "        x = self.dense1(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.dense2(x)\n",
+        "        x = F.softmax(x)\n",
+        "        return x\n",
+        "\n",
+        "\n",
+        "net = Net().to(device)"
+      ],
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5WAwho2XP0He"
+      },
+      "source": [
+        "def get_accuracy(loader, model):\n",
+        "    num_correct = 0\n",
+        "    num_samples = 0\n",
+        "    model.eval()\n",
+        "    \n",
+        "    with torch.no_grad():\n",
+        "        for x, y in loader:\n",
+        "            x = x.to(device)\n",
+        "            y = y.to(device)\n",
+        "            \n",
+        "            scores = model(x)\n",
+        "            _, predictions = scores.max(1)\n",
+        "            num_correct += (predictions == y).sum()\n",
+        "            num_samples += predictions.size(0)\n",
+        "        \n",
+        "        #print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}') \n",
+        "    \n",
+        "    model.train()\n",
+        "    return float(num_correct)/float(num_samples)*100"
+      ],
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "11QtW-7MRzCz"
+      },
+      "source": [
+        "import seaborn as sns\n",
+        "sns.set()\n",
+        "from IPython.display import clear_output\n",
+        "\n",
+        "import time\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tqdm.auto import trange\n",
+        "\n",
+        "from IPython import display\n",
+        "from ipywidgets import Output\n",
+        "\n",
+        "def plot_progress(losses, scores, disp):\n",
+        "  with disp:\n",
+        "    fig, ax = plt.subplots(1, 2, figsize=(16, 5))\n",
+        "    ax[0].plot([i*100 for i in range(len(losses))], losses)\n",
+        "    ax[1].plot([i*100 for i in range(len(scores))], scores)\n",
+        "    ax[0].scatter([i*100 for i in range(len(losses))], losses, color='red')\n",
+        "    ax[1].scatter([i*100 for i in range(len(scores))], scores, color='red')\n",
+        "\n",
+        "\n",
+        "    ax[0].set(xlabel='samples', ylabel='loss',\n",
+        "          title=f'Training loss')\n",
+        "    ax[1].set(xlabel='samples', ylabel='accuracy',\n",
+        "          title=f'Test accuracy')\n",
+        "    clear_output(wait=True)\n",
+        "    plt.show()\n",
+        "  time.sleep(1)"
+      ],
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xR1uxQ-GGGLr",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 372
+        },
+        "outputId": "4cc36e7d-5a88-4003-b448-6f0c87f9b4e1"
+      },
+      "source": [
+        "losses = []\n",
+        "scores = []\n",
+        "out = Output()\n",
+        "display.display(out)\n",
+        "\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
+        "\n",
+        "for epoch in range(15):  # loop over the dataset multiple times\n",
+        "\n",
+        "    running_loss = 0.0\n",
+        "    for i, data in tqdm(enumerate(trainloader, 0), total=len(trainloader)):\n",
+        "        # get the inputs; data is a list of [inputs, labels]\n",
+        "        inputs, labels = data\n",
+        "        inputs = inputs.to(device)\n",
+        "        labels = labels.to(device)\n",
+        "\n",
+        "        # zero the parameter gradients\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        "        # forward + backward + optimize\n",
+        "        outputs = net(inputs)\n",
+        "        loss = criterion(outputs, labels)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        # print statistics\n",
+        "        running_loss += loss.item()\n",
+        "        if i % 100 == 99:    # print every 100 mini-batches\n",
+        "            acc = get_accuracy(testloader, net)\n",
+        "            print('[%d, %5d] loss: %.3f, accuracy: %.3f' %\n",
+        "                  (epoch + 1, i + 1, running_loss / 100, acc))\n",
+        "            losses.append(running_loss / 100)\n",
+        "            scores.append(acc)\n",
+        "            plot_progress(losses, scores, out)\n",
+        "            running_loss = 0.0\n",
+        "\n",
+        "print('Finished Training')"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1152x360 with 2 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Finished Training\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z_FwbUID5y3Z"
+      },
+      "source": [
+        "Получили уже очень хороший результат на baseline. Попробуем улучшить результат"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KhHHlZ_fqLOj",
+        "outputId": "22cba0e8-2f69-43af-a3d2-4f37cdeba5ce"
+      },
+      "source": [
+        "from torchsummary import summary\n",
+        "summary(net, (1, 160, 128))"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "----------------------------------------------------------------\n",
+            "        Layer (type)               Output Shape         Param #\n",
+            "================================================================\n",
+            "            Conv2d-1         [-1, 64, 156, 124]           1,664\n",
+            "       BatchNorm2d-2         [-1, 64, 156, 124]             128\n",
+            "           Dropout-3         [-1, 64, 156, 124]               0\n",
+            "            Conv2d-4        [-1, 128, 152, 120]         204,928\n",
+            "       BatchNorm2d-5        [-1, 128, 152, 120]             256\n",
+            "         MaxPool2d-6          [-1, 128, 76, 60]               0\n",
+            "            Conv2d-7          [-1, 256, 72, 56]         819,456\n",
+            "       BatchNorm2d-8          [-1, 256, 72, 56]             512\n",
+            "         MaxPool2d-9          [-1, 256, 18, 14]               0\n",
+            "           Conv2d-10          [-1, 256, 14, 10]       1,638,656\n",
+            "      BatchNorm2d-11          [-1, 256, 14, 10]             512\n",
+            "        MaxPool2d-12            [-1, 256, 2, 1]               0\n",
+            "          Flatten-13                  [-1, 512]               0\n",
+            "           Linear-14                  [-1, 256]         131,328\n",
+            "           Linear-15                    [-1, 5]           1,285\n",
+            "================================================================\n",
+            "Total params: 2,798,725\n",
+            "Trainable params: 2,798,725\n",
+            "Non-trainable params: 0\n",
+            "----------------------------------------------------------------\n",
+            "Input size (MB): 0.08\n",
+            "Forward/backward pass size (MB): 85.21\n",
+            "Params size (MB): 10.68\n",
+            "Estimated Total Size (MB): 95.97\n",
+            "----------------------------------------------------------------\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:43: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ojEuXjx5DlDW"
+      },
+      "source": [
+        "Train a model: finally, lets' build and train a classifier neural network. You can use any library you like. If in doubt, consult the model & training tips below."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hwgnOrZy1E8p"
+      },
+      "source": [
+        "__Training tips:__ here's what you can try:\n",
+        "* __Layers:__ 1d or 2d convolutions, perhaps with some batch normalization in between;\n",
+        "* __Architecture:__ VGG-like, residual, highway, densely-connected, MatchboxNet, Dilated convs - you name it :)\n",
+        "* __Batch size matters:__ smaller batches usually train slower but better. Try to find the one that suits you best.\n",
+        "* __Data augmentation:__ add background noise, faster/slower, change pitch;\n",
+        "* __Average checkpoints:__ you can make model more stable with [this simple technique (arxiv)](https://arxiv.org/abs/1803.05407)\n",
+        "* __For full scale stage:__ make sure you're not losing too much data due to max_length in the pre-processing stage!\n",
+        "\n",
+        "These are just recommendations. As long as your model works, you're not required to follow them."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Oc15vA7qv4QN"
+      },
+      "source": [
+        "#### Densely-connected"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kzvGktg0wGY3"
+      },
+      "source": [
+        "Реализуем densely-connected и посмотрим на результат  \n",
+        "туториал https://towardsdatascience.com/simple-implementation-of-densely-connected-convolutional-networks-in-pytorch-3846978f2f36  \n",
+        "\n",
+        "Также добавим scheduler, умножающий learning rate на 0.1 каждую пятую эпоху"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "J5vfMLrcpS76"
+      },
+      "source": [
+        "class Dense_Block(nn.Module):\n",
+        "  def __init__(self, in_channels):\n",
+        "    super(Dense_Block, self).__init__()\n",
+        "    self.relu = nn.ReLU(inplace = True)\n",
+        "    self.bn = nn.BatchNorm2d(in_channels)\n",
+        "\n",
+        "    self.conv1 = nn.Conv2d(in_channels = in_channels, out_channels = 32, kernel_size = 3, stride = 1, padding = 1)\n",
+        "    self.conv2 = nn.Conv2d(in_channels = 32, out_channels = 32, kernel_size = 3, stride = 1, padding = 1)\n",
+        "    self.conv3 = nn.Conv2d(in_channels = 64, out_channels = 32, kernel_size = 3, stride = 1, padding = 1)\n",
+        "    self.conv4 = nn.Conv2d(in_channels = 96, out_channels = 32, kernel_size = 3, stride = 1, padding = 1)\n",
+        "    self.conv5 = nn.Conv2d(in_channels = 128, out_channels = 32, kernel_size = 3, stride = 1, padding = 1)\n",
+        "  \n",
+        "  def forward(self, x):\n",
+        "    bn = self.bn(x)\n",
+        "    conv1 = self.relu(self.conv1(bn))\n",
+        "    conv2 = self.relu(self.conv2(conv1))\n",
+        "    # Concatenate in channel dimension\n",
+        "    c2_dense = self.relu(torch.cat([conv1, conv2], 1))\n",
+        "    conv3 = self.relu(self.conv3(c2_dense))\n",
+        "    c3_dense = self.relu(torch.cat([conv1, conv2, conv3], 1))\n",
+        "\n",
+        "    conv4 = self.relu(self.conv4(c3_dense))\n",
+        "    c4_dense = self.relu(torch.cat([conv1, conv2, conv3, conv4], 1))\n",
+        "\n",
+        "    conv5 = self.relu(self.conv5(c4_dense))\n",
+        "    c5_dense = self.relu(torch.cat([conv1, conv2, conv3, conv4, conv5], 1))\n",
+        "\n",
+        "    return c5_dense\n",
+        "\n",
+        "class Transition_Layer(nn.Module): \n",
+        "  def __init__(self, in_channels, out_channels):\n",
+        "    super(Transition_Layer, self).__init__() \n",
+        "\n",
+        "    self.relu = nn.ReLU(inplace = True) \n",
+        "    self.bn = nn.BatchNorm2d(num_features = out_channels) \n",
+        "    self.conv = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 1, bias = False) \n",
+        "    self.avg_pool = nn.AvgPool2d(kernel_size = 2, stride = 2, padding = 0) \n",
+        "\n",
+        "  def forward(self, x): \n",
+        "    bn = self.bn(self.relu(self.conv(x))) \n",
+        "    out = self.avg_pool(bn) \n",
+        "    return out\n",
+        "\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "\n",
+        "class NetDense(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        # TODO: define your layers here\n",
+        "        self.conv1 = nn.Conv2d(1, 64, 5)\n",
+        "        self.bn1 = nn.BatchNorm2d(64)\n",
+        "        self.dropout1 = nn.Dropout(0.2)\n",
+        "        self.conv2 = nn.Conv2d(64, 128, 5)\n",
+        "        self.bn2 = nn.BatchNorm2d(128)\n",
+        "        self.pool1 = nn.MaxPool2d(2)\n",
+        "        # Make Dense Blocks \n",
+        "        self.denseblock1 = self._make_dense_block(Dense_Block, 128) \n",
+        "        self.denseblock2 = self._make_dense_block(Dense_Block, 160)\n",
+        "        self.denseblock3 = self._make_dense_block(Dense_Block, 160)\n",
+        "        # Make transition Layers \n",
+        "        self.transitionLayer1 = self._make_transition_layer(Transition_Layer, in_channels = 160, out_channels = 128) \n",
+        "        self.transitionLayer2 = self._make_transition_layer(Transition_Layer, in_channels = 128, out_channels = 128) \n",
+        "        self.transitionLayer3 = self._make_transition_layer(Transition_Layer, in_channels = 128, out_channels = 128)\n",
+        "        self.conv3 = nn.Conv2d(128, 256, 5)\n",
+        "        self.bn3 = nn.BatchNorm2d(256)\n",
+        "        self.pool2 = nn.MaxPool2d(3)\n",
+        "        # self.conv4 = nn.Conv2d(256, 128, 5)\n",
+        "        # self.bn4 = nn.BatchNorm2d(128)\n",
+        "        # self.pool3 = nn.MaxPool2d(6)\n",
+        "        self.flatten = nn.Flatten()\n",
+        "        self.dense1 = nn.Linear(256, 64)\n",
+        "        self.dense2 = nn.Linear(64, 5)\n",
+        "\n",
+        "    def _make_dense_block(self, block, in_channels): \n",
+        "      layers = [] \n",
+        "      layers.append(block(in_channels)) \n",
+        "      return nn.Sequential(*layers) \n",
+        "    def _make_transition_layer(self, layer, in_channels, out_channels): \n",
+        "      modules = [] \n",
+        "      modules.append(layer(in_channels, out_channels)) \n",
+        "      return nn.Sequential(*modules) \n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # TODO: apply your layers here\n",
+        "        x = self.conv1(x)\n",
+        "        x = self.bn1(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.dropout1(x)\n",
+        "        x = self.conv2(x)\n",
+        "        x = self.bn2(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.pool1(x)\n",
+        "        x = self.denseblock1(x)\n",
+        "        x = self.denseblock2(x)\n",
+        "        x = self.denseblock3(x)\n",
+        "        x = self.transitionLayer1(x)\n",
+        "        x = self.transitionLayer2(x)\n",
+        "        x = self.transitionLayer3(x)\n",
+        "        x = self.conv3(x)\n",
+        "        x = self.bn3(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.pool2(x)\n",
+        "        # x = self.conv4(x)\n",
+        "        # x = self.bn4(x)\n",
+        "        # x = F.relu(x)\n",
+        "        # x = self.pool3(x)\n",
+        "        x = self.flatten(x)\n",
+        "        x = self.dense1(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.dense2(x)\n",
+        "        x = F.softmax(x)\n",
+        "        return x\n",
+        "\n",
+        "\n",
+        "net_dense = NetDense().to(device)"
+      ],
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "60PS1D0IweI6",
+        "outputId": "fe86b090-4b18-42bd-a7c4-ae2a503c4995"
+      },
+      "source": [
+        "from torchsummary import summary\n",
+        "summary(net_dense, (1, 160, 128))"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "----------------------------------------------------------------\n",
+            "        Layer (type)               Output Shape         Param #\n",
+            "================================================================\n",
+            "            Conv2d-1         [-1, 64, 156, 124]           1,664\n",
+            "       BatchNorm2d-2         [-1, 64, 156, 124]             128\n",
+            "           Dropout-3         [-1, 64, 156, 124]               0\n",
+            "            Conv2d-4        [-1, 128, 152, 120]         204,928\n",
+            "       BatchNorm2d-5        [-1, 128, 152, 120]             256\n",
+            "         MaxPool2d-6          [-1, 128, 76, 60]               0\n",
+            "       BatchNorm2d-7          [-1, 128, 76, 60]             256\n",
+            "            Conv2d-8           [-1, 32, 76, 60]          36,896\n",
+            "              ReLU-9           [-1, 32, 76, 60]               0\n",
+            "           Conv2d-10           [-1, 32, 76, 60]           9,248\n",
+            "             ReLU-11           [-1, 32, 76, 60]               0\n",
+            "             ReLU-12           [-1, 64, 76, 60]               0\n",
+            "           Conv2d-13           [-1, 32, 76, 60]          18,464\n",
+            "             ReLU-14           [-1, 32, 76, 60]               0\n",
+            "             ReLU-15           [-1, 96, 76, 60]               0\n",
+            "           Conv2d-16           [-1, 32, 76, 60]          27,680\n",
+            "             ReLU-17           [-1, 32, 76, 60]               0\n",
+            "             ReLU-18          [-1, 128, 76, 60]               0\n",
+            "           Conv2d-19           [-1, 32, 76, 60]          36,896\n",
+            "             ReLU-20           [-1, 32, 76, 60]               0\n",
+            "             ReLU-21          [-1, 160, 76, 60]               0\n",
+            "      Dense_Block-22          [-1, 160, 76, 60]               0\n",
+            "      BatchNorm2d-23          [-1, 160, 76, 60]             320\n",
+            "           Conv2d-24           [-1, 32, 76, 60]          46,112\n",
+            "             ReLU-25           [-1, 32, 76, 60]               0\n",
+            "           Conv2d-26           [-1, 32, 76, 60]           9,248\n",
+            "             ReLU-27           [-1, 32, 76, 60]               0\n",
+            "             ReLU-28           [-1, 64, 76, 60]               0\n",
+            "           Conv2d-29           [-1, 32, 76, 60]          18,464\n",
+            "             ReLU-30           [-1, 32, 76, 60]               0\n",
+            "             ReLU-31           [-1, 96, 76, 60]               0\n",
+            "           Conv2d-32           [-1, 32, 76, 60]          27,680\n",
+            "             ReLU-33           [-1, 32, 76, 60]               0\n",
+            "             ReLU-34          [-1, 128, 76, 60]               0\n",
+            "           Conv2d-35           [-1, 32, 76, 60]          36,896\n",
+            "             ReLU-36           [-1, 32, 76, 60]               0\n",
+            "             ReLU-37          [-1, 160, 76, 60]               0\n",
+            "      Dense_Block-38          [-1, 160, 76, 60]               0\n",
+            "      BatchNorm2d-39          [-1, 160, 76, 60]             320\n",
+            "           Conv2d-40           [-1, 32, 76, 60]          46,112\n",
+            "             ReLU-41           [-1, 32, 76, 60]               0\n",
+            "           Conv2d-42           [-1, 32, 76, 60]           9,248\n",
+            "             ReLU-43           [-1, 32, 76, 60]               0\n",
+            "             ReLU-44           [-1, 64, 76, 60]               0\n",
+            "           Conv2d-45           [-1, 32, 76, 60]          18,464\n",
+            "             ReLU-46           [-1, 32, 76, 60]               0\n",
+            "             ReLU-47           [-1, 96, 76, 60]               0\n",
+            "           Conv2d-48           [-1, 32, 76, 60]          27,680\n",
+            "             ReLU-49           [-1, 32, 76, 60]               0\n",
+            "             ReLU-50          [-1, 128, 76, 60]               0\n",
+            "           Conv2d-51           [-1, 32, 76, 60]          36,896\n",
+            "             ReLU-52           [-1, 32, 76, 60]               0\n",
+            "             ReLU-53          [-1, 160, 76, 60]               0\n",
+            "      Dense_Block-54          [-1, 160, 76, 60]               0\n",
+            "           Conv2d-55          [-1, 128, 76, 60]          20,480\n",
+            "             ReLU-56          [-1, 128, 76, 60]               0\n",
+            "      BatchNorm2d-57          [-1, 128, 76, 60]             256\n",
+            "        AvgPool2d-58          [-1, 128, 38, 30]               0\n",
+            " Transition_Layer-59          [-1, 128, 38, 30]               0\n",
+            "           Conv2d-60          [-1, 128, 38, 30]          16,384\n",
+            "             ReLU-61          [-1, 128, 38, 30]               0\n",
+            "      BatchNorm2d-62          [-1, 128, 38, 30]             256\n",
+            "        AvgPool2d-63          [-1, 128, 19, 15]               0\n",
+            " Transition_Layer-64          [-1, 128, 19, 15]               0\n",
+            "           Conv2d-65          [-1, 128, 19, 15]          16,384\n",
+            "             ReLU-66          [-1, 128, 19, 15]               0\n",
+            "      BatchNorm2d-67          [-1, 128, 19, 15]             256\n",
+            "        AvgPool2d-68            [-1, 128, 9, 7]               0\n",
+            " Transition_Layer-69            [-1, 128, 9, 7]               0\n",
+            "           Conv2d-70            [-1, 256, 5, 3]         819,456\n",
+            "      BatchNorm2d-71            [-1, 256, 5, 3]             512\n",
+            "        MaxPool2d-72            [-1, 256, 1, 1]               0\n",
+            "          Flatten-73                  [-1, 256]               0\n",
+            "           Linear-74                   [-1, 64]          16,448\n",
+            "           Linear-75                    [-1, 5]             325\n",
+            "================================================================\n",
+            "Total params: 1,504,613\n",
+            "Trainable params: 1,504,613\n",
+            "Non-trainable params: 0\n",
+            "----------------------------------------------------------------\n",
+            "Input size (MB): 0.08\n",
+            "Forward/backward pass size (MB): 201.36\n",
+            "Params size (MB): 5.74\n",
+            "Estimated Total Size (MB): 207.18\n",
+            "----------------------------------------------------------------\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:113: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
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+            "a76e1c666eac4f39822279fbe88e6b15",
+            "2e906ffc9bf84013a529c9a393fa64a0",
+            "9c76584e1d844fdb96dd0e73dfbd2cae",
+            "11d9bb4261014ddb8d790bfaf1100bce",
+            "3475fd64c3f141fc825fa550601e3a3d",
+            "191bc5cbe4a14d0abc43f8dc7286aa66",
+            "bb11c74e81074f04994ff69c23c58caa",
+            "c059cf2603f243d6ae3536e443a048f0",
+            "6a7a022bed154aac9e2a9051e9c5e26d",
+            "54bcafd6deca44179b003614c8634a81"
+          ]
+        },
+        "id": "fQIMaX7SpS5V",
+        "outputId": "59863c65-1933-42fb-caf6-01baaf5688c9"
+      },
+      "source": [
+        "losses = []\n",
+        "scores = []\n",
+        "out = Output()\n",
+        "display(out)\n",
+        "\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "#optimizer = optim.Adam(net_dense.parameters(), lr=0.001)\n",
+        "optimizer = optim.SGD(net_dense.parameters(), lr=0.001, momentum=0.9)\n",
+        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)\n",
+        "\n",
+        "for epoch in range(10):  # loop over the dataset multiple times\n",
+        "    running_loss = 0.0\n",
+        "    for i, data in tqdm(enumerate(trainloader, 0), total=len(trainloader)):\n",
+        "        # get the inputs; data is a list of [inputs, labels]\n",
+        "        inputs, labels = data\n",
+        "        inputs = inputs.to(device)\n",
+        "        labels = labels.to(device)\n",
+        "\n",
+        "        # zero the parameter gradients\n",
+        "        optimizer.zero_grad()\n",
+        "        \n",
+        "        # forward + backward + optimize\n",
+        "        outputs = net_dense(inputs)\n",
+        "        loss = criterion(outputs, labels)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        # print statistics\n",
+        "        running_loss += loss.item()\n",
+        "        if i % 100 == 99:    # print every 100 mini-batches\n",
+        "            acc = get_accuracy(testloader, net_dense)\n",
+        "            print('[%d, %5d] loss: %.3f, accuracy: %.3f' %\n",
+        "                  (epoch + 1, i + 1, running_loss / 100, acc))\n",
+        "            losses.append(running_loss / 100)\n",
+        "            scores.append(acc)\n",
+        "            plot_progress(losses, scores, out)\n",
+        "            running_loss = 0.0\n",
+        "    scheduler.step()\n",
+        "\n",
+        "print('Finished Training')"
+      ],
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "87ec48515e31461abf18f037271f32b0",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f9d8c5c0f2274043956102f43e222275",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:113: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1,   100] loss: 0.985, accuracy: 53.261\n",
+            "[1,   200] loss: 0.972, accuracy: 53.870\n",
+            "[1,   300] loss: 0.990, accuracy: 67.726\n",
+            "[1,   400] loss: 0.975, accuracy: 70.260\n",
+            "[1,   500] loss: 0.973, accuracy: 45.522\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "aa8df6e8cba34acaa5a7d45af40fae25",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2,   100] loss: 0.963, accuracy: 67.692\n",
+            "[2,   200] loss: 0.977, accuracy: 77.729\n",
+            "[2,   300] loss: 0.974, accuracy: 69.483\n",
+            "[2,   400] loss: 0.966, accuracy: 59.209\n",
+            "[2,   500] loss: 0.973, accuracy: 53.498\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0b0cc7ef827944da9165c49df7b72949",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[3,   100] loss: 0.969, accuracy: 79.689\n",
+            "[3,   200] loss: 0.959, accuracy: 53.768\n",
+            "[3,   300] loss: 0.967, accuracy: 66.847\n",
+            "[3,   400] loss: 0.965, accuracy: 56.844\n",
+            "[3,   500] loss: 0.973, accuracy: 58.635\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4efd89a9743048fda856d3edf9cfec03",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[4,   100] loss: 0.966, accuracy: 49.172\n",
+            "[4,   200] loss: 0.969, accuracy: 66.340\n",
+            "[4,   300] loss: 0.963, accuracy: 47.246\n",
+            "[4,   400] loss: 0.962, accuracy: 63.062\n",
+            "[4,   500] loss: 0.969, accuracy: 64.988\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "cfb29a4109b743f8b47d98d3982d57e6",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[5,   100] loss: 0.949, accuracy: 68.165\n",
+            "[5,   200] loss: 0.960, accuracy: 73.707\n",
+            "[5,   300] loss: 0.968, accuracy: 66.408\n",
+            "[5,   400] loss: 0.958, accuracy: 62.420\n",
+            "[5,   500] loss: 0.966, accuracy: 46.874\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8662ba0c7219424ebf7ed87e47eb299f",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[6,   100] loss: 0.953, accuracy: 49.104\n",
+            "[6,   200] loss: 0.954, accuracy: 54.072\n",
+            "[6,   300] loss: 0.949, accuracy: 53.058\n",
+            "[6,   400] loss: 0.941, accuracy: 61.913\n",
+            "[6,   500] loss: 0.946, accuracy: 54.883\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "954df9c547b3439f9c13ecdab74415d0",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[7,   100] loss: 0.941, accuracy: 54.714\n",
+            "[7,   200] loss: 0.947, accuracy: 59.175\n",
+            "[7,   300] loss: 0.945, accuracy: 49.476\n",
+            "[7,   400] loss: 0.948, accuracy: 64.887\n",
+            "[7,   500] loss: 0.939, accuracy: 56.438\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f559daeed7ed48fb9923f481a20a23a3",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[8,   100] loss: 0.935, accuracy: 59.784\n",
+            "[8,   200] loss: 0.949, accuracy: 59.040\n",
+            "[8,   300] loss: 0.942, accuracy: 61.778\n",
+            "[8,   400] loss: 0.943, accuracy: 57.891\n",
+            "[8,   500] loss: 0.946, accuracy: 63.839\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "67f5bc57487e43778a8750b274570432",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[9,   100] loss: 0.947, accuracy: 56.201\n",
+            "[9,   200] loss: 0.939, accuracy: 69.956\n",
+            "[9,   300] loss: 0.942, accuracy: 67.252\n",
+            "[9,   400] loss: 0.938, accuracy: 61.440\n",
+            "[9,   500] loss: 0.936, accuracy: 57.148\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "211cee1669f64587bde272b6176f916a",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[10,   100] loss: 0.939, accuracy: 71.713\n",
+            "[10,   200] loss: 0.943, accuracy: 67.726\n",
+            "[10,   300] loss: 0.944, accuracy: 66.610\n",
+            "[10,   400] loss: 0.943, accuracy: 56.404\n",
+            "[10,   500] loss: 0.943, accuracy: 62.656\n",
+            "Finished Training\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OQqv7Qi6wTmh"
+      },
+      "source": [
+        "Результат получился хуже. Пробуем другой подход"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1LGfEciLzNXB"
+      },
+      "source": [
+        "#### VGG 19"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kEiyRwqtF97Q"
+      },
+      "source": [
+        "Источник:  \n",
+        "https://github.com/tugstugi/pytorch-speech-commands.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c_2ro7-rzQYC",
+        "outputId": "547bd8e7-210f-4a48-e956-14f570559268"
+      },
+      "source": [
+        "!git clone https://github.com/tugstugi/pytorch-speech-commands.git"
+      ],
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'pytorch-speech-commands'...\n",
+            "remote: Enumerating objects: 296, done.\u001b[K\n",
+            "remote: Total 296 (delta 0), reused 0 (delta 0), pack-reused 296\u001b[K\n",
+            "Receiving objects: 100% (296/296), 56.40 KiB | 5.13 MiB/s, done.\n",
+            "Resolving deltas: 100% (174/174), done.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ehGy6HpWzQYC",
+        "outputId": "448497ce-48f2-4b2d-c142-4b3595eea4b1"
+      },
+      "source": [
+        "%cd pytorch-speech-commands/models/"
+      ],
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/pytorch-speech-commands/models\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8H1-D_9tzQYD"
+      },
+      "source": [
+        "from vgg import *\n",
+        "cfg = {\n",
+        "    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n",
+        "    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n",
+        "    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],\n",
+        "    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],\n",
+        "}\n",
+        "def make_layers(cfg, batch_norm=False, in_channels = 3):\n",
+        "    layers = []\n",
+        "    for v in cfg:\n",
+        "        if v == 'M':\n",
+        "            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]\n",
+        "        else:\n",
+        "            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)\n",
+        "            if batch_norm:\n",
+        "                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]\n",
+        "            else:\n",
+        "                layers += [conv2d, nn.ReLU(inplace=True)]\n",
+        "            in_channels = v\n",
+        "    return nn.Sequential(*layers)\n",
+        "\n",
+        "def vgg19(pretrained=False, in_channels=3, **kwargs):\n",
+        "  \"\"\"VGG 19-layer model (configuration \"E\")\n",
+        "  Args:\n",
+        "      pretrained (bool): If True, returns a model pre-trained on ImageNet\n",
+        "  \"\"\"\n",
+        "  if pretrained:\n",
+        "      kwargs['init_weights'] = False\n",
+        "  model = VGG(make_layers(cfg['E'], in_channels=in_channels), **kwargs)\n",
+        "  if pretrained:\n",
+        "      model.load_state_dict(model_zoo.load_url(model_urls['vgg19']))\n",
+        "  return model"
+      ],
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_LL8PIwtzQYD"
+      },
+      "source": [
+        "net_vgg_19 = vgg19(in_channels=1, num_classes=5)\n",
+        "net_vgg_19.classifier[0] = nn.Linear(in_features=10240, out_features=4096, bias=True)\n",
+        "net_vgg_19 = net_vgg_19.to(device)"
+      ],
+      "execution_count": 37,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vjGO2OMSQgSq",
+        "outputId": "a61b4181-8a49-44b9-c3a4-1fffc8601f3a"
+      },
+      "source": [
+        "net_vgg_19"
+      ],
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "VGG(\n",
+              "  (features): Sequential(\n",
+              "    (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (1): ReLU(inplace=True)\n",
+              "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (3): ReLU(inplace=True)\n",
+              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+              "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (6): ReLU(inplace=True)\n",
+              "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (8): ReLU(inplace=True)\n",
+              "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+              "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (11): ReLU(inplace=True)\n",
+              "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (13): ReLU(inplace=True)\n",
+              "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (15): ReLU(inplace=True)\n",
+              "    (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (17): ReLU(inplace=True)\n",
+              "    (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+              "    (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (20): ReLU(inplace=True)\n",
+              "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (22): ReLU(inplace=True)\n",
+              "    (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (24): ReLU(inplace=True)\n",
+              "    (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (26): ReLU(inplace=True)\n",
+              "    (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+              "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (29): ReLU(inplace=True)\n",
+              "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (31): ReLU(inplace=True)\n",
+              "    (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (33): ReLU(inplace=True)\n",
+              "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (35): ReLU(inplace=True)\n",
+              "    (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+              "  )\n",
+              "  (classifier): Sequential(\n",
+              "    (0): Linear(in_features=10240, out_features=4096, bias=True)\n",
+              "    (1): ReLU(inplace=True)\n",
+              "    (2): Dropout(p=0.5, inplace=False)\n",
+              "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
+              "    (4): ReLU(inplace=True)\n",
+              "    (5): Dropout(p=0.5, inplace=False)\n",
+              "    (6): Linear(in_features=4096, out_features=5, bias=True)\n",
+              "  )\n",
+              ")"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 886,
+          "referenced_widgets": [
+            "9bd5d2084b26475e96e160b1d07a8bca",
+            "a34496c95aa1450681d983f0b1c72206",
+            "deec64e7d270491da551afe1dc287819",
+            "98d91281457a4b9eb696f0906dde67d0",
+            "caa39fa0c04744cd8d2c92ebeff6d3fb",
+            "00db916c2df94710abc752b42128d795",
+            "27a2a18d288844329da0c5770ece61f9",
+            "87a4b12f16764c13b43346fb6f3618bb",
+            "4463b32de551424aa69f8920f82f472b",
+            "078120e2598d465babedabfdf1f580db",
+            "49c8a3bb0cfc460ca68156e98936fe33",
+            "1fb99f0c435640e3af516d8f591118a8",
+            "d48158a7cc23414ca234bb108ece167a",
+            "6c758dfc1cf746bc996fe2cda345b617",
+            "6d06cbdbc2bb4e8eb5a1a25a8191a187",
+            "790494d80e22464cba2b08e1befc7f64",
+            "3fdc9650421a498d91b8b72087ed1f40",
+            "45e30995475c4e83a2d8d9001967fa42",
+            "67060133f68f43aa9114e357485bbed7",
+            "137f1af19d254f23a8ce76a1d7e0f11c",
+            "2c0ae6b6a58e4dafb43885cfe2bbb12d",
+            "81f8c9eb30224fdb972b6e6370d0d90e",
+            "9526e81764944b3ea837c2b16a1b91b5",
+            "50452cc0482d4ef6945b0b64ee9d5bd3"
+          ]
+        },
+        "id": "YWwL6W1jzQYD",
+        "outputId": "c1762729-b418-423e-a8ed-6fefc3f89592"
+      },
+      "source": [
+        "losses = []\n",
+        "scores = []\n",
+        "out = Output()\n",
+        "display(out)\n",
+        "\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "#optimizer = optim.Adam(net_vgg_19.parameters(), lr=0.001)\n",
+        "optimizer = optim.SGD(net_vgg_19.parameters(), lr=0.01, momentum=0.9)\n",
+        "\n",
+        "#scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)\n",
+        "\n",
+        "for epoch in range(10):  # loop over the dataset multiple times\n",
+        "\n",
+        "    running_loss = 0.0\n",
+        "    for i, data in tqdm(enumerate(trainloader, 0), total=len(trainloader)):\n",
+        "        # get the inputs; data is a list of [inputs, labels]\n",
+        "        inputs, labels = data\n",
+        "        inputs = inputs.to(device)\n",
+        "        labels = labels.to(device)\n",
+        "\n",
+        "        # zero the parameter gradients\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        "        # forward + backward + optimize\n",
+        "        outputs = net_vgg_19(inputs)\n",
+        "        outputs = torch.nn.functional.softmax(outputs)\n",
+        "        loss = criterion(outputs, labels)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        # print statistics\n",
+        "        running_loss += loss.item()\n",
+        "        if i % 100 == 99:    # print every 100 mini-batches\n",
+        "            acc = get_accuracy(testloader, net_vgg_19)\n",
+        "            print('[%d, %5d] loss: %.3f, accuracy: %.3f' %\n",
+        "                  (epoch + 1, i + 1, running_loss / 100, acc))\n",
+        "            losses.append(running_loss / 100)\n",
+        "            scores.append(acc)\n",
+        "            plot_progress(losses, scores, out)\n",
+        "            running_loss = 0.0\n",
+        "    #scheduler.step()\n",
+        "\n",
+        "print('Finished Training')"
+      ],
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9bd5d2084b26475e96e160b1d07a8bca",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "deec64e7d270491da551afe1dc287819",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:26: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1,   100] loss: 1.703, accuracy: 20.210\n",
+            "[1,   200] loss: 1.704, accuracy: 20.210\n",
+            "[1,   300] loss: 1.712, accuracy: 20.210\n",
+            "[1,   400] loss: 1.704, accuracy: 20.210\n",
+            "[1,   500] loss: 1.690, accuracy: 20.210\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6c758dfc1cf746bc996fe2cda345b617",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2,   100] loss: 1.714, accuracy: 20.210\n",
+            "[2,   200] loss: 1.695, accuracy: 20.210\n",
+            "[2,   300] loss: 1.699, accuracy: 20.210\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-41-26df47acbac0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     37\u001b[0m             \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrunning_loss\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m             \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0macc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0mplot_progress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m             \u001b[0mrunning_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;31m#scheduler.step()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-12-cc143ef056da>\u001b[0m in \u001b[0;36mplot_progress\u001b[0;34m(losses, scores, disp)\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0mclear_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m   \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1d8DTQYy6Q0j"
+      },
+      "source": [
+        "Результат ещё хуже"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QD0nIfO8UFIW"
+      },
+      "source": [
+        "#### KWTNet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ifTn8GrJULS9"
+      },
+      "source": [
+        "Источник:  \n",
+        "https://arxiv.org/abs/2104.00769  \n",
+        "https://github.com/ID56/Torch-KWT"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LcDJ0o5rwbJB",
+        "outputId": "9c664a9d-a9bc-4224-8315-733a7c45c750"
+      },
+      "source": [
+        "!pip install einops"
+      ],
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting einops\n",
+            "  Downloading einops-0.3.2-py3-none-any.whl (25 kB)\n",
+            "Installing collected packages: einops\n",
+            "Successfully installed einops-0.3.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zKC_l3DMwbG0"
+      },
+      "source": [
+        "import torch\n",
+        "import torch.fft\n",
+        "import torch.nn.functional as F\n",
+        "from torch import nn, einsum\n",
+        "\n",
+        "from einops import rearrange, repeat\n",
+        "from einops.layers.torch import Rearrange\n",
+        "\n",
+        "\n",
+        "# Basically vision transformer, ViT that accepts MFCC + SpecAug. Refer to:\n",
+        "# https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit.py\n",
+        "\n",
+        "\n",
+        "class PreNorm(nn.Module):\n",
+        "    def __init__(self, dim, fn):\n",
+        "        super().__init__()\n",
+        "        self.norm = nn.LayerNorm(dim)\n",
+        "        self.fn = fn\n",
+        "\n",
+        "    def forward(self, x, **kwargs):\n",
+        "        return self.fn(self.norm(x), **kwargs)\n",
+        "\n",
+        "\n",
+        "class PostNorm(nn.Module):\n",
+        "    def __init__(self, dim, fn):\n",
+        "        super().__init__()\n",
+        "        self.norm = nn.LayerNorm(dim)\n",
+        "        self.fn = fn\n",
+        "\n",
+        "    def forward(self, x, **kwargs):\n",
+        "        return self.norm(self.fn(x, **kwargs))\n",
+        "\n",
+        "\n",
+        "class FeedForward(nn.Module):\n",
+        "    def __init__(self, dim, hidden_dim, dropout = 0.):\n",
+        "        super().__init__()\n",
+        "        self.net = nn.Sequential(\n",
+        "            nn.Linear(dim, hidden_dim),\n",
+        "            nn.GELU(),\n",
+        "            nn.Dropout(dropout),\n",
+        "            nn.Linear(hidden_dim, dim),\n",
+        "            nn.Dropout(dropout)\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.net(x)\n",
+        "\n",
+        "\n",
+        "class Attention(nn.Module):\n",
+        "    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):\n",
+        "        super().__init__()\n",
+        "        inner_dim = dim_head *  heads\n",
+        "        project_out = not (heads == 1 and dim_head == dim)\n",
+        "\n",
+        "        self.heads = heads\n",
+        "        self.scale = dim_head ** -0.5\n",
+        "\n",
+        "        self.attend = nn.Softmax(dim = -1)\n",
+        "        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)\n",
+        "\n",
+        "        self.to_out = nn.Sequential(\n",
+        "            nn.Linear(inner_dim, dim),\n",
+        "            nn.Dropout(dropout)\n",
+        "        ) if project_out else nn.Identity()\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        b, n, _, h = *x.shape, self.heads\n",
+        "        qkv = self.to_qkv(x).chunk(3, dim = -1)\n",
+        "        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)\n",
+        "\n",
+        "        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale\n",
+        "\n",
+        "        attn = self.attend(dots)\n",
+        "\n",
+        "        out = einsum('b h i j, b h j d -> b h i d', attn, v)\n",
+        "        out = rearrange(out, 'b h n d -> b n (h d)')\n",
+        "        return self.to_out(out)\n",
+        "\n",
+        "\n",
+        "class Transformer(nn.Module):\n",
+        "    def __init__(self, dim, depth, heads, dim_head, mlp_dim, pre_norm=True, dropout = 0.):\n",
+        "        super().__init__()\n",
+        "        self.layers = nn.ModuleList([])\n",
+        "        \n",
+        "        P_Norm = PreNorm if pre_norm else PostNorm\n",
+        "\n",
+        "        for _ in range(depth):\n",
+        "            self.layers.append(nn.ModuleList([\n",
+        "                P_Norm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),\n",
+        "                P_Norm(dim, FeedForward(dim, mlp_dim, dropout = dropout))\n",
+        "            ]))\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        for attn, ff in self.layers:\n",
+        "            x = attn(x) + x\n",
+        "            x = ff(x) + x\n",
+        "        return x\n",
+        "\n",
+        "\n",
+        "class KWT(nn.Module):\n",
+        "    def __init__(self, input_res, patch_res, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 1, dim_head = 64, dropout = 0., emb_dropout = 0., pre_norm = True, **kwargs):\n",
+        "        super().__init__()\n",
+        "        \n",
+        "        num_patches = int(input_res[0]/patch_res[0] * input_res[1]/patch_res[1])\n",
+        "        \n",
+        "        patch_dim = channels * patch_res[0] * patch_res[1]\n",
+        "        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'\n",
+        "\n",
+        "        self.to_patch_embedding = nn.Sequential(\n",
+        "            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_res[0], p2 = patch_res[1]),\n",
+        "            nn.Linear(patch_dim, dim),\n",
+        "        )\n",
+        "\n",
+        "        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))\n",
+        "        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))\n",
+        "        self.dropout = nn.Dropout(emb_dropout)\n",
+        "\n",
+        "        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, pre_norm, dropout)\n",
+        "\n",
+        "        self.pool = pool\n",
+        "        self.to_latent = nn.Identity()\n",
+        "\n",
+        "        self.mlp_head = nn.Sequential(\n",
+        "            nn.LayerNorm(dim),\n",
+        "            nn.Linear(dim, num_classes)\n",
+        "        ) \n",
+        "        \n",
+        "    def forward(self, x):\n",
+        "        x = self.to_patch_embedding(x)\n",
+        "        \n",
+        "        b, n, _ = x.shape\n",
+        "\n",
+        "        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)\n",
+        "        x = torch.cat((cls_tokens, x), dim=1)\n",
+        "        x += self.pos_embedding[:, :(n + 1)]\n",
+        "        x = self.dropout(x)\n",
+        "\n",
+        "        x = self.transformer(x)\n",
+        "\n",
+        "        x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]\n",
+        "\n",
+        "        x = self.to_latent(x)\n",
+        "        return self.mlp_head(x)\n",
+        "\n",
+        "\n",
+        "def kwt_from_name(model_name: str):\n",
+        "\n",
+        "    models = {\n",
+        "        \"kwt-1\": {\n",
+        "            \"input_res\": [40, 98],\n",
+        "            \"patch_res\": [40, 1],\n",
+        "            \"num_classes\": 35,\n",
+        "            \"mlp_dim\": 256,\n",
+        "            \"dim\": 64,\n",
+        "            \"heads\": 1,\n",
+        "            \"depth\": 12,\n",
+        "            \"dropout\": 0.0,\n",
+        "            \"emb_dropout\": 0.1,\n",
+        "            \"pre_norm\": False\n",
+        "        },\n",
+        "\n",
+        "        \"kwt-2\": {\n",
+        "            \"input_res\": [40, 98],\n",
+        "            \"patch_res\": [40, 1],\n",
+        "            \"num_classes\": 35,\n",
+        "            \"mlp_dim\": 512,\n",
+        "            \"dim\": 128,\n",
+        "            \"heads\": 2,\n",
+        "            \"depth\": 12,\n",
+        "            \"dropout\": 0.0,\n",
+        "            \"emb_dropout\": 0.1,\n",
+        "            \"pre_norm\": False\n",
+        "        },\n",
+        "\n",
+        "        \"kwt-3\": {\n",
+        "            \"input_res\": [40, 98],\n",
+        "            \"patch_res\": [40, 1],\n",
+        "            \"num_classes\": 35,\n",
+        "            \"mlp_dim\": 768,\n",
+        "            \"dim\": 192,\n",
+        "            \"heads\": 3,\n",
+        "            \"depth\": 12,\n",
+        "            \"dropout\": 0.0,\n",
+        "            \"emb_dropout\": 0.1,\n",
+        "            \"pre_norm\": False\n",
+        "        },\n",
+        "\n",
+        "        \"kwt-4\": {\n",
+        "            \"input_res\": [40, 512],\n",
+        "            \"patch_res\": [40, 1],\n",
+        "            \"num_classes\": 5,\n",
+        "            \"mlp_dim\": 256,\n",
+        "            \"dim\": 64,\n",
+        "            \"heads\": 1,\n",
+        "            \"depth\": 12,\n",
+        "            \"dropout\": 0.0,\n",
+        "            \"emb_dropout\": 0.1,\n",
+        "            \"pre_norm\": False\n",
+        "        },\n",
+        "    }\n",
+        "\n",
+        "    assert model_name in models.keys(), f\"Unsupported model_name {model_name}; must be one of {list(models.keys())}\"\n",
+        "\n",
+        "    return KWT(**models[model_name])\n",
+        "\n",
+        "kwtnet = kwt_from_name('kwt-4').to(device)"
+      ],
+      "execution_count": 63,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yB6E5wxV9QU-",
+        "outputId": "b966b125-9dcf-4e37-fdfc-a187bbf51335"
+      },
+      "source": [
+        "kwtnet"
+      ],
+      "execution_count": 64,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "KWT(\n",
+              "  (to_patch_embedding): Sequential(\n",
+              "    (0): Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=40, p2=1)\n",
+              "    (1): Linear(in_features=40, out_features=64, bias=True)\n",
+              "  )\n",
+              "  (dropout): Dropout(p=0.1, inplace=False)\n",
+              "  (transformer): Transformer(\n",
+              "    (layers): ModuleList(\n",
+              "      (0): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (1): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (2): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (3): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (4): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (5): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (6): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (7): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (8): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (9): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (10): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (11): ModuleList(\n",
+              "        (0): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): Attention(\n",
+              "            (attend): Softmax(dim=-1)\n",
+              "            (to_qkv): Linear(in_features=64, out_features=192, bias=False)\n",
+              "            (to_out): Identity()\n",
+              "          )\n",
+              "        )\n",
+              "        (1): PostNorm(\n",
+              "          (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "          (fn): FeedForward(\n",
+              "            (net): Sequential(\n",
+              "              (0): Linear(in_features=64, out_features=256, bias=True)\n",
+              "              (1): GELU()\n",
+              "              (2): Dropout(p=0.0, inplace=False)\n",
+              "              (3): Linear(in_features=256, out_features=64, bias=True)\n",
+              "              (4): Dropout(p=0.0, inplace=False)\n",
+              "            )\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "    )\n",
+              "  )\n",
+              "  (to_latent): Identity()\n",
+              "  (mlp_head): Sequential(\n",
+              "    (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
+              "    (1): Linear(in_features=64, out_features=5, bias=True)\n",
+              "  )\n",
+              ")"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 64
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
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+            "4a7eba737dba4b79947bea37c147619b",
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+            "713b1e36d96e4780b7edddf2ab036534"
+          ]
+        },
+        "id": "4X3z2xxlwbEb",
+        "outputId": "2e6c5e08-383c-4093-befa-b03bc59134c8"
+      },
+      "source": [
+        "losses = []\n",
+        "scores = []\n",
+        "out = Output()\n",
+        "display(out)\n",
+        "\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer = optim.SGD(kwtnet.parameters(), lr=0.001, momentum=0.9)\n",
+        "\n",
+        "for epoch in range(10):  # loop over the dataset multiple times\n",
+        "\n",
+        "    running_loss = 0.0\n",
+        "    for i, data in tqdm(enumerate(trainloader, 0), total=len(trainloader)):\n",
+        "        # get the inputs; data is a list of [inputs, labels]\n",
+        "        inputs, labels = data\n",
+        "        inputs = inputs.to(device)\n",
+        "        labels = labels.to(device)\n",
+        "\n",
+        "        # zero the parameter gradients\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        "        # forward + backward + optimize\n",
+        "        outputs = kwtnet(inputs)\n",
+        "        outputs = torch.nn.functional.softmax(outputs)\n",
+        "        loss = criterion(outputs, labels)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        # print statistics\n",
+        "        running_loss += loss.item()\n",
+        "        if i % 100 == 99:    # print every 100 mini-batches\n",
+        "            acc = get_accuracy(testloader, kwtnet)\n",
+        "            print('[%d, %5d] loss: %.3f, accuracy: %.3f' %\n",
+        "                  (epoch + 1, i + 1, running_loss / 100, acc))\n",
+        "            losses.append(running_loss / 100)\n",
+        "            scores.append(acc)\n",
+        "            plot_progress(losses, scores, out)\n",
+        "            running_loss = 0.0\n",
+        "\n",
+        "print('Finished Training')"
+      ],
+      "execution_count": 65,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "03981ca9687d4eecb179268e09375ee6",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "894158a4cd35425d9a0366254f5872d9",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1,   100] loss: 1.607, accuracy: 22.541\n",
+            "[1,   200] loss: 1.607, accuracy: 22.609\n",
+            "[1,   300] loss: 1.606, accuracy: 25.921\n",
+            "[1,   400] loss: 1.603, accuracy: 25.448\n",
+            "[1,   500] loss: 1.602, accuracy: 26.394\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b78181a447d04e01b34b362a223771b6",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2,   100] loss: 1.597, accuracy: 25.887\n",
+            "[2,   200] loss: 1.592, accuracy: 25.752\n",
+            "[2,   300] loss: 1.593, accuracy: 25.583\n",
+            "[2,   400] loss: 1.592, accuracy: 26.428\n",
+            "[2,   500] loss: 1.589, accuracy: 26.394\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e19a7aab228b4261919e5c55a5d6bc87",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[3,   100] loss: 1.579, accuracy: 28.760\n",
+            "[3,   200] loss: 1.575, accuracy: 29.976\n",
+            "[3,   300] loss: 1.566, accuracy: 29.605\n",
+            "[3,   400] loss: 1.567, accuracy: 29.064\n",
+            "[3,   500] loss: 1.571, accuracy: 29.740\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3355ab2f71654ef9be1597ed12d70b97",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[4,   100] loss: 1.552, accuracy: 29.131\n",
+            "[4,   200] loss: 1.556, accuracy: 31.125\n",
+            "[4,   300] loss: 1.557, accuracy: 29.233\n",
+            "[4,   400] loss: 1.571, accuracy: 29.638\n",
+            "[4,   500] loss: 1.556, accuracy: 30.720\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e0b725a8658a401693a4d9c6ffd1cad5",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[5,   100] loss: 1.560, accuracy: 32.477\n",
+            "[5,   200] loss: 1.550, accuracy: 31.970\n",
+            "[5,   300] loss: 1.541, accuracy: 32.612\n",
+            "[5,   400] loss: 1.547, accuracy: 31.734\n",
+            "[5,   500] loss: 1.563, accuracy: 28.489\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1d675e6c258344d8811558fcc1b42bfa",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[6,   100] loss: 1.554, accuracy: 26.428\n",
+            "[6,   200] loss: 1.560, accuracy: 32.105\n",
+            "[6,   300] loss: 1.555, accuracy: 32.376\n",
+            "[6,   400] loss: 1.538, accuracy: 32.342\n",
+            "[6,   500] loss: 1.557, accuracy: 32.410\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "873e87852c064a29bb121c160980d331",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[7,   100] loss: 1.532, accuracy: 33.491\n",
+            "[7,   200] loss: 1.522, accuracy: 34.877\n",
+            "[7,   300] loss: 1.537, accuracy: 34.572\n",
+            "[7,   400] loss: 1.544, accuracy: 32.984\n",
+            "[7,   500] loss: 1.536, accuracy: 34.066\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "66b18e5ac48c4bacaaeee429a75dee80",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[8,   100] loss: 1.525, accuracy: 34.944\n",
+            "[8,   200] loss: 1.516, accuracy: 35.519\n",
+            "[8,   300] loss: 1.530, accuracy: 33.660\n",
+            "[8,   400] loss: 1.537, accuracy: 30.382\n",
+            "[8,   500] loss: 1.525, accuracy: 35.755\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f22498c4afdb4bea8e392949c10cb74f",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[9,   100] loss: 1.527, accuracy: 35.316\n",
+            "[9,   200] loss: 1.532, accuracy: 33.356\n",
+            "[9,   300] loss: 1.524, accuracy: 32.748\n",
+            "[9,   400] loss: 1.535, accuracy: 34.099\n",
+            "[9,   500] loss: 1.544, accuracy: 34.572\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ee870a8bb84847c0b5f0ae8f7fbc6d46",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[10,   100] loss: 1.523, accuracy: 35.823\n",
+            "[10,   200] loss: 1.528, accuracy: 36.195\n",
+            "[10,   300] loss: 1.517, accuracy: 35.586\n",
+            "[10,   400] loss: 1.517, accuracy: 34.978\n",
+            "[10,   500] loss: 1.534, accuracy: 35.046\n",
+            "Finished Training\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jqK4pAwkEw7s"
+      },
+      "source": [
+        "#### BCResNet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CXWySDvVFt-H"
+      },
+      "source": [
+        "Источник:  \n",
+        "https://arxiv.org/abs/2106.04140  \n",
+        "https://github.com/roman-vygon/BCResNet"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KhurIXTipStm"
+      },
+      "source": [
+        "import torch\n",
+        "from torch import Tensor\n",
+        "import torch.nn as nn\n",
+        "\n",
+        "\n",
+        "class SubSpectralNorm(nn.Module):\n",
+        "    def __init__(self, C, S, eps=1e-5):\n",
+        "        super(SubSpectralNorm, self).__init__()\n",
+        "        self.S = S\n",
+        "        self.eps = eps\n",
+        "        self.bn = nn.BatchNorm2d(C*S)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # x: input features with shape {N, C, F, T}\n",
+        "        # S: number of sub-bands\n",
+        "        N, C, F, T = x.size()\n",
+        "        x = x.view(N, C * self.S, F // self.S, T)\n",
+        "\n",
+        "        x = self.bn(x)\n",
+        "\n",
+        "        return x.view(N, C, F, T)\n",
+        "\n",
+        "\n",
+        "class BroadcastedBlock(nn.Module):\n",
+        "    def __init__(\n",
+        "            self,\n",
+        "            planes: int,\n",
+        "            dilation=1,\n",
+        "            stride=1,\n",
+        "            temp_pad=(0, 1),\n",
+        "    ) -> None:\n",
+        "        super(BroadcastedBlock, self).__init__()\n",
+        "\n",
+        "        self.freq_dw_conv = nn.Conv2d(planes, planes, kernel_size=(3, 1), padding=(1, 0), groups=planes,\n",
+        "                                      dilation=dilation,\n",
+        "                                      stride=stride, bias=False)\n",
+        "        self.ssn1 = SubSpectralNorm(planes, 5)\n",
+        "        self.temp_dw_conv = nn.Conv2d(planes, planes, kernel_size=(1, 3), padding=temp_pad, groups=planes,\n",
+        "                                      dilation=dilation, stride=stride, bias=False)\n",
+        "        self.bn = nn.BatchNorm2d(planes)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.channel_drop = nn.Dropout2d(p=0.5)\n",
+        "        self.swish = nn.SiLU()\n",
+        "        self.conv1x1 = nn.Conv2d(planes, planes, kernel_size=(1, 1), bias=False)\n",
+        "\n",
+        "    def forward(self, x: Tensor) -> Tensor:\n",
+        "        identity = x\n",
+        "\n",
+        "        # f2\n",
+        "        ##########################\n",
+        "        out = self.freq_dw_conv(x)\n",
+        "        out = self.ssn1(out)\n",
+        "        ##########################\n",
+        "\n",
+        "        auxilary = out\n",
+        "        out = out.mean(2, keepdim=True)  # frequency average pooling\n",
+        "\n",
+        "        # f1\n",
+        "        ############################\n",
+        "        out = self.temp_dw_conv(out)\n",
+        "        out = self.bn(out)\n",
+        "        out = self.swish(out)\n",
+        "        out = self.conv1x1(out)\n",
+        "        out = self.channel_drop(out)\n",
+        "        ############################\n",
+        "\n",
+        "        out = out + identity + auxilary\n",
+        "        out = self.relu(out)\n",
+        "\n",
+        "        return out\n",
+        "\n",
+        "\n",
+        "class TransitionBlock(nn.Module):\n",
+        "\n",
+        "    def __init__(\n",
+        "            self,\n",
+        "            inplanes: int,\n",
+        "            planes: int,\n",
+        "            dilation=1,\n",
+        "            stride=1,\n",
+        "            temp_pad=(0, 1),\n",
+        "    ) -> None:\n",
+        "        super(TransitionBlock, self).__init__()\n",
+        "\n",
+        "        self.freq_dw_conv = nn.Conv2d(planes, planes, kernel_size=(3, 1), padding=(1, 0), groups=planes,\n",
+        "                                      stride=stride,\n",
+        "                                      dilation=dilation, bias=False)\n",
+        "        self.ssn = SubSpectralNorm(planes, 5)\n",
+        "        self.temp_dw_conv = nn.Conv2d(planes, planes, kernel_size=(1, 3), padding=temp_pad, groups=planes,\n",
+        "                                      dilation=dilation, stride=stride, bias=False)\n",
+        "        self.bn1 = nn.BatchNorm2d(planes)\n",
+        "        self.bn2 = nn.BatchNorm2d(planes)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.channel_drop = nn.Dropout2d(p=0.5)\n",
+        "        self.swish = nn.SiLU()\n",
+        "        self.conv1x1_1 = nn.Conv2d(inplanes, planes, kernel_size=(1, 1), bias=False)\n",
+        "        self.conv1x1_2 = nn.Conv2d(planes, planes, kernel_size=(1, 1), bias=False)\n",
+        "\n",
+        "    def forward(self, x: Tensor) -> Tensor:\n",
+        "        # f2\n",
+        "        #############################\n",
+        "        out = self.conv1x1_1(x)\n",
+        "        out = self.bn1(out)\n",
+        "        out = self.relu(out)\n",
+        "        out = self.freq_dw_conv(out)\n",
+        "        out = self.ssn(out)\n",
+        "        #############################\n",
+        "        auxilary = out\n",
+        "        out = out.mean(2, keepdim=True)  # frequency average pooling\n",
+        "\n",
+        "        # f1\n",
+        "        #############################\n",
+        "        out = self.temp_dw_conv(out)\n",
+        "        out = self.bn2(out)\n",
+        "        out = self.swish(out)\n",
+        "        out = self.conv1x1_2(out)\n",
+        "        out = self.channel_drop(out)\n",
+        "        #############################\n",
+        "\n",
+        "        out = auxilary + out\n",
+        "        out = self.relu(out)\n",
+        "\n",
+        "        return out\n",
+        "\n",
+        "\n",
+        "class BCResNet(torch.nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(BCResNet, self).__init__()\n",
+        "        self.conv1 = nn.Conv2d(1, 16, 5, stride=(2, 1), padding=(2, 2))\n",
+        "        self.block1_1 = TransitionBlock(16, 8)\n",
+        "        self.block1_2 = BroadcastedBlock(8)\n",
+        "\n",
+        "        self.block2_1 = TransitionBlock(8, 12, stride=(2, 1), dilation=(1, 2), temp_pad=(0, 2))\n",
+        "        self.block2_2 = BroadcastedBlock(12, dilation=(1, 2), temp_pad=(0, 2))\n",
+        "\n",
+        "        self.block3_1 = TransitionBlock(12, 16, stride=(2, 1), dilation=(1, 4), temp_pad=(0, 4))\n",
+        "        self.block3_2 = BroadcastedBlock(16, dilation=(1, 4), temp_pad=(0, 4))\n",
+        "        self.block3_3 = BroadcastedBlock(16, dilation=(1, 4), temp_pad=(0, 4))\n",
+        "        self.block3_4 = BroadcastedBlock(16, dilation=(1, 4), temp_pad=(0, 4))\n",
+        "\n",
+        "        self.block4_1 = TransitionBlock(16, 20, dilation=(1, 8), temp_pad=(0, 8))\n",
+        "        self.block4_2 = BroadcastedBlock(20, dilation=(1, 8), temp_pad=(0, 8))\n",
+        "        self.block4_3 = BroadcastedBlock(20, dilation=(1, 8), temp_pad=(0, 8))\n",
+        "        self.block4_4 = BroadcastedBlock(20, dilation=(1, 8), temp_pad=(0, 8))\n",
+        "\n",
+        "        self.conv2 = nn.Conv2d(20, 20, 5, groups=20, padding=(0, 2))\n",
+        "        self.conv3 = nn.Conv2d(20, 32, 1, bias=False)\n",
+        "        self.conv4 = nn.Conv2d(32, 12, 1, bias=False)\n",
+        "\n",
+        "        self.adapt = nn.AdaptiveMaxPool2d((5,7))\n",
+        "        self.flatten = nn.Flatten()\n",
+        "        self.dense1 = nn.Linear(420, 120)\n",
+        "        self.dense2 = nn.Linear(120, 5)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "\n",
+        "        #print('INPUT SHAPE:', x.shape)\n",
+        "        out = self.conv1(x)\n",
+        "\n",
+        "        #print('BLOCK1 INPUT SHAPE:', out.shape)\n",
+        "        out = self.block1_1(out)\n",
+        "        out = self.block1_2(out)\n",
+        "\n",
+        "        #print('BLOCK2 INPUT SHAPE:', out.shape)\n",
+        "        out = self.block2_1(out)\n",
+        "        out = self.block2_2(out)\n",
+        "\n",
+        "        #print('BLOCK3 INPUT SHAPE:', out.shape)\n",
+        "        out = self.block3_1(out)\n",
+        "        out = self.block3_2(out)\n",
+        "        out = self.block3_3(out)\n",
+        "        out = self.block3_4(out)\n",
+        "\n",
+        "        #print('BLOCK4 INPUT SHAPE:', out.shape)\n",
+        "        out = self.block4_1(out)\n",
+        "        out = self.block4_2(out)\n",
+        "        out = self.block4_3(out)\n",
+        "        out = self.block4_4(out)\n",
+        "\n",
+        "        #print('Conv2 INPUT SHAPE:', out.shape)\n",
+        "        out = self.conv2(out)\n",
+        "\n",
+        "        #print('Conv3 INPUT SHAPE:', out.shape)\n",
+        "        out = self.conv3(out)\n",
+        "        out = out.mean(-1, keepdim=True)\n",
+        "\n",
+        "        #print('Conv4 INPUT SHAPE:', out.shape)\n",
+        "        out = self.conv4(out)\n",
+        "\n",
+        "        out = self.adapt(out)\n",
+        "        out = self.flatten(out)\n",
+        "        #print(\"------------>\", out.shape)\n",
+        "        out = self.dense1(out)\n",
+        "        out = F.relu(out)\n",
+        "        out = self.dense2(out)\n",
+        "        out = F.softmax(out)\n",
+        "\n",
+        "        #print('OUTPUT SHAPE:', out.shape)\n",
+        "        return out\n",
+        "\n",
+        "bcresnet = BCResNet().to(device)"
+      ],
+      "execution_count": 49,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "7ec262c1062b442ea3627f49c2370a92",
+            "5de403e1051b486f850552af9e0caa27",
+            "af6bf79fed44484ba68408797cccb9cb",
+            "30ded0aace03496ca8439e8fd2cec2a7",
+            "fd1f9ae2470542e4942314f27d9843cc",
+            "2d75fc216f964421b1214f01605720b9",
+            "520475b0701349e0887020bafe60fb5c",
+            "6d0908e7771a46a3afb627fad216bb32",
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+          ]
+        },
+        "id": "jotrOxrBpSq9",
+        "outputId": "8518ae23-3f90-4397-d4dc-73f47a662e3f"
+      },
+      "source": [
+        "losses = []\n",
+        "scores = []\n",
+        "out = Output()\n",
+        "display(out)\n",
+        "\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer = optim.SGD(bcresnet.parameters(), lr=0.001, momentum=0.9)\n",
+        "#optimizer = optim.Adam(bcresnet.parameters(), lr=0.001)\n",
+        "\n",
+        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)\n",
+        "\n",
+        "for epoch in range(10):  # loop over the dataset multiple times\n",
+        "    running_loss = 0.0\n",
+        "    for i, data in tqdm(enumerate(trainloader, 0), total=len(trainloader)):\n",
+        "        # get the inputs; data is a list of [inputs, labels]\n",
+        "        inputs, labels = data\n",
+        "        inputs = inputs.to(device)\n",
+        "        labels = labels.to(device)\n",
+        "\n",
+        "        # zero the parameter gradients\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        "        # forward + backward + optimize\n",
+        "        outputs = bcresnet(inputs)\n",
+        "        loss = criterion(outputs, labels)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        # print statistics\n",
+        "        running_loss += loss.item()\n",
+        "        if i % 100 == 99:    # print every 100 mini-batches\n",
+        "            acc = get_accuracy(testloader, bcresnet)\n",
+        "            print('[%d, %5d] loss: %.3f, accuracy: %.3f' %\n",
+        "                  (epoch + 1, i + 1, running_loss / 100, acc))\n",
+        "            losses.append(running_loss / 100)\n",
+        "            scores.append(acc)\n",
+        "            plot_progress(losses, scores, out)\n",
+        "            running_loss = 0.0\n",
+        "    scheduler.step()\n",
+        "\n",
+        "print('Finished Training')"
+      ],
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7ec262c1062b442ea3627f49c2370a92",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "af6bf79fed44484ba68408797cccb9cb",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:196: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1,   100] loss: 1.464, accuracy: 19.703\n",
+            "[1,   200] loss: 1.476, accuracy: 32.105\n",
+            "[1,   300] loss: 1.478, accuracy: 19.669\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f3466c1ba70>\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
+            "    self._shutdown_workers()\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
+            "    if w.is_alive():\n",
+            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
+            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
+            "AssertionError: can only test a child process\n",
+            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f3466c1ba70>\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
+            "    self._shutdown_workers()\n",
+            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
+            "    if w.is_alive():\n",
+            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
+            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
+            "AssertionError: can only test a child process\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1,   400] loss: 1.483, accuracy: 25.718\n",
+            "[1,   500] loss: 1.479, accuracy: 20.176\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b01609dac4d04154af39c97d011b35d5",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2,   100] loss: 1.489, accuracy: 32.376\n",
+            "[2,   200] loss: 1.474, accuracy: 19.804\n",
+            "[2,   300] loss: 1.497, accuracy: 25.786\n",
+            "[2,   400] loss: 1.482, accuracy: 21.021\n",
+            "[2,   500] loss: 1.484, accuracy: 20.649\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "dd086b8bd940422dbd76b4de91f81420",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[3,   100] loss: 1.485, accuracy: 28.320\n",
+            "[3,   200] loss: 1.495, accuracy: 20.142\n",
+            "[3,   300] loss: 1.488, accuracy: 29.098\n",
+            "[3,   400] loss: 1.480, accuracy: 23.352\n",
+            "[3,   500] loss: 1.459, accuracy: 37.107\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "726558f5c8c24ddd86146e07c24e493e",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[4,   100] loss: 1.472, accuracy: 21.359\n",
+            "[4,   200] loss: 1.480, accuracy: 20.176\n",
+            "[4,   300] loss: 1.480, accuracy: 25.853\n",
+            "[4,   400] loss: 1.477, accuracy: 21.561\n",
+            "[4,   500] loss: 1.448, accuracy: 31.328\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a294a8807ae04036b2b2af58c459819a",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[5,   100] loss: 1.457, accuracy: 20.885\n",
+            "[5,   200] loss: 1.461, accuracy: 40.858\n",
+            "[5,   300] loss: 1.541, accuracy: 27.509\n",
+            "[5,   400] loss: 1.556, accuracy: 29.436\n",
+            "[5,   500] loss: 1.538, accuracy: 20.311\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8774f03f82ec4c76a988818fcb1d101c",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[6,   100] loss: 1.529, accuracy: 21.730\n",
+            "[6,   200] loss: 1.527, accuracy: 22.034\n",
+            "[6,   300] loss: 1.522, accuracy: 27.002\n",
+            "[6,   400] loss: 1.520, accuracy: 24.704\n",
+            "[6,   500] loss: 1.542, accuracy: 35.113\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e8dc957ca59c48af9cd221ab31b079df",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[7,   100] loss: 1.506, accuracy: 35.248\n",
+            "[7,   200] loss: 1.522, accuracy: 23.048\n",
+            "[7,   300] loss: 1.519, accuracy: 35.553\n",
+            "[7,   400] loss: 1.511, accuracy: 40.554\n",
+            "[7,   500] loss: 1.517, accuracy: 23.285\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3291369ab7614451a0c21348c4445940",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[8,   100] loss: 1.505, accuracy: 33.525\n",
+            "[8,   200] loss: 1.504, accuracy: 28.523\n",
+            "[8,   300] loss: 1.515, accuracy: 30.923\n",
+            "[8,   400] loss: 1.507, accuracy: 26.664\n",
+            "[8,   500] loss: 1.503, accuracy: 24.975\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "617a69b2faf04a7ab86fb9e4c6f12683",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[9,   100] loss: 1.513, accuracy: 30.551\n",
+            "[9,   200] loss: 1.498, accuracy: 27.611\n",
+            "[9,   300] loss: 1.490, accuracy: 22.710\n",
+            "[9,   400] loss: 1.515, accuracy: 34.640\n",
+            "[9,   500] loss: 1.493, accuracy: 25.989\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9b79cb2e2d0f41a4a0578105b8bdce1a",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/555 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[10,   100] loss: 1.492, accuracy: 23.116\n",
+            "[10,   200] loss: 1.511, accuracy: 27.678\n",
+            "[10,   300] loss: 1.495, accuracy: 21.697\n",
+            "[10,   400] loss: 1.498, accuracy: 21.156\n",
+            "[10,   500] loss: 1.479, accuracy: 21.832\n",
+            "Finished Training\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FEUCFBMuWGrR"
+      },
+      "source": [
+        "#### Итоги"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kAHI9NmRWKrH"
+      },
+      "source": [
+        "Лучше всего показал себя BaseLine с семинара"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "c4woC3AepSl8"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oN77pEO8pSjS"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fvf8UCsPDvj2"
+      },
+      "source": [
+        "### Full scale commands recognition (3+ points)\n",
+        "\n",
+        "Your final task is to train a full-scale voice command spotter and apply it to a video:\n",
+        "1. Build the dataset with all 30+ classes (directions, digits, names, etc.)\n",
+        "  * __Optional:__ include a special \"noise\" class that contains random unrelated sounds\n",
+        "  * You can download youtube videos with [`youtube-dl`](https://ytdl-org.github.io/youtube-dl/index.html) library.\n",
+        "2. Train a model on this full dataset. Kudos for tuning its accuracy :)\n",
+        "3. Apply it to a audio/video of your choice to spot the occurences of each keyword\n",
+        " * Here's one [video about primes](https://www.youtube.com/watch?v=EK32jo7i5LQ) that you can try. It should be full of numbers :)\n",
+        " * There are multiple ways you can analyze the performance of your network, e.g. plot probabilities predicted for every time-step. Chances are you'll discover something useful about how to improve your model :)\n",
+        "\n",
+        "\n",
+        "Please briefly describe what you did in a short informal report."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KS2H-RQdWc2i"
+      },
+      "source": [
+        "Натренеруем лучшую модель из предыдущего задания на все 31 классов"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "s0seqdl1hYlI"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zOo5H-r3WthB",
+        "outputId": "6e1576c3-6db6-4987-fe9d-54d112d18bd8"
+      },
+      "source": [
+        "%cd /content/\n",
+        "!rm -r ./speech_commands\n",
+        "\n",
+        "datadir = \"speech_commands\"\n",
+        "\n",
+        "!wget http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz -O speech_commands_v0.01.tar.gz\n",
+        "# alternative url: https://www.dropbox.com/s/j95n278g48bcbta/speech_commands_v0.01.tar.gz?dl=1\n",
+        "!mkdir {datadir} && tar -C {datadir} -xvzf speech_commands_v0.01.tar.gz 1> log\n",
+        "\n",
+        "samples_by_target = {\n",
+        "    cls: [os.path.join(datadir, cls, name) for name in os.listdir(\"./speech_commands/{}\".format(cls)) if 'README.md' not in name]\n",
+        "    for cls in os.listdir(datadir)\n",
+        "    if os.path.isdir(os.path.join(datadir, cls))\n",
+        "}\n",
+        "classes = list(samples_by_target.keys())\n",
+        "print('Classes:', ', '.join(sorted(classes)))"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content\n",
+            "rm: cannot remove './speech_commands': No such file or directory\n",
+            "--2021-10-27 06:06:00--  http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz\n",
+            "Resolving download.tensorflow.org (download.tensorflow.org)... 74.125.142.128, 2607:f8b0:400e:c08::80\n",
+            "Connecting to download.tensorflow.org (download.tensorflow.org)|74.125.142.128|:80... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 1489096277 (1.4G) [application/gzip]\n",
+            "Saving to: ‘speech_commands_v0.01.tar.gz’\n",
+            "\n",
+            "speech_commands_v0. 100%[===================>]   1.39G   181MB/s    in 11s     \n",
+            "\n",
+            "2021-10-27 06:06:11 (124 MB/s) - ‘speech_commands_v0.01.tar.gz’ saved [1489096277/1489096277]\n",
+            "\n",
+            "Classes: _background_noise_, bed, bird, cat, dog, down, eight, five, four, go, happy, house, left, marvin, nine, no, off, on, one, right, seven, sheila, six, stop, three, tree, two, up, wow, yes, zero\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "r7TTZEycXQJK",
+        "outputId": "698c143a-2ffb-4bef-ad80-f754bd029020"
+      },
+      "source": [
+        "print(len(classes))"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "31\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "342104400d6c41f5bbc6162e31511bb7",
+            "0f9a19caa6bc4cddb82783fd94d8d5bb",
+            "dd029a3068ff40f796a66cc09cce14e6",
+            "0aea3fb3ff074c9c9e352e81d2b9e005",
+            "9c503fa4450a4887bc2819988bf97bb2",
+            "a4cbc901238a4549968b0b04ee7f6395",
+            "79081b70c5664e669c55dc0a5b0ef0c9",
+            "35192fec31da443384a40a459bd46d0e",
+            "26debf8d45674a939bc9a2b75645d229",
+            "7a2aca9a64cf4e6680ed52d98d1fdf2b",
+            "8d67decfbde24c089d1030af5bffb24d"
+          ]
+        },
+        "id": "s2GscegbWYPN",
+        "outputId": "c9aa4e7c-4e06-4fca-df69-f9e2f0de3184"
+      },
+      "source": [
+        "def preprocess_sample(filepath, max_length=160):\n",
+        "    amplitudes, sr = librosa.core.load(filepath)\n",
+        "    spectrogram = librosa.feature.melspectrogram(amplitudes, sr=sr)[:, :max_length]\n",
+        "    spectrogram = np.pad(spectrogram, [[0, 0], [0, max(0, max_length - spectrogram.shape[1])]], mode='constant')\n",
+        "    target = classes.index(filepath.split(os.sep)[-2])\n",
+        "    #print(target)\n",
+        "    return np.float32(spectrogram), np.int64(target)\n",
+        "\n",
+        "all_files = chain(*(samples_by_target[cls] for cls in classes))\n",
+        "\n",
+        "spectrograms_and_targets = jl.Parallel(n_jobs=-1)(tqdm(list(map(jl.delayed(preprocess_sample), all_files))))\n",
+        "#spectrograms_and_targets = list(map(preprocess_sample, all_files))\n",
+        "X, y = map(np.stack, zip(*spectrograms_and_targets))\n",
+        "X = X.transpose([0, 2, 1])  # to [batch, time, channels]\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "342104400d6c41f5bbc6162e31511bb7",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/64727 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GMlpd-6VWYLq"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lf1YoT_mbMZ1",
+        "outputId": "4b9638e5-b77b-4574-bda6-ea41e8a2b9e4"
+      },
+      "source": [
+        "print('Train before: {}'.format(X_train.shape))\n",
+        "print('Test before: {}'.format(X_test.shape))\n",
+        "X_train = np.expand_dims(X_train, axis=1)\n",
+        "X_test = np.expand_dims(X_test, axis=1)\n",
+        "print('Train after: {}'.format(X_train.shape))\n",
+        "print('Test after: {}'.format(X_test.shape))"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train before: (48545, 160, 128)\n",
+            "Test before: (16182, 160, 128)\n",
+            "Train after: (48545, 1, 160, 128)\n",
+            "Test after: (16182, 1, 160, 128)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "S4EUVDtVbMZ2"
+      },
+      "source": [
+        "if torch.cuda.is_available:\n",
+        "  device = 'cuda'\n",
+        "else:\n",
+        "  device = 'cpu'\n",
+        "\n",
+        "batch_size = 16\n",
+        "\n",
+        "tensor_x = torch.Tensor(X_train)\n",
+        "tensor_y = torch.LongTensor(y_train)\n",
+        "\n",
+        "train_dataset = TensorDataset(tensor_x, tensor_y)\n",
+        "\n",
+        "tensor_x = torch.Tensor(X_test) # transform to torch tensor\n",
+        "tensor_y = torch.LongTensor(y_test)\n",
+        "\n",
+        "test_dataset = TensorDataset(tensor_x, tensor_y)\n",
+        "\n",
+        "\n",
+        "trainloader = DataLoader(train_dataset, batch_size=batch_size,\n",
+        "                         shuffle=True, num_workers=2)\n",
+        "testloader = DataLoader(test_dataset, batch_size=batch_size,\n",
+        "                        shuffle=False, num_workers=2)"
+      ],
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7lc8H7WhbMZ2",
+        "outputId": "0c41a3f0-5262-4cba-d667-d5389c3b2458"
+      },
+      "source": [
+        "X_train.shape"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(48545, 1, 160, 128)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YG_UdecrbMZ3"
+      },
+      "source": [
+        "class Net(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        # TODO: define your layers here\n",
+        "        self.conv1 = nn.Conv2d(1, 64, 5)\n",
+        "        self.bn1 = nn.BatchNorm2d(64)\n",
+        "        self.dropout1 = nn.Dropout(0.2)\n",
+        "        self.conv2 = nn.Conv2d(64, 128, 5)\n",
+        "        self.bn2 = nn.BatchNorm2d(128)\n",
+        "        self.pool1 = nn.MaxPool2d(2)\n",
+        "        self.conv3 = nn.Conv2d(128, 256, 5)\n",
+        "        self.bn3 = nn.BatchNorm2d(256)\n",
+        "        self.pool2 = nn.MaxPool2d(4)\n",
+        "        self.conv4 = nn.Conv2d(256, 256, 5)\n",
+        "        self.bn4 = nn.BatchNorm2d(256)\n",
+        "        self.pool3 = nn.MaxPool2d(6)\n",
+        "        self.flatten = nn.Flatten()\n",
+        "        self.dense1 = nn.Linear(512, 256)\n",
+        "        self.dense2 = nn.Linear(256, 31)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # TODO: apply your layers here\n",
+        "        x = self.conv1(x)\n",
+        "        x = self.bn1(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.dropout1(x)\n",
+        "        x = self.conv2(x)\n",
+        "        x = self.bn2(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.pool1(x)\n",
+        "        x = self.conv3(x)\n",
+        "        x = self.bn3(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.pool2(x)\n",
+        "        x = self.conv4(x)\n",
+        "        x = self.bn4(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.pool3(x)\n",
+        "        x = self.flatten(x)\n",
+        "        x = self.dense1(x)\n",
+        "        x = F.relu(x)\n",
+        "        x = self.dense2(x)\n",
+        "        x = F.softmax(x)\n",
+        "        return x\n",
+        "\n",
+        "\n",
+        "net = Net().to(device)"
+      ],
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AD5UftqhbMZ3"
+      },
+      "source": [
+        "def get_accuracy(loader, model):\n",
+        "    num_correct = 0\n",
+        "    num_samples = 0\n",
+        "    model.eval()\n",
+        "    \n",
+        "    with torch.no_grad():\n",
+        "        for x, y in loader:\n",
+        "            x = x.to(device)\n",
+        "            y = y.to(device)\n",
+        "            \n",
+        "            scores = model(x)\n",
+        "            _, predictions = scores.max(1)\n",
+        "            num_correct += (predictions == y).sum()\n",
+        "            num_samples += predictions.size(0)\n",
+        "        \n",
+        "        #print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}') \n",
+        "    \n",
+        "    model.train()\n",
+        "    return float(num_correct)/float(num_samples)*100"
+      ],
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-p4bjWo5bMZ4"
+      },
+      "source": [
+        "import seaborn as sns\n",
+        "sns.set()\n",
+        "from IPython.display import clear_output, display\n",
+        "\n",
+        "import time\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tqdm.auto import trange\n",
+        "\n",
+        "from ipywidgets import Output\n",
+        "\n",
+        "def plot_progress(losses, scores, disp):\n",
+        "  with disp:\n",
+        "    fig, ax = plt.subplots(1, 2, figsize=(16, 5))\n",
+        "    ax[0].plot([i*100 for i in range(len(losses))], losses)\n",
+        "    ax[1].plot([i*100 for i in range(len(scores))], scores)\n",
+        "    ax[0].scatter([i*100 for i in range(len(losses))], losses, color='red')\n",
+        "    ax[1].scatter([i*100 for i in range(len(scores))], scores, color='red')\n",
+        "\n",
+        "\n",
+        "    ax[0].set(xlabel='samples', ylabel='loss',\n",
+        "          title=f'Training loss')\n",
+        "    ax[1].set(xlabel='samples', ylabel='accuracy',\n",
+        "          title=f'Test accuracy')\n",
+        "    clear_output(wait=True)\n",
+        "    plt.show()\n",
+        "  time.sleep(1)"
+      ],
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 685
+        },
+        "id": "zizOjZUJbMZ4",
+        "outputId": "c6998c85-a0cc-4850-f8f5-e47913157029"
+      },
+      "source": [
+        "losses = []\n",
+        "scores = []\n",
+        "out = Output()\n",
+        "display(out)\n",
+        "\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
+        "\n",
+        "for epoch in range(15):  # loop over the dataset multiple times\n",
+        "\n",
+        "    running_loss = 0.0\n",
+        "    for i, data in tqdm(enumerate(trainloader, 0), total=len(trainloader)):\n",
+        "        # get the inputs; data is a list of [inputs, labels]\n",
+        "        inputs, labels = data\n",
+        "        inputs = inputs.to(device)\n",
+        "        labels = labels.to(device)\n",
+        "\n",
+        "        # zero the parameter gradients\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        "        # forward + backward + optimize\n",
+        "        outputs = net(inputs)\n",
+        "        loss = criterion(outputs, labels)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        # print statistics\n",
+        "        running_loss += loss.item()\n",
+        "        if i % 100 == 99:    # print every 100 mini-batches\n",
+        "            acc = get_accuracy(testloader, net)\n",
+        "            print('[%d, %5d] loss: %.3f, accuracy: %.3f' %\n",
+        "                  (epoch + 1, i + 1, running_loss / 100, acc))\n",
+        "            losses.append(running_loss / 100)\n",
+        "            scores.append(acc)\n",
+        "            plot_progress(losses, scores, out)\n",
+        "            running_loss = 0.0\n",
+        "\n",
+        "print('Finished Training')"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1152x360 with 2 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-12-c21f98760de1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0mrunning_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m100\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# print every 100 mini-batches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m             \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_accuracy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m             print('[%d, %5d] loss: %.3f, accuracy: %.3f' %\n\u001b[1;32m     32\u001b[0m                   (epoch + 1, i + 1, running_loss / 100, acc))\n",
+            "\u001b[0;32m<ipython-input-10-2a33b9e02de2>\u001b[0m in \u001b[0;36mget_accuracy\u001b[0;34m(loader, model)\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "id": "U83lWlE4WYJf",
+        "outputId": "21de5e62-0563-4b86-82e4-a09a7792f59d"
+      },
+      "source": [
+        "from google.colab import files\n",
+        "path_to_save = \"baseline_model.pth\"\n",
+        "torch.save(net.state_dict(), path_to_save)\n",
+        "files.download(path_to_save )"
+      ],
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "download(\"download_a03e9112-9c2b-4410-8b1f-72bcd191f0e4\", \"baseline_model.pth\", 11236549)"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TGudfNbAnynd"
+      },
+      "source": [
+        "#### BCResNet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BQAzPR-Inynd"
+      },
+      "source": [
+        "Источник:  \n",
+        "https://arxiv.org/abs/2106.04140  \n",
+        "https://github.com/roman-vygon/BCResNet"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AuZzxdoz-18e"
+      },
+      "source": [
+        "import torch\n",
+        "from torch import Tensor\n",
+        "import torch.nn as nn\n",
+        "\n",
+        "\n",
+        "class SubSpectralNorm(nn.Module):\n",
+        "    def __init__(self, C, S, eps=1e-5):\n",
+        "        super(SubSpectralNorm, self).__init__()\n",
+        "        self.S = S\n",
+        "        self.eps = eps\n",
+        "        self.bn = nn.BatchNorm2d(C*S)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # x: input features with shape {N, C, F, T}\n",
+        "        # S: number of sub-bands\n",
+        "        N, C, F, T = x.size()\n",
+        "        x = x.view(N, C * self.S, F // self.S, T)\n",
+        "\n",
+        "        x = self.bn(x)\n",
+        "\n",
+        "        return x.view(N, C, F, T)\n",
+        "\n",
+        "\n",
+        "class BroadcastedBlock(nn.Module):\n",
+        "    def __init__(\n",
+        "            self,\n",
+        "            planes: int,\n",
+        "            dilation=1,\n",
+        "            stride=1,\n",
+        "            temp_pad=(0, 1),\n",
+        "    ) -> None:\n",
+        "        super(BroadcastedBlock, self).__init__()\n",
+        "\n",
+        "        self.freq_dw_conv = nn.Conv2d(planes, planes, kernel_size=(3, 1), padding=(1, 0), groups=planes,\n",
+        "                                      dilation=dilation,\n",
+        "                                      stride=stride, bias=False)\n",
+        "        self.ssn1 = SubSpectralNorm(planes, 5)\n",
+        "        self.temp_dw_conv = nn.Conv2d(planes, planes, kernel_size=(1, 3), padding=temp_pad, groups=planes,\n",
+        "                                      dilation=dilation, stride=stride, bias=False)\n",
+        "        self.bn = nn.BatchNorm2d(planes)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.channel_drop = nn.Dropout2d(p=0.5)\n",
+        "        self.swish = nn.SiLU()\n",
+        "        self.conv1x1 = nn.Conv2d(planes, planes, kernel_size=(1, 1), bias=False)\n",
+        "\n",
+        "    def forward(self, x: Tensor) -> Tensor:\n",
+        "        identity = x\n",
+        "\n",
+        "        # f2\n",
+        "        ##########################\n",
+        "        out = self.freq_dw_conv(x)\n",
+        "        out = self.ssn1(out)\n",
+        "        ##########################\n",
+        "\n",
+        "        auxilary = out\n",
+        "        out = out.mean(2, keepdim=True)  # frequency average pooling\n",
+        "\n",
+        "        # f1\n",
+        "        ############################\n",
+        "        out = self.temp_dw_conv(out)\n",
+        "        out = self.bn(out)\n",
+        "        out = self.swish(out)\n",
+        "        out = self.conv1x1(out)\n",
+        "        out = self.channel_drop(out)\n",
+        "        ############################\n",
+        "\n",
+        "        out = out + identity + auxilary\n",
+        "        out = self.relu(out)\n",
+        "\n",
+        "        return out\n",
+        "\n",
+        "\n",
+        "class TransitionBlock(nn.Module):\n",
+        "\n",
+        "    def __init__(\n",
+        "            self,\n",
+        "            inplanes: int,\n",
+        "            planes: int,\n",
+        "            dilation=1,\n",
+        "            stride=1,\n",
+        "            temp_pad=(0, 1),\n",
+        "    ) -> None:\n",
+        "        super(TransitionBlock, self).__init__()\n",
+        "\n",
+        "        self.freq_dw_conv = nn.Conv2d(planes, planes, kernel_size=(3, 1), padding=(1, 0), groups=planes,\n",
+        "                                      stride=stride,\n",
+        "                                      dilation=dilation, bias=False)\n",
+        "        self.ssn = SubSpectralNorm(planes, 5)\n",
+        "        self.temp_dw_conv = nn.Conv2d(planes, planes, kernel_size=(1, 3), padding=temp_pad, groups=planes,\n",
+        "                                      dilation=dilation, stride=stride, bias=False)\n",
+        "        self.bn1 = nn.BatchNorm2d(planes)\n",
+        "        self.bn2 = nn.BatchNorm2d(planes)\n",
+        "        self.relu = nn.ReLU(inplace=True)\n",
+        "        self.channel_drop = nn.Dropout2d(p=0.5)\n",
+        "        self.swish = nn.SiLU()\n",
+        "        self.conv1x1_1 = nn.Conv2d(inplanes, planes, kernel_size=(1, 1), bias=False)\n",
+        "        self.conv1x1_2 = nn.Conv2d(planes, planes, kernel_size=(1, 1), bias=False)\n",
+        "\n",
+        "    def forward(self, x: Tensor) -> Tensor:\n",
+        "        # f2\n",
+        "        #############################\n",
+        "        out = self.conv1x1_1(x)\n",
+        "        out = self.bn1(out)\n",
+        "        out = self.relu(out)\n",
+        "        out = self.freq_dw_conv(out)\n",
+        "        out = self.ssn(out)\n",
+        "        #############################\n",
+        "        auxilary = out\n",
+        "        out = out.mean(2, keepdim=True)  # frequency average pooling\n",
+        "\n",
+        "        # f1\n",
+        "        #############################\n",
+        "        out = self.temp_dw_conv(out)\n",
+        "        out = self.bn2(out)\n",
+        "        out = self.swish(out)\n",
+        "        out = self.conv1x1_2(out)\n",
+        "        out = self.channel_drop(out)\n",
+        "        #############################\n",
+        "\n",
+        "        out = auxilary + out\n",
+        "        out = self.relu(out)\n",
+        "\n",
+        "        return out\n",
+        "\n",
+        "\n",
+        "class BCResNet(torch.nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(BCResNet, self).__init__()\n",
+        "        self.conv1 = nn.Conv2d(1, 16, 5, stride=(2, 1), padding=(2, 2))\n",
+        "        self.block1_1 = TransitionBlock(16, 8)\n",
+        "        self.block1_2 = BroadcastedBlock(8)\n",
+        "\n",
+        "        self.block2_1 = TransitionBlock(8, 12, stride=(2, 1), dilation=(1, 2), temp_pad=(0, 2))\n",
+        "        self.block2_2 = BroadcastedBlock(12, dilation=(1, 2), temp_pad=(0, 2))\n",
+        "\n",
+        "        self.block3_1 = TransitionBlock(12, 16, stride=(2, 1), dilation=(1, 4), temp_pad=(0, 4))\n",
+        "        self.block3_2 = BroadcastedBlock(16, dilation=(1, 4), temp_pad=(0, 4))\n",
+        "        self.block3_3 = BroadcastedBlock(16, dilation=(1, 4), temp_pad=(0, 4))\n",
+        "        self.block3_4 = BroadcastedBlock(16, dilation=(1, 4), temp_pad=(0, 4))\n",
+        "\n",
+        "        self.block4_1 = TransitionBlock(16, 20, dilation=(1, 8), temp_pad=(0, 8))\n",
+        "        self.block4_2 = BroadcastedBlock(20, dilation=(1, 8), temp_pad=(0, 8))\n",
+        "        self.block4_3 = BroadcastedBlock(20, dilation=(1, 8), temp_pad=(0, 8))\n",
+        "        self.block4_4 = BroadcastedBlock(20, dilation=(1, 8), temp_pad=(0, 8))\n",
+        "\n",
+        "        self.conv2 = nn.Conv2d(20, 20, 5, groups=20, padding=(0, 2))\n",
+        "        self.conv3 = nn.Conv2d(20, 32, 1, bias=False)\n",
+        "        self.conv4 = nn.Conv2d(32, 12, 1, bias=False)\n",
+        "\n",
+        "        self.adapt = nn.AdaptiveMaxPool2d((5,7))\n",
+        "        self.flatten = nn.Flatten()\n",
+        "        self.dense1 = nn.Linear(420, 120)\n",
+        "        self.dense2 = nn.Linear(120, 31)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "\n",
+        "        #print('INPUT SHAPE:', x.shape)\n",
+        "        out = self.conv1(x)\n",
+        "\n",
+        "        #print('BLOCK1 INPUT SHAPE:', out.shape)\n",
+        "        out = self.block1_1(out)\n",
+        "        out = self.block1_2(out)\n",
+        "\n",
+        "        #print('BLOCK2 INPUT SHAPE:', out.shape)\n",
+        "        out = self.block2_1(out)\n",
+        "        out = self.block2_2(out)\n",
+        "\n",
+        "        #print('BLOCK3 INPUT SHAPE:', out.shape)\n",
+        "        out = self.block3_1(out)\n",
+        "        out = self.block3_2(out)\n",
+        "        out = self.block3_3(out)\n",
+        "        out = self.block3_4(out)\n",
+        "\n",
+        "        #print('BLOCK4 INPUT SHAPE:', out.shape)\n",
+        "        out = self.block4_1(out)\n",
+        "        out = self.block4_2(out)\n",
+        "        out = self.block4_3(out)\n",
+        "        out = self.block4_4(out)\n",
+        "\n",
+        "        #print('Conv2 INPUT SHAPE:', out.shape)\n",
+        "        out = self.conv2(out)\n",
+        "\n",
+        "        #print('Conv3 INPUT SHAPE:', out.shape)\n",
+        "        out = self.conv3(out)\n",
+        "        out = out.mean(-1, keepdim=True)\n",
+        "\n",
+        "        #print('Conv4 INPUT SHAPE:', out.shape)\n",
+        "        out = self.conv4(out)\n",
+        "\n",
+        "        out = self.adapt(out)\n",
+        "        out = self.flatten(out)\n",
+        "        #print(\"------------>\", out.shape)\n",
+        "        out = self.dense1(out)\n",
+        "        out = F.relu(out)\n",
+        "        out = self.dense2(out)\n",
+        "        out = F.softmax(out)\n",
+        "\n",
+        "        #print('OUTPUT SHAPE:', out.shape)\n",
+        "        return out\n",
+        "\n",
+        "bcresnet = BCResNet().to(device)"
+      ],
+      "execution_count": 28,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
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+            "c10cd61062a5407ab1a9c93c14126b7e",
+            "bded0fd63da64b5c968f1f1deefbfd52"
+          ]
+        },
+        "id": "7WhKLXg_nynf",
+        "outputId": "adb79d61-fc3e-4503-cd1b-9c4edd1c18f8"
+      },
+      "source": [
+        "losses = []\n",
+        "scores = []\n",
+        "out = Output()\n",
+        "display(out)\n",
+        "\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "#optimizer = optim.SGD(bcresnet.parameters(), lr=0.001, momentum=0.9)\n",
+        "optimizer = optim.Adam(bcresnet.parameters(), lr=0.001)\n",
+        "\n",
+        "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)\n",
+        "\n",
+        "for epoch in range(10):  # loop over the dataset multiple times\n",
+        "    running_loss = 0.0\n",
+        "    for i, data in tqdm(enumerate(trainloader, 0), total=len(trainloader)):\n",
+        "        # get the inputs; data is a list of [inputs, labels]\n",
+        "        inputs, labels = data\n",
+        "        inputs = inputs.to(device)\n",
+        "        labels = labels.to(device)\n",
+        "\n",
+        "        # zero the parameter gradients\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        "        # forward + backward + optimize\n",
+        "        outputs = bcresnet(inputs)\n",
+        "        loss = criterion(outputs, labels)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        # print statistics\n",
+        "        running_loss += loss.item()\n",
+        "        if i % 100 == 99:    # print every 100 mini-batches\n",
+        "            acc = get_accuracy(testloader, bcresnet)\n",
+        "            print('[%d, %5d] loss: %.3f, accuracy: %.3f' %\n",
+        "                  (epoch + 1, i + 1, running_loss / 100, acc))\n",
+        "            losses.append(running_loss / 100)\n",
+        "            scores.append(acc)\n",
+        "            plot_progress(losses, scores, out)\n",
+        "            running_loss = 0.0\n",
+        "    scheduler.step()\n",
+        "\n",
+        "print('Finished Training')"
+      ],
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "05ed10d35f254b8ab2a27e7991ab05cb",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6f80fdc8d5b34863bcc368c65f8bdf61",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "  0%|          | 0/3035 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:196: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1,   100] loss: 3.433, accuracy: 3.504\n",
+            "[1,   200] loss: 3.431, accuracy: 3.479\n",
+            "[1,   300] loss: 3.431, accuracy: 3.510\n",
+            "[1,   400] loss: 3.431, accuracy: 3.689\n",
+            "[1,   500] loss: 3.431, accuracy: 3.689\n",
+            "[1,   600] loss: 3.429, accuracy: 3.541\n",
+            "[1,   700] loss: 3.432, accuracy: 3.689\n",
+            "[1,   800] loss: 3.432, accuracy: 3.566\n",
+            "[1,   900] loss: 3.430, accuracy: 3.603\n",
+            "[1,  1000] loss: 3.430, accuracy: 3.467\n",
+            "[1,  1100] loss: 3.430, accuracy: 3.479\n",
+            "[1,  1200] loss: 3.430, accuracy: 3.739\n",
+            "[1,  1300] loss: 3.432, accuracy: 3.547\n",
+            "[1,  1400] loss: 3.430, accuracy: 3.510\n",
+            "[1,  1500] loss: 3.429, accuracy: 3.479\n",
+            "[1,  1600] loss: 3.430, accuracy: 3.479\n",
+            "[1,  1700] loss: 3.433, accuracy: 3.974\n",
+            "[1,  1800] loss: 3.429, accuracy: 4.313\n",
+            "[1,  1900] loss: 3.430, accuracy: 3.479\n",
+            "[1,  2000] loss: 3.431, accuracy: 3.479\n",
+            "[1,  2100] loss: 3.425, accuracy: 3.479\n",
+            "[1,  2200] loss: 3.430, accuracy: 4.456\n",
+            "[1,  2300] loss: 3.426, accuracy: 4.042\n",
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+            "[10,   600] loss: 3.335, accuracy: 3.776\n",
+            "[10,   700] loss: 3.335, accuracy: 3.770\n",
+            "[10,   800] loss: 3.334, accuracy: 4.140\n",
+            "[10,   900] loss: 3.329, accuracy: 3.788\n",
+            "[10,  1000] loss: 3.324, accuracy: 3.955\n",
+            "[10,  1100] loss: 3.323, accuracy: 3.943\n",
+            "[10,  1200] loss: 3.341, accuracy: 3.949\n",
+            "[10,  1300] loss: 3.321, accuracy: 3.770\n",
+            "[10,  1400] loss: 3.319, accuracy: 3.733\n",
+            "[10,  1500] loss: 3.319, accuracy: 3.770\n",
+            "[10,  1600] loss: 3.327, accuracy: 3.949\n",
+            "[10,  1700] loss: 3.337, accuracy: 3.949\n",
+            "[10,  1800] loss: 3.317, accuracy: 3.943\n",
+            "[10,  1900] loss: 3.330, accuracy: 3.770\n",
+            "[10,  2000] loss: 3.322, accuracy: 3.949\n",
+            "[10,  2100] loss: 3.314, accuracy: 3.949\n",
+            "[10,  2200] loss: 3.330, accuracy: 3.819\n",
+            "[10,  2300] loss: 3.326, accuracy: 3.924\n",
+            "[10,  2400] loss: 3.336, accuracy: 3.949\n",
+            "[10,  2500] loss: 3.322, accuracy: 3.770\n",
+            "[10,  2600] loss: 3.320, accuracy: 3.936\n",
+            "[10,  2700] loss: 3.332, accuracy: 3.912\n",
+            "[10,  2800] loss: 3.315, accuracy: 3.936\n",
+            "[10,  2900] loss: 3.317, accuracy: 3.949\n",
+            "[10,  3000] loss: 3.322, accuracy: 3.757\n",
+            "Finished Training\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nus1ewzlnynf"
+      },
+      "source": [
+        "#### Проверим работоспособность модели на видео с youtube"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RtnAky0fMwrT"
+      },
+      "source": [
+        "Применим модель к видео https://www.youtube.com/watch?v=t7w3PHgvKJE  \n",
+        "Это обучающее видео для детей на тему изучения цифр. Соответственно, посмотрим, насколько хорошо модель распознает цифры в речи\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GGSbboxungln",
+        "outputId": "6e22b397-149d-4a66-ecb6-a17949532be3"
+      },
+      "source": [
+        "!pip install youtube_dl"
+      ],
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting youtube_dl\n",
+            "  Downloading youtube_dl-2021.6.6-py2.py3-none-any.whl (1.9 MB)\n",
+            "\u001b[K     |████████████████████████████████| 1.9 MB 8.5 MB/s \n",
+            "\u001b[?25hInstalling collected packages: youtube-dl\n",
+            "Successfully installed youtube-dl-2021.6.6\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "otnHG2idnJKK"
+      },
+      "source": [
+        "Скачиваем звук из видео"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tGoK5fKwNl62",
+        "outputId": "b682d1f4-c9d5-4d43-d57f-7d770fcecaee"
+      },
+      "source": [
+        "from __future__ import unicode_literals\n",
+        "import youtube_dl\n",
+        "\n",
+        "path = \"/content\"\n",
+        "filename = \"video.mp3\"\n",
+        "\n",
+        "ydl_opts = {\n",
+        "    'format': 'bestaudio/best',\n",
+        "    'postprocessors': [{\n",
+        "        'key': 'FFmpegExtractAudio',\n",
+        "        #'preferredcodec': 'mp3',\n",
+        "        'preferredquality': '256'\n",
+        "    }],\n",
+        "    'postprocessor_args': [\n",
+        "        '-ar', '16000',\n",
+        "        '-ac', '1'\n",
+        "        \n",
+        "    ],\n",
+        "    'prefer_ffmpeg': True,\n",
+        "    'keepvideo': True,\n",
+        "    'outtmpl': f\"{path}/{filename}\"\n",
+        "}\n",
+        "\n",
+        "with youtube_dl.YoutubeDL(ydl_opts) as ydl:\n",
+        "    ydl.download(['https://www.youtube.com/watch?v=t7w3PHgvKJE'])"
+      ],
+      "execution_count": 219,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[youtube] t7w3PHgvKJE: Downloading webpage\n",
+            "[download] /content/video.mp3 has already been downloaded\n",
+            "[download] 100% of 5.14MiB\n",
+            "[ffmpeg] Destination: /content/video.opus\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JusuhTE6nOCU"
+      },
+      "source": [
+        "Преобразуем видео к формату, на котором училась модель"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2JCq7CrHXi15"
+      },
+      "source": [
+        "!ffmpeg -i video.opus video.wav"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YiqtF7gtX_gi"
+      },
+      "source": [
+        "video = \"video.wav\""
+      ],
+      "execution_count": 229,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TcBJanoSToD-",
+        "outputId": "aba20a74-0eb0-4d9e-ccc0-34f3a0e6adf9"
+      },
+      "source": [
+        "!sox --info {path}/{video}"
+      ],
+      "execution_count": 230,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Input File     : '/content/video.wav'\n",
+            "Channels       : 2\n",
+            "Sample Rate    : 48000\n",
+            "Precision      : 16-bit\n",
+            "Duration       : 00:05:29.92 = 15836160 samples ~ 24744 CDDA sectors\n",
+            "File Size      : 63.3M\n",
+            "Bit Rate       : 1.54M\n",
+            "Sample Encoding: 16-bit Signed Integer PCM\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "m-etcq0Wqzxp",
+        "outputId": "1e2cb5d7-d4dd-4b15-8add-f8d9131808ae"
+      },
+      "source": [
+        "new_filename = \"video_converted.wav\"\n",
+        "!ffmpeg -i {path}/{video} -vn -ar 16000 -ac 1 {path}/{new_filename}"
+      ],
+      "execution_count": 244,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers\n",
+            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
+            "  configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
+            "  libavutil      55. 78.100 / 55. 78.100\n",
+            "  libavcodec     57.107.100 / 57.107.100\n",
+            "  libavformat    57. 83.100 / 57. 83.100\n",
+            "  libavdevice    57. 10.100 / 57. 10.100\n",
+            "  libavfilter     6.107.100 /  6.107.100\n",
+            "  libavresample   3.  7.  0 /  3.  7.  0\n",
+            "  libswscale      4.  8.100 /  4.  8.100\n",
+            "  libswresample   2.  9.100 /  2.  9.100\n",
+            "  libpostproc    54.  7.100 / 54.  7.100\n",
+            "\u001b[0;33mGuessed Channel Layout for Input Stream #0.0 : stereo\n",
+            "\u001b[0mInput #0, wav, from '/content/video.wav':\n",
+            "  Metadata:\n",
+            "    encoder         : Lavf57.83.100\n",
+            "  Duration: 00:05:29.92, bitrate: 1536 kb/s\n",
+            "    Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 48000 Hz, stereo, s16, 1536 kb/s\n",
+            "Stream mapping:\n",
+            "  Stream #0:0 -> #0:0 (pcm_s16le (native) -> pcm_s16le (native))\n",
+            "Press [q] to stop, [?] for help\n",
+            "Output #0, wav, to '/content/video_converted.wav':\n",
+            "  Metadata:\n",
+            "    ISFT            : Lavf57.83.100\n",
+            "    Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 16000 Hz, mono, s16, 256 kb/s\n",
+            "    Metadata:\n",
+            "      encoder         : Lavc57.107.100 pcm_s16le\n",
+            "size=   10310kB time=00:05:29.92 bitrate= 256.0kbits/s speed=1.32e+03x    \n",
+            "video:0kB audio:10310kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.000739%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F8-mXvwzr513",
+        "outputId": "25918ace-1ce3-401e-ccde-cd1fdd86dbd0"
+      },
+      "source": [
+        "!sox --info {path}/{new_filename}"
+      ],
+      "execution_count": 245,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Input File     : '/content/video_converted.wav'\n",
+            "Channels       : 1\n",
+            "Sample Rate    : 16000\n",
+            "Precision      : 16-bit\n",
+            "Duration       : 00:05:29.92 = 5278720 samples ~ 24744 CDDA sectors\n",
+            "File Size      : 10.6M\n",
+            "Bit Rate       : 256k\n",
+            "Sample Encoding: 16-bit Signed Integer PCM\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PYuCDOScnXUO"
+      },
+      "source": [
+        "Преобразуем данные аналогично тренировочному датасету. При этом нашу аудиозапись нарежем на кусочки длины 160"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "L0ScZ4l4Om9j"
+      },
+      "source": [
+        "def preprocess_sample_1(filepath, max_length=160, step_size=10):\n",
+        "    samples = []\n",
+        "    amplitudes, sr = librosa.core.load(filepath)\n",
+        "    spectrogram = librosa.feature.melspectrogram(amplitudes, sr=sr)\n",
+        "    for i in range(0, spectrogram.shape[1] - max_length, step_size):\n",
+        "      spec = spectrogram[:, i:i+max_length]\n",
+        "      spec = np.pad(spec, [[0, 0], [0, max(0, max_length - spec.shape[1])]], mode='constant')\n",
+        "      spec = np.float32(spec)\n",
+        "      samples.append(spec)\n",
+        "    return samples\n",
+        "\n",
+        "spectrograms = preprocess_sample_1(new_filename)\n",
+        "video_data = np.stack(spectrograms)\n",
+        "video_data = video_data.transpose([0, 2, 1])  # to [batch, time, channels]\n",
+        "video_data = np.expand_dims(video_data, axis=1)"
+      ],
+      "execution_count": 246,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2fGULWvrOm58",
+        "outputId": "c90c4482-507e-42b8-8f9f-7e76155b620f"
+      },
+      "source": [
+        "video_data.shape"
+      ],
+      "execution_count": 247,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1405, 1, 160, 128)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 247
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7Yd4JUlxt-0i"
+      },
+      "source": [
+        "if torch.cuda.is_available:\n",
+        "  device = 'cuda'\n",
+        "else:\n",
+        "  device = 'cpu'\n",
+        "\n",
+        "batch_size = 16\n",
+        "\n",
+        "tensor_x = torch.Tensor(video_data)\n",
+        "dataset = TensorDataset(tensor_x)\n",
+        "\n",
+        "loader = DataLoader(dataset, batch_size=batch_size,\n",
+        "                        shuffle=False, num_workers=2)"
+      ],
+      "execution_count": 248,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2jWTKmMqnk-x"
+      },
+      "source": [
+        "Пропустим данные через модель"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "464fe9e485ea4b6494c8287c8e6dec1e",
+            "e9fc9bb1b08d4b8e9f75781a30bca066",
+            "c6140f89198e496a9d177afca73dbf74",
+            "d760ef65d72c41e39a215fcdf952198b",
+            "305926a06d304cada2d311992bfeb7a5",
+            "c2cdcfb6392b4df59bddace983dc7a73",
+            "4239cb9a15dd4220971de0195bc6c1f2",
+            "1791073dfef649f880db68655ed92a2b",
+            "ae5077630baa4de782b81192eace9cf7",
+            "5fc8bd382b1e4d5698f5431055acbafd",
+            "ef52b106fb354fa583ca7c95eeb3214d"
+          ]
+        },
+        "id": "hCMV8axyt-yq",
+        "outputId": "420254b0-a6a9-436a-ff3f-10a415e43a9a"
+      },
+      "source": [
+        "preds = []\n",
+        "mill = 0\n",
+        "with torch.no_grad():\n",
+        "    for i, x in tqdm(enumerate(loader)):\n",
+        "        x = x[0].to(device)\n",
+        "        \n",
+        "        scores = net(x)\n",
+        "        _, predictions = scores.max(1)\n",
+        "        for j, cl in enumerate(predictions):\n",
+        "          preds.append((mill, i*batch_size + j, classes[cl]))\n",
+        "          mill = mill + 5"
+      ],
+      "execution_count": 249,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "464fe9e485ea4b6494c8287c8e6dec1e",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "0it [00:00, ?it/s]"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zAEa5D85nwQy"
+      },
+      "source": [
+        "Выделим из всех предсказаний только предсказания цифр"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qF53lFNdt-wr"
+      },
+      "source": [
+        "number_preds = [pr for pr in preds if pr[2] in ['eight', 'five', 'four', 'nine', 'one', 'seven', 'six', 'three', 'two', 'zero']]"
+      ],
+      "execution_count": 274,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W2-D53VXt-uO",
+        "outputId": "336a820e-e942-4073-c538-a4c46dcd79c3"
+      },
+      "source": [
+        "number_preds[:5]"
+      ],
+      "execution_count": 275,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[(55, 11, 'three'),\n",
+              " (60, 12, 'three'),\n",
+              " (65, 13, 'zero'),\n",
+              " (120, 24, 'three'),\n",
+              " (145, 29, 'three')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 275
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_lwu94hmn2Uu"
+      },
+      "source": [
+        "Для наглядности выведем предсказания модели одновременно с фреймом видео, для которого было сделано предсказание"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EUvKK3nz-I6x"
+      },
+      "source": [
+        "from PIL import Image\n",
+        "import numpy as np\n",
+        "\n",
+        "def extract_frames(url, times):\n",
+        "  frames = []\n",
+        "  output_frame = 'frame.jpg'\n",
+        "  for t in tqdm(times):\n",
+        "    !ffmpeg -ss {t} -i $(youtube-dl -f 22 --get-url \"{url}\") -vframes 1 -q:v 2 {output_frame} -y\n",
+        "    frames.append(np.array(Image.open(output_frame)))\n",
+        "  return frames\n",
+        "\n",
+        "#frames = extract_frames('https://www.youtube.com/watch?v=t7w3PHgvKJE', [5, 20, 50, 100])"
+      ],
+      "execution_count": 276,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "2b64d39a15c346eca9131afa28d73d61",
+            "31846b7b23264098908b25d2bff42745",
+            "524a57363f6f4336a64951ab4478e988",
+            "1394abf1052b458894fbbc36bf53342c",
+            "6b1aa22bb9d04ea5aee72288216d630c",
+            "52b9b154b1a94dd389d690645cf05234",
+            "34d40d1f1ee140ec866b151d4d7397bb",
+            "6c93b5131d4a4e71aaac4dba2ec6c0ee",
+            "09e16853b44a4c74bb249cfdbb2f8c94",
+            "ccba0222efbf4d1db61d54af1e04c8e9",
+            "1f65c1cb24274a5da9ff794832ab53b2"
+          ]
+        },
+        "id": "xwvpYEbtIIEe",
+        "outputId": "12ad5319-ac77-4995-fde0-5705fee12e1c"
+      },
+      "source": [
+        "import random\n",
+        "random_nums = []\n",
+        "for _ in range(4):\n",
+        "  for _cls in ['three', 'zero']:\n",
+        "    print(tmp)\n",
+        "    tmp = [rn for rn in number_preds if rn[-1] == _cls]\n",
+        "    random_nums.append(random.choice(tmp))\n",
+        "frames = extract_frames('https://www.youtube.com/watch?v=t7w3PHgvKJE', [t[0]*0.01 for t in random_nums])\n",
+        "#random_nums"
+      ],
+      "execution_count": 295,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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+              "  0%|          | 0/8 [00:00<?, ?it/s]"
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+            "ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers\n",
+            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
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+            "  libavutil      55. 78.100 / 55. 78.100\n",
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+            "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'https://r5---sn-qxoedne7.googlevideo.com/videoplayback?expire=1635374503&ei=R4F5YYX9L8i2kwbizYHQBQ&ip=35.230.91.161&id=o-ALjakoh7YMN1reu4jlaZnNZz8tgAwa6jqnKJ4iyyUUJx&itag=22&source=youtube&requiressl=yes&mh=uD&mm=31%2C29&mn=sn-qxoedne7%2Csn-qxo7rn7e&ms=au%2Crdu&mv=m&mvi=5&pl=20&initcwndbps=1802500&vprv=1&mime=video%2Fmp4&ns=gZegFHljMaQoGwc98i16pN0G&ratebypass=yes&dur=329.955&lmt=1633970271919764&mt=1635352619&fvip=5&fexp=24001373%2C24007246&c=WEB&txp=5432434&n=BWnazbUaCKoXeizLiktK&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cvprv%2Cmime%2Cns%2Cratebypass%2Cdur%2Clmt&sig=AOq0QJ8wRgIhALrqnVKWcgKfgas6kXlH7MUW11QyM9KsUwvfYmDQ607SAiEAvNeh7Cuniq-D2b6rj91ZOC8QtLzKsYqV0PqKGo5SIho%3D&lsparams=mh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Cinitcwndbps&lsig=AG3C_xAwRAIgLN68BTUwOrnldoeZ_wMUTByKDekfmzuFoEEXkQ6jQycCIC7K6YPOZnLbj9lJY-sNWiYJpJr5cAjQ8dP0iggNtBcv':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Google\n",
+            "  Duration: 00:05:29.89, start: 0.000000, bitrate: 1087 kb/s\n",
+            "    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 1280x720 [SAR 1:1 DAR 16:9], 957 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)\n",
+            "    Metadata:\n",
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+            "    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "Stream mapping:\n",
+            "  Stream #0:0 -> #0:0 (h264 (native) -> mjpeg (native))\n",
+            "Press [q] to stop, [?] for help\n",
+            "\u001b[1;34m[swscaler @ 0x5619dbe9a000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
+            "\u001b[0mOutput #0, image2, to 'frame.jpg':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
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+            "    Metadata:\n",
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+            "      encoder         : Lavc57.107.100 mjpeg\n",
+            "    Side data:\n",
+            "      cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: -1\n",
+            "frame=    1 fps=0.0 q=2.0 Lsize=N/A time=00:00:00.04 bitrate=N/A speed=0.068x    \n",
+            "video:22kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown\n",
+            "ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers\n",
+            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
+            "  configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
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+            "  libswresample   2.  9.100 /  2.  9.100\n",
+            "  libpostproc    54.  7.100 / 54.  7.100\n",
+            "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'https://r5---sn-qxoedne7.googlevideo.com/videoplayback?expire=1635374507&ei=S4F5YYftK5nykgat7qNo&ip=35.230.91.161&id=o-AEbFo8kTGd7Jl82uM2wd6Z98WQR2O7KmtIz4p-Pk9Xzy&itag=22&source=youtube&requiressl=yes&mh=uD&mm=31%2C26&mn=sn-qxoedne7%2Csn-5goeen76&ms=au%2Conr&mv=m&mvi=5&pl=20&initcwndbps=1802500&vprv=1&mime=video%2Fmp4&ns=yVO1QwFpI72tVvzuI5tZ8AEG&ratebypass=yes&dur=329.955&lmt=1633970271919764&mt=1635352619&fvip=5&fexp=24001373%2C24007246&c=WEB&txp=5432434&n=LJvuk5ym1Tdw3XVyy3yx&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cvprv%2Cmime%2Cns%2Cratebypass%2Cdur%2Clmt&sig=AOq0QJ8wRgIhAIOVjXNC_nDET_gv1q3DDd7m6rdowhheI7VJ_9WjVh0AAiEAhd3zE5sIiWJtrC11FOR68aykGllD5wM2IMBraZ4a38A%3D&lsparams=mh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Cinitcwndbps&lsig=AG3C_xAwRAIgfVqu0XFXwtBzPfBDzK8i9gexTdw9vkdSfZcmAwrr2DgCIAjFLquiDkrOgA6Iyk9-RcVB5RpIB6qygB8gFV-mKkk4':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Google\n",
+            "  Duration: 00:05:29.89, start: 0.000000, bitrate: 1087 kb/s\n",
+            "    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 1280x720 [SAR 1:1 DAR 16:9], 957 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "Stream mapping:\n",
+            "  Stream #0:0 -> #0:0 (h264 (native) -> mjpeg (native))\n",
+            "Press [q] to stop, [?] for help\n",
+            "\u001b[1;34m[swscaler @ 0x55c83c2ce000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
+            "\u001b[0mOutput #0, image2, to 'frame.jpg':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Lavf57.83.100\n",
+            "    Stream #0:0(und): Video: mjpeg, yuvj420p(pc), 1280x720 [SAR 1:1 DAR 16:9], q=2-31, 200 kb/s, 25 fps, 25 tbn, 25 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "      encoder         : Lavc57.107.100 mjpeg\n",
+            "    Side data:\n",
+            "      cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: -1\n",
+            "frame=    1 fps=0.3 q=2.0 Lsize=N/A time=00:00:00.04 bitrate=N/A speed=0.0111x    \n",
+            "video:44kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown\n",
+            "ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers\n",
+            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
+            "  configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
+            "  libavutil      55. 78.100 / 55. 78.100\n",
+            "  libavcodec     57.107.100 / 57.107.100\n",
+            "  libavformat    57. 83.100 / 57. 83.100\n",
+            "  libavdevice    57. 10.100 / 57. 10.100\n",
+            "  libavfilter     6.107.100 /  6.107.100\n",
+            "  libavresample   3.  7.  0 /  3.  7.  0\n",
+            "  libswscale      4.  8.100 /  4.  8.100\n",
+            "  libswresample   2.  9.100 /  2.  9.100\n",
+            "  libpostproc    54.  7.100 / 54.  7.100\n",
+            "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'https://r5---sn-qxo7rn7e.googlevideo.com/videoplayback?expire=1635374514&ei=UoF5YZHsJ5CukwaLsaywDw&ip=35.230.91.161&id=o-AP1VDgMmvPB6p2lunHjggpPE4Iyys_UFsBf1s9KtgkDp&itag=22&source=youtube&requiressl=yes&mh=uD&mm=31%2C26&mn=sn-qxo7rn7e%2Csn-5goeen76&ms=au%2Conr&mv=m&mvi=5&pl=20&initcwndbps=1802500&vprv=1&mime=video%2Fmp4&ns=daLhZWEKjDM9T1UpDn6iMAUG&ratebypass=yes&dur=329.955&lmt=1633970271919764&mt=1635352619&fvip=5&fexp=24001373%2C24007246&c=WEB&txp=5432434&n=xHoHGSyVf2dVbotlqHFN&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cvprv%2Cmime%2Cns%2Cratebypass%2Cdur%2Clmt&sig=AOq0QJ8wRQIgbTuUPQHPDx7kJR5EZnk8LeZ9aFVzLfe8zSZxlt8_14ECIQCS3oGi9WJ9qcKaG2NykekUbJU3hKlfDqodO4Kf5Ri48Q%3D%3D&lsparams=mh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Cinitcwndbps&lsig=AG3C_xAwRAIgZPqBFufWpr3hClstkgE4FIxdTWG2DzGXrFEC_FfJOpACIBZoyewNSJlyJqIaIPeAIWNt-kz9-EEc-Wgwj90rUD54':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Google\n",
+            "  Duration: 00:05:29.89, start: 0.000000, bitrate: 1087 kb/s\n",
+            "    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 1280x720 [SAR 1:1 DAR 16:9], 957 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "Stream mapping:\n",
+            "  Stream #0:0 -> #0:0 (h264 (native) -> mjpeg (native))\n",
+            "Press [q] to stop, [?] for help\n",
+            "\u001b[1;34m[swscaler @ 0x5606b8344000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
+            "\u001b[0mOutput #0, image2, to 'frame.jpg':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Lavf57.83.100\n",
+            "    Stream #0:0(und): Video: mjpeg, yuvj420p(pc), 1280x720 [SAR 1:1 DAR 16:9], q=2-31, 200 kb/s, 25 fps, 25 tbn, 25 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "      encoder         : Lavc57.107.100 mjpeg\n",
+            "    Side data:\n",
+            "      cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: -1\n",
+            "frame=    1 fps=0.3 q=2.0 Lsize=N/A time=00:00:00.04 bitrate=N/A speed=0.0115x    \n",
+            "video:59kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown\n",
+            "ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers\n",
+            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
+            "  configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
+            "  libavutil      55. 78.100 / 55. 78.100\n",
+            "  libavcodec     57.107.100 / 57.107.100\n",
+            "  libavformat    57. 83.100 / 57. 83.100\n",
+            "  libavdevice    57. 10.100 / 57. 10.100\n",
+            "  libavfilter     6.107.100 /  6.107.100\n",
+            "  libavresample   3.  7.  0 /  3.  7.  0\n",
+            "  libswscale      4.  8.100 /  4.  8.100\n",
+            "  libswresample   2.  9.100 /  2.  9.100\n",
+            "  libpostproc    54.  7.100 / 54.  7.100\n",
+            "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'https://r5---sn-qxoedne7.googlevideo.com/videoplayback?expire=1635374521&ei=WYF5YeT1AdrRkgbK7pHYCA&ip=35.230.91.161&id=o-AErLYtF_4evVcGk-f_RiFkCYi6e-57O73oPtuP2CO0IR&itag=22&source=youtube&requiressl=yes&mh=uD&mm=31%2C26&mn=sn-qxoedne7%2Csn-5goeen76&ms=au%2Conr&mv=m&mvi=5&pl=20&initcwndbps=1802500&vprv=1&mime=video%2Fmp4&ns=uDCk0vLwEGbJ9A1NmnozHdIG&ratebypass=yes&dur=329.955&lmt=1633970271919764&mt=1635352619&fvip=5&fexp=24001373%2C24007246&c=WEB&txp=5432434&n=9yyMeJoaEXG3V8-Z_-RK&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cvprv%2Cmime%2Cns%2Cratebypass%2Cdur%2Clmt&sig=AOq0QJ8wRQIhAJlL7oyQ4br5Q94WKQ4jM-g6CzQzloNkUDzFTGjDLu0SAiA_t1FiM7SKM0lU4GE1wvDMxr0K31IJ61OCID9_TBVIaA%3D%3D&lsparams=mh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Cinitcwndbps&lsig=AG3C_xAwRQIhALnpUjnVT8-n7I5Bm-T5MO1ke5CLVT244UBm2A8Q6bXdAiBQj64l1EOiqeNoEBZCXFEC2HzUjUlBm9KQJtLByoRuYA%3D%3D':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Google\n",
+            "  Duration: 00:05:29.89, start: 0.000000, bitrate: 1087 kb/s\n",
+            "    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 1280x720 [SAR 1:1 DAR 16:9], 957 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "Stream mapping:\n",
+            "  Stream #0:0 -> #0:0 (h264 (native) -> mjpeg (native))\n",
+            "Press [q] to stop, [?] for help\n",
+            "\u001b[1;34m[swscaler @ 0x55d3fae7e000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
+            "\u001b[0mOutput #0, image2, to 'frame.jpg':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Lavf57.83.100\n",
+            "    Stream #0:0(und): Video: mjpeg, yuvj420p(pc), 1280x720 [SAR 1:1 DAR 16:9], q=2-31, 200 kb/s, 25 fps, 25 tbn, 25 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "      encoder         : Lavc57.107.100 mjpeg\n",
+            "    Side data:\n",
+            "      cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: -1\n",
+            "frame=    1 fps=0.0 q=2.0 Lsize=N/A time=00:00:00.04 bitrate=N/A speed=0.158x    \n",
+            "video:26kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown\n",
+            "ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers\n",
+            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
+            "  configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
+            "  libavutil      55. 78.100 / 55. 78.100\n",
+            "  libavcodec     57.107.100 / 57.107.100\n",
+            "  libavformat    57. 83.100 / 57. 83.100\n",
+            "  libavdevice    57. 10.100 / 57. 10.100\n",
+            "  libavfilter     6.107.100 /  6.107.100\n",
+            "  libavresample   3.  7.  0 /  3.  7.  0\n",
+            "  libswscale      4.  8.100 /  4.  8.100\n",
+            "  libswresample   2.  9.100 /  2.  9.100\n",
+            "  libpostproc    54.  7.100 / 54.  7.100\n",
+            "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'https://r5---sn-qxoedne7.googlevideo.com/videoplayback?expire=1635374522&ei=WoF5YeKDNNGPkwbs0o6wBw&ip=35.230.91.161&id=o-AHcwQsZcCACAVoS0VMfjBTAtkGn6fvJESqWtOIpzSOa9&itag=22&source=youtube&requiressl=yes&mh=uD&mm=31%2C29&mn=sn-qxoedne7%2Csn-qxo7rn7e&ms=au%2Crdu&mv=m&mvi=5&pl=20&initcwndbps=1802500&vprv=1&mime=video%2Fmp4&ns=JJ-6o91ckIqdq4GvgtyFe08G&ratebypass=yes&dur=329.955&lmt=1633970271919764&mt=1635352619&fvip=5&fexp=24001373%2C24007246&c=WEB&txp=5432434&n=Ma5jsL7N98Hs9VZMWr59&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cvprv%2Cmime%2Cns%2Cratebypass%2Cdur%2Clmt&sig=AOq0QJ8wRAIgZLOe9m1Iq73vTFSV_3GfGoN9B-9ZjtMNg4vUKiwRHS0CIHjbzDB7JyRMeaHLYrb3u6qXhOELqoUcwsi7e-bg2cyb&lsparams=mh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Cinitcwndbps&lsig=AG3C_xAwRQIhAJaaV1eJfsLINhN4mfITkz53u3HNSqXFga6gc1VChQK6AiA0XNTSqTTdxquZjgvMLJw9koyxTIUTPDLuibUs11CpmQ%3D%3D':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Google\n",
+            "  Duration: 00:05:29.89, start: 0.000000, bitrate: 1087 kb/s\n",
+            "    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 1280x720 [SAR 1:1 DAR 16:9], 957 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "Stream mapping:\n",
+            "  Stream #0:0 -> #0:0 (h264 (native) -> mjpeg (native))\n",
+            "Press [q] to stop, [?] for help\n",
+            "\u001b[1;34m[swscaler @ 0x555693acc000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
+            "\u001b[0mOutput #0, image2, to 'frame.jpg':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Lavf57.83.100\n",
+            "    Stream #0:0(und): Video: mjpeg, yuvj420p(pc), 1280x720 [SAR 1:1 DAR 16:9], q=2-31, 200 kb/s, 25 fps, 25 tbn, 25 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "      encoder         : Lavc57.107.100 mjpeg\n",
+            "    Side data:\n",
+            "      cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: -1\n",
+            "frame=    1 fps=0.5 q=2.0 Lsize=N/A time=00:00:00.04 bitrate=N/A speed=0.0203x    \n",
+            "video:51kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown\n",
+            "ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers\n",
+            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
+            "  configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
+            "  libavutil      55. 78.100 / 55. 78.100\n",
+            "  libavcodec     57.107.100 / 57.107.100\n",
+            "  libavformat    57. 83.100 / 57. 83.100\n",
+            "  libavdevice    57. 10.100 / 57. 10.100\n",
+            "  libavfilter     6.107.100 /  6.107.100\n",
+            "  libavresample   3.  7.  0 /  3.  7.  0\n",
+            "  libswscale      4.  8.100 /  4.  8.100\n",
+            "  libswresample   2.  9.100 /  2.  9.100\n",
+            "  libpostproc    54.  7.100 / 54.  7.100\n",
+            "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'https://r5---sn-qxoedne7.googlevideo.com/videoplayback?expire=1635374528&ei=YIF5YcGNB56BsfIP046cOA&ip=35.230.91.161&id=o-AOVHyfFbI_yqXq-sjMGyCpxO4KYfRGApbPr8JpTwoJ8y&itag=22&source=youtube&requiressl=yes&mh=uD&mm=31%2C26&mn=sn-qxoedne7%2Csn-5go7yner&ms=au%2Conr&mv=m&mvi=5&pl=20&initcwndbps=1802500&vprv=1&mime=video%2Fmp4&ns=Wkgqu79pZMk-rr45bc7krWQG&ratebypass=yes&dur=329.955&lmt=1633970271919764&mt=1635352619&fvip=5&fexp=24001373%2C24007246&c=WEB&txp=5432434&n=3mTwx7yGy3HxAGeoxJvA&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cvprv%2Cmime%2Cns%2Cratebypass%2Cdur%2Clmt&sig=AOq0QJ8wRAIgYpW8y6OUj4c0Xk1HQKYdfEJqK-_KkFotWnOjpOwBA7MCIDgdscN8eTRBbz5G63_zwJd7hPxapP1AJThzddLqVEzi&lsparams=mh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Cinitcwndbps&lsig=AG3C_xAwRQIhAMSUkPMEAXUHylidOnlsPqZKpWX8vbM75pkCBWx0Zvd1AiBJ5IGziXwwi_K9KALy0YCIlHF0y4G2QgxkHeHGseiYBw%3D%3D':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Google\n",
+            "  Duration: 00:05:29.89, start: 0.000000, bitrate: 1087 kb/s\n",
+            "    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 1280x720 [SAR 1:1 DAR 16:9], 957 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "Stream mapping:\n",
+            "  Stream #0:0 -> #0:0 (h264 (native) -> mjpeg (native))\n",
+            "Press [q] to stop, [?] for help\n",
+            "\u001b[1;34m[swscaler @ 0x55598d9ea000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
+            "\u001b[0mOutput #0, image2, to 'frame.jpg':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Lavf57.83.100\n",
+            "    Stream #0:0(und): Video: mjpeg, yuvj420p(pc), 1280x720 [SAR 1:1 DAR 16:9], q=2-31, 200 kb/s, 25 fps, 25 tbn, 25 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "      encoder         : Lavc57.107.100 mjpeg\n",
+            "    Side data:\n",
+            "      cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: -1\n",
+            "frame=    1 fps=0.2 q=2.0 Lsize=N/A time=00:00:00.04 bitrate=N/A speed=0.00671x    \n",
+            "video:45kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown\n",
+            "ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers\n",
+            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
+            "  configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
+            "  libavutil      55. 78.100 / 55. 78.100\n",
+            "  libavcodec     57.107.100 / 57.107.100\n",
+            "  libavformat    57. 83.100 / 57. 83.100\n",
+            "  libavdevice    57. 10.100 / 57. 10.100\n",
+            "  libavfilter     6.107.100 /  6.107.100\n",
+            "  libavresample   3.  7.  0 /  3.  7.  0\n",
+            "  libswscale      4.  8.100 /  4.  8.100\n",
+            "  libswresample   2.  9.100 /  2.  9.100\n",
+            "  libpostproc    54.  7.100 / 54.  7.100\n",
+            "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'https://r5---sn-qxoedne7.googlevideo.com/videoplayback?expire=1635374537&ei=aYF5YYv0FYnRkgbj4LDwDA&ip=35.230.91.161&id=o-AGS1XFZz-L9e2pH0adUpi4SdXhVZpXpewxNCUjoYF_97&itag=22&source=youtube&requiressl=yes&mh=uD&mm=31%2C26&mn=sn-qxoedne7%2Csn-5goeen76&ms=au%2Conr&mv=m&mvi=5&pl=20&initcwndbps=1802500&vprv=1&mime=video%2Fmp4&ns=9q5ufJqq-FyJcThEW-twyE8G&ratebypass=yes&dur=329.955&lmt=1633970271919764&mt=1635352619&fvip=5&fexp=24001373%2C24007246&c=WEB&txp=5432434&n=MTHESX9lGIbOv6f5Zswp&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cvprv%2Cmime%2Cns%2Cratebypass%2Cdur%2Clmt&sig=AOq0QJ8wRQIgAv3s7Q4NAPG1voCQWsX94FJiARZu4oN17t2d9A4D9pwCIQCPlDJWournXN6w2he88pT2evk3imLWlUkOBuSaBUVzUA%3D%3D&lsparams=mh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Cinitcwndbps&lsig=AG3C_xAwRQIgEkWo59gslEpo8iMEwQGYuNra4h9DrnCzG3hDXX4NjHwCIQCBK2Jhx1sdLy0cMjFcv4LZShK-A1thfGVoMmA-Ylotlg%3D%3D':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Google\n",
+            "  Duration: 00:05:29.89, start: 0.000000, bitrate: 1087 kb/s\n",
+            "    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 1280x720 [SAR 1:1 DAR 16:9], 957 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "Stream mapping:\n",
+            "  Stream #0:0 -> #0:0 (h264 (native) -> mjpeg (native))\n",
+            "Press [q] to stop, [?] for help\n",
+            "\u001b[1;34m[swscaler @ 0x561864cc4000] \u001b[0m\u001b[0;33mdeprecated pixel format used, make sure you did set range correctly\n",
+            "\u001b[0mOutput #0, image2, to 'frame.jpg':\n",
+            "  Metadata:\n",
+            "    major_brand     : mp42\n",
+            "    minor_version   : 0\n",
+            "    compatible_brands: isommp42\n",
+            "    encoder         : Lavf57.83.100\n",
+            "    Stream #0:0(und): Video: mjpeg, yuvj420p(pc), 1280x720 [SAR 1:1 DAR 16:9], q=2-31, 200 kb/s, 25 fps, 25 tbn, 25 tbc (default)\n",
+            "    Metadata:\n",
+            "      handler_name    : ISO Media file produced by Google Inc.\n",
+            "      encoder         : Lavc57.107.100 mjpeg\n",
+            "    Side data:\n",
+            "      cpb: bitrate max/min/avg: 0/0/200000 buffer size: 0 vbv_delay: -1\n",
+            "frame=    1 fps=0.7 q=2.0 Lsize=N/A time=00:00:00.04 bitrate=N/A speed=0.0286x    \n",
+            "video:35kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown\n",
+            "ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers\n",
+            "  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)\n",
+            "  configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared\n",
+            "  libavutil      55. 78.100 / 55. 78.100\n",
+            "  libavcodec     57.107.100 / 57.107.100\n",
+            "  libavformat    57. 83.100 / 57. 83.100\n",
+            "  libavdevice    57. 10.100 / 57. 10.100\n",
+            "  libavfilter     6.107.100 /  6.107.100\n",
+            "  libavresample   3.  7.  0 /  3.  7.  0\n",
+            "  libswscale      4.  8.100 /  4.  8.100\n",
+            "  libswresample   2.  9.100 /  2.  9.100\n",
+            "  libpostproc    54.  7.100 / 54.  7.100\n",
+            "\u001b[0;39m[https @ 0x55c79ae0a800] \u001b[0m\u001b[0;33mHTTP error 403 Forbidden\n",
+            "\u001b[0m\u001b[1;31mhttps://r5---sn-qxoedne7.googlevideo.com/videoplayback?expire=1635374542&ei=boF5YYGNBpKOkgaHnaCgAQ&ip=35.230.91.161&id=o-ADvkb9lphhh4pZupbsrYbacOlQLWFKzMhGQj-DzOfpoM&itag=22&source=youtube&requiressl=yes&mh=uD&mm=31%2C29&mn=sn-qxoedne7%2Csn-qxo7rn7e&ms=au%2Crdu&mv=m&mvi=5&pl=20&initcwndbps=1802500&vprv=1&mime=video%2Fmp4&ns=EplNOAYUoSUXsKu9hLVxGLsG&ratebypass=yes&dur=329.955&lmt=1633970271919764&mt=1635352619&fvip=5&fexp=24001373%2C24007246&c=WEB&txp=5432434&n=d69R9CDqrwo-MAIb9rUJ&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cvprv%2Cmime%2Cns%2Cratebypass%2Cdur%2Clmt&sig=AOq0QJ8wRQIgQHiMcFp9tW00C28nBw2yOb0xOG598UNfDwIPOWttWKgCIQCn-AlVFNM5Ky7JMpN_ZGlFD5o8v7uJ5Hv8bs8QDKQLnQ%3D%3D&lsparams=mh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Cinitcwndbps&lsig=AG3C_xAwRAIgOEOHIkZY1D3FCcrnyW_HtJrZyBJqizNEu7-WySbheEwCIFuqv1tkPPK5bRq3nW1V66sLK8amKB7-Mk35wl7Y6x2p: Server returned 403 Forbidden (access denied)\n",
+            "\u001b[0m"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "b43iAIHDIICG"
+      },
+      "source": [
+        "def plot_multy(data_plot, frames, num_cols=3, figsize=(12,8)):\n",
+        "  num_rows = (len(data_plot) // (num_cols + 1)) + 1\n",
+        "  count = 0\n",
+        "  fig, ax = plt.subplots(num_rows, num_cols, figsize=figsize)\n",
+        "  for i in range(num_rows) :\n",
+        "    for j in range(num_cols):\n",
+        "      if count >= len(data_plot):\n",
+        "        break\n",
+        "      if num_rows <= 1:\n",
+        "        ax[j].imshow(frames[count])\n",
+        "        ax[j].axis('off')\n",
+        "        ax[j].text(3,-0.1, data_plot[count][-1], size=12, ha=\"center\")\n",
+        "      else:   \n",
+        "        ax[i, j].imshow(frames[count])\n",
+        "        ax[i, j].axis('off')\n",
+        "        ax[i, j].text(3,-0.1, data_plot[count][-1], size=12, ha=\"center\")\n",
+        "      count += 1\n",
+        "  plt.show()"
+      ],
+      "execution_count": 255,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "id": "7yh0NKwiIIAb",
+        "outputId": "38a6ac06-d9b4-4bde-f8be-e706129dd918"
+      },
+      "source": [
+        "plot_multy(random_nums, frames)"
+      ],
+      "execution_count": 296,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 864x576 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zWUNPqHMoSMp"
+      },
+      "source": [
+        "#### Итоги"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UCYIYZsVoYko"
+      },
+      "source": [
+        "Модель работает плохо. Из видео выделяются фреймы с цифрами, но сами предсказания неверные. Это соотносится с тем, что точность модели на тренировке низкая.  \n",
+        "\n",
+        "Требуется дополнительное исследование"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "L-oh2wovIH7B"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file