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Реализованы все job-ы для решения задач. #7

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b83bf46
Реализованы все job-ы для решения задач.
NSuprotivniy Nov 25, 2017
1030fbc
Добавлены результаты подсчёта в answer.txt.
NSuprotivniy Nov 25, 2017
d78986a
Добавлены результаты замеров работы с комбайнером и без.
NSuprotivniy Nov 25, 2017
0a7d857
50 epochs trainning
NSuprotivniy Apr 25, 2019
2ee52a1
evaluation
NSuprotivniy Apr 25, 2019
128010d
autoencoder
NSuprotivniy Apr 25, 2019
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2 changes: 2 additions & 0 deletions .gitignore
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.idea
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79 changes: 79 additions & 0 deletions 2017-big-data.iml
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9 changes: 6 additions & 3 deletions answers.txt
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2. is 126420
3. 41.602
4. french 5742
Stop words: 315 / 50111754
Top 7 word: was 18390091
Top 5 name: October 1072212

Время выполняения без комбайнеров: 1 час 3 минуты
Время выполнения с комбайнерами: 1 час 52 минут
294 changes: 294 additions & 0 deletions autoencoder.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "autoencoder.ipynb",
"version": "0.3.2",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/NSuprotivniy/2017-big-data/blob/master/autoencoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "tOU8ff_RJ7zY",
"colab_type": "code",
"outputId": "3894c90e-508e-4144-9e1b-fd8c2c855f70",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 326
}
},
"cell_type": "code",
"source": [
"pip install git+https://github.com/rcmalli/keras-vggface.git"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting git+https://github.com/rcmalli/keras-vggface.git\n",
" Cloning https://github.com/rcmalli/keras-vggface.git to /tmp/pip-req-build-xfo8xg7l\n",
"Requirement already satisfied (use --upgrade to upgrade): keras-vggface==0.5 from git+https://github.com/rcmalli/keras-vggface.git in /usr/local/lib/python3.6/dist-packages\n",
"Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (1.16.3)\n",
"Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (1.2.1)\n",
"Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (2.8.0)\n",
"Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (4.3.0)\n",
"Requirement already satisfied: keras in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (2.2.4)\n",
"Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (1.12.0)\n",
"Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (3.13)\n",
"Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->keras-vggface==0.5) (0.46)\n",
"Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras->keras-vggface==0.5) (1.0.9)\n",
"Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras->keras-vggface==0.5) (1.0.7)\n",
"Building wheels for collected packages: keras-vggface\n",
" Building wheel for keras-vggface (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Stored in directory: /tmp/pip-ephem-wheel-cache-yvofgm87/wheels/36/07/46/06c25ce8e9cd396dabe151ea1d8a2bc28dafcb11321c1f3a6d\n",
"Successfully built keras-vggface\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "ZfPNLVe9JoyO",
"colab_type": "code",
"outputId": "37054b0a-5378-4fa4-f466-f52b16b4f1c3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"from keras.engine import Model\n",
"from keras.layers import Flatten, Dense, Input, Dropout, Reshape\n",
"from keras_vggface.vggface import VGGFace\n",
"from keras.utils import Sequence\n",
"from keras.callbacks import ModelCheckpoint\n",
"from keras.optimizers import Adam\n",
"import tensorflow as tf\n",
"from keras import backend as K"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
}
]
},
{
"metadata": {
"id": "-W3_Na-7Sx9u",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import os\n",
"from glob import glob\n",
"import numpy as np\n",
"from google.colab import drive\n",
"import math"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "mIG0AxdaKaUb",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"def get_model():\n",
" vgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3), model='resnet50')\n",
" vgg_model.trainable = False\n",
" last_layer = vgg_model.get_layer('avg_pool').output\n",
" x = Flatten(name='flatten')(last_layer)\n",
" out = Dense(2048, activation='relu')(x)\n",
" encoder = Model(vgg_model.input, out, name=\"resnet50_faces_encoder\")\n",
" \n",
" input_encoded = Input(shape=(2048,))\n",
" flat_decoded = Dense(224*224*3, activation='relu')(input_encoded)\n",
" decoded = Reshape((224, 224, 3))(flat_decoded)\n",
" \n",
" decoder = Model(input_encoded, decoded)\n",
"\n",
" autoencoder = Model(vgg_model.input, decoder(encoder(vgg_model.input)))\n",
" autoencoder.compile(optimizer=Adam(lr=0.000001), loss='binary_crossentropy')\n",
" return encoder, autoencoder"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "-Tq1VVX-kSQc",
"colab_type": "code",
"outputId": "20b5f9d0-a0e6-435d-bf66-6c877836bdbc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"drive.mount(\"/content/nsuprotivniy/\")\n",
"data_path = \"/content/nsuprotivniy/My Drive/Colab Notebooks/data/LCC_FASD/\""
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"Drive already mounted at /content/nsuprotivniy/; to attempt to forcibly remount, call drive.mount(\"/content/nsuprotivniy/\", force_remount=True).\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "CDOcZhKPZGqX",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"class Generator(Sequence): \n",
" def __init__(self, batch_size = 2):\n",
" files = glob(os.path.join(data_path, \"*/*/cropped/*.npy\")) + glob(os.path.join(data_path, \"LCC_FASD_evaluation/cropped/*.npy\"))\n",
" self.batches = np.array_split(files, math.ceil(len(files) / batch_size))\n",
" \n",
" def __len__(self):\n",
" return len(self.batches)\n",
"\n",
" def __getitem__(self, idx):\n",
" X = np.array([np.load(path) for path in self.batches[idx]])\n",
" return X, X"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "22gRJnAuNtOu",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"gen = Generator()"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "eZkLM1f74371",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"model_path = os.path.join(data_path, \"autoencoder\")\n",
"if (not os.path.exists(model_path)):\n",
" os.mkdir(model_path)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "9KBbrZtrMlYt",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"checkpoints_path = os.path.join(model_path, \"checkpoints\")\n",
"if (not os.path.exists(checkpoints_path)):\n",
" os.mkdir(checkpoints_path)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "S_xR37tueCy1",
"colab_type": "code",
"outputId": "2ef79dbf-9a0b-40d8-9835-9afe412e4406",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 207
}
},
"cell_type": "code",
"source": [
"checkpoint_filename = \"autoencoder_model_weights.h5\"\n",
"checkpoint = [ModelCheckpoint(os.path.join(checkpoints_path, checkpoint_filename), verbose=1, period=5)]\n",
"encoder, autoencoder = get_model()\n",
"autoencoder.fit_generator(gen, epochs=100, verbose=1, callbacks=checkpoint)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"Epoch 1/100\n",
"9414/9414 [==============================] - 3506s 372ms/step - loss: -1288.1002\n",
"Epoch 2/100\n",
" 245/9414 [..............................] - ETA: 46:07 - loss: -1884.6891"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "2n2ZjLk74b_Y",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"model_json = encoder.to_json()\n",
"with open(os.path.join(model_path, \"encoder.json\"), \"w\") as json_file:\n",
" json_file.write(model_json)\n",
" \n",
"encoder.save_weights(os.path.join(model_path, \"encoder.h5\"))"
],
"execution_count": 0,
"outputs": []
}
]
}
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