From 604f81e82bd260f888df8623286bc6454566e069 Mon Sep 17 00:00:00 2001
From: sing-git <seungju.paek@uzh.ch>
Date: Sun, 8 Mar 2026 21:05:09 +0100
Subject: [PATCH 1/2] Checkpoint from VS Code for cloud agent session

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Group Members:\n",
+    "\n",
+    "- Name, matriculation number\n",
+    "- Name, matriculation number\n",
+    "- Name, matriculation number"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZmubxigEE-Zb"
+   },
+   "source": [
+    "# Assignment 1: Universal Function Approximator\n",
+    "\n",
+    "\n",
+    "The goal of this exercise is to compare three different neural network architectures and analyze their capacity for function approximation:\n",
+    "\n",
+    "1. $N_0$: One-layer network (linear transformation only)\n",
+    "2. $N_1$: One-layer network with non-linear activation function\n",
+    "3. $N_2$: Two-layer network (hidden layer with non-linear activation function)\n",
+    "\n",
+    "They will be trained via gradient descent with weight decay. \n",
+    "To show the flexibility of the approach, three different functions will be approximated:\n",
+    "1. $X_0: t = \\sin(2x)$ for $x\\in[-2,2]$\n",
+    "2. $X_1: t = e^{-x^2}$ for $x\\in[-3,3]$\n",
+    "3. $X_2: t = -x^5 - 3x^4 + 11x^3 +27x^2 - 10x - 32$ for $x\\in[-4.5,3.5]$\n",
+    "\n",
+    "In the theoretical section, the networks will be designed, and the necessary derivatives will be computed by hand.\n",
+    "\n",
+    "In the coding section, we will: \n",
+    "\n",
+    "- implement the networks and their gradients,\n",
+    "- generate target data for three different functions, \n",
+    "- apply the training procedure to the data, and \n",
+    "- plot the resulting approximated function together with the data samples."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "n36PMmPANmJ7"
+   },
+   "source": [
+    "## Section 1: Theoretical Questions"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oXFApo7obLKe"
+   },
+   "source": [
+    "### Network Design"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FH8oL0-MQ0IY"
+   },
+   "source": [
+    "#### Task 1.1: Network Structure\n",
+    "\n",
+    "Given input $\\vec x = (1, x)^T$, define three neural networks ($N_0$, $N_1$, $N_2$) mathematically, to reach output $y$. Use $g()$ to represent the activation function.\n",
+    "\n",
+    "Explain how their structures differ and analyze their function approximation capabilities.\n",
+    "\n",
+    "--- \n",
+    "Note:\n",
+    "\n",
+    "For one-layer networks, define parameter $\\Theta=\\vec w \\in\\mathbb R^{D+1}$\n",
+    "\n",
+    "For two-layer network, define parameters $\\Theta=(\\mathbf W^{(1)},\\vec w^{(2)})$ that are split into $\\mathbf W^{(1)}\\in\\mathbb R^{K\\times {(D+1)}}$ for the first layer and $\\vec w^{(2)}\\in\\mathbb R^{K+1}$ for the second layer"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kujWaTnHTEnk"
+   },
+   "source": [
+    "$N_0$: Only linear forms can be expressed, making complex function representations difficult.\n",
+    "$N_1$: Although nonlinear, it has limited expressiveness due to having only one neuron.\n",
+    "$N_2$: It can approximate complex curves because it uses multiple hidden neurons."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eSm46qR8SdQH"
+   },
+   "source": [
+    "#### Task 1.2: Network Comparison\n",
+    "\n",
+    "Can the one-layer network approximate all three functions well? \n",
+    "Why or why not?\n",
+    "\n",
+    "What advantages does the two-layer network have compared to a one-layer network?\n",
+    "\n",
+    "How can we determine the appropriate number of hidden neurons?\n",
+    "When looking at the example plots in the OLAT, how many hidden neurons do we need in order to approximate the functions? \n",
+    "Is there any difference between the three target functions?"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "N_ZK0aMfafSP"
+   },
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ki1cAn6zSvj2"
+   },
+   "source": [
+    "#### Task 1.3: Network Performance\n",
+    "\n",
+    "If the network struggles to approximate a function well, what are some possible reasons?\n",
+    "\n",
+    "How can we improve the network's performance?\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DP60Na-kbF_1"
+   },
+   "source": [
+    "##### Reasons\n",
+    "The model may be too simple.\n",
+    "The number of hidden neurons may be insufficient.\n",
+    "The learning rate may be too large or too small.\n",
+    "Weight decay may be too strong.\n",
+    "The initial weights may be suboptimal.\n",
+    "\n",
+    "##### Solutions\n",
+    "Use a more powerful model.\n",
+    "Increase the number of hidden neurons.\n",
+    "Adjust the learning rate.\n",
+    "Adjust Weight decay λ appropriately.\n",
+    "Train for longer."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UPUvWGhybtv9"
+   },
+   "source": [
+    "### Derivatives"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "F9ibD4zrPOvE"
+   },
+   "source": [
+    "#### Task 1.4: Activation Function\n",
+    "\n",
+    "Given the hyperbolic tangent ($\\tanh$) activation function as:\n",
+    "\n",
+    "$$\\tanh(x) = \\frac{e^x - e^{-x}}{e^x + e^{-x}}$$\n",
+    "\n",
+    "Prove:\n",
+    "\n",
+    "$$\\frac{\\partial}{\\partial x} \\tanh(x) = 1 - \\tanh^2(x)$$\n",
+    "\n",
+    "Hint: Apply the derivative rules as defined in the Lecture:\n",
+    "* Quotient rule\n",
+    "* Sum rule\n",
+    "* Exponential rule\n",
+    "\n",
+    "Also, avoid factoring out parentheses."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TW6QrQKPUffH"
+   },
+   "source": [
+    "$\\frac{\\partial}{\\partial x} \\tanh(x) =...$"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mC041GO7PzIP"
+   },
+   "source": [
+    "#### Task 1.5: Weight Decay\n",
+    "\n",
+    "Consider a loss function with L2 regularization (weight decay):\n",
+    "$$\n",
+    "\\mathcal J'_\\Theta = \\mathcal J_\\Theta + \\frac{\\lambda}{2} \\|\\Theta\\|^2\n",
+    "$$\n",
+    "\n",
+    "Expecting the gradient of the network $\\nabla \\mathcal J_\\Theta$ to be given, compute the derivative of $\\mathcal J'$ with respect to all $\\Theta_i$: $$\\biggl[\\frac{\\partial}{\\partial \\Theta_i} \\mathcal J'_\\Theta\\biggr]_i$$"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2o4IjaZ4VimR"
+   },
+   "source": [
+    "$\\frac{\\partial}{\\partial \\Theta_i} \\mathcal J'_\\Theta = ...$"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aOiw6bNJQG5E"
+   },
+   "source": [
+    "#### Task 1.6\n",
+    "\n",
+    "How large should an appropriate weight decay parameter $\\lambda$ as shown in Task 1.5 be? What would happen if $\\lambda$ is set too high or too low?"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vErgVANhV9cu"
+   },
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "SY8F0hc3Ttyn"
+   },
+   "source": [
+    "## Section 2: Coding\n",
+    "\n",
+    "**<font color='red' size='5'>This section has to be submitted by 11:59 p.m. on Wednesday, March 11th, to be graded.</font>**\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pLvRiQ-6NRB8"
+   },
+   "source": [
+    "### Network Implementation\n",
+    "#### Task 2.1\n",
+    "\n",
+    "Recall that for one-layer networks, we define parameter $\\Theta=\\vec w \\in\\mathbb R^{D+1}$, and for a two-layer network, we define parameters $\\Theta=(\\mathbf W^{(1)}\\in\\mathbb R^{K\\times {(D+1)}},\\vec w^{(2)}\\in\\mathbb R^{K+1})$.\n",
+    "\n",
+    "- D: The dimension of the input. In this assignment, $D = 1$ since there is only one input.\n",
+    "- K: The number of neurons in the hidden layer of the two-layer network ($N_2$)\n",
+    "\n",
+    "Implement a function that returns the network output for a given input $\\vec x$, model_type ($N_0$, $N_1$, or $N_2$) and parameters $\\Theta$. \n",
+    "Remember that the input of the function $\\vec x = (1, x)^T$.\n",
+    "\n",
+    "---\n",
+    "Note:\n",
+    "\n",
+    "1. Use the `numpy` to implement the $\\tanh$ function.\n",
+    "2. Use `numpy.concatenate` or `numpy.insert` to prepend $h_0$.\n",
+    "3. Make use of `numpy.dot` to compute matrix-vector and vector-vector products."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "4XOSElIwJ5BB"
+   },
+   "outputs": [],
+   "source": [
+    "from math import tanh\n",
+    "\n",
+    "import numpy \n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def network(x, model_type, Theta):\n",
+    "    \"\"\"\n",
+    "    Compute the output of a neural network model.\n",
+    "\n",
+    "    Args:\n",
+    "        x: Input vector (1, x) including bias.\n",
+    "        model_type: Type of model (0, 1, or 2).\n",
+    "        Theta: Tuple of network parameters (W1, w2).\n",
+    "\n",
+    "    Returns:\n",
+    "        y: Network output.\n",
+    "        h: Hidden layer output, or None.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    W1, w2 = Theta # w2 is None if model_type is 1 or 2\n",
+    "    x = numpy.asarray(x, dtype=float)\n",
+    "\n",
+    "\n",
+    "    if model_type == 0:\n",
+    "        # One-layer network (Linear Model)\n",
+    "        y = W1 @ x\n",
+    "        return y, None # To make this consistent when model_type is 3\n",
+    "\n",
+    "    elif model_type == 1:\n",
+    "        # One-layer network with tanh activation\n",
+    "        y = numpy.tanh(W1 @ x)\n",
+    "        return y, None # To make this consistent when model_type is 3\n",
+    "\n",
+    "    elif model_type == 2:\n",
+    "        # Two-layer network with tanh activation\n",
+    "        a_ = W1 @ x\n",
+    "        h_ = numpy.tanh(a_)\n",
+    "        h = numpy.insert(h_, 0, 1) # Add bias term to hidden layer output\n",
+    "        y = w2 @ h\n",
+    "        return y, h"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g5XL_ohAE-Zh"
+   },
+   "source": [
+    "#### Test 1: Sanity Check\n",
+    "\n",
+    "We select a specific number of hidden neurons and create the weights accordingly, using all zeros in the first layer and all ones in the second. The test case below ensures that the function from Task 1 actually returns $11$ for those weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ku3Sy5fzj8YH",
+    "outputId": "45b07ac7-686d-432e-8d09-1342af087687"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N1 test passed.\n",
+      "N2 test passed.\n",
+      "N3 test passed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define test parameters\n",
+    "K_ = 20\n",
+    "D_ = 1\n",
+    "Theta_one_layer = [numpy.ones(D_+1),None]\n",
+    "Theta_two_layer = [numpy.zeros((K_, D_+1)), numpy.ones(K_+1)]\n",
+    "x = numpy.random.rand(D_+1)\n",
+    "\n",
+    "# Sanity check for N1\n",
+    "y1, _ = network(x, 0, Theta_one_layer)\n",
+    "assert abs(numpy.sum(x) - y1) < 1e-6\n",
+    "print(\"N1 test passed.\")\n",
+    "\n",
+    "# Sanity check for N2\n",
+    "y2, _ = network(x, 1, Theta_one_layer)\n",
+    "assert abs(numpy.tanh(numpy.sum(x)) - y2) < 1e-6\n",
+    "print(\"N2 test passed.\")\n",
+    "\n",
+    "# Sanity check for N3\n",
+    "y3, _ = network(x, 2, Theta_two_layer)\n",
+    "assert abs(1.0 - y3) < 1e-6\n",
+    "print(\"N3 test passed.\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-fW7_KzcE-Zi"
+   },
+   "source": [
+    "### Gradient Implementation\n",
+    "\n",
+    "#### Task 2.2: Gradient Computation\n",
+    "\n",
+    "\n",
+    "Implementation of a function that returns the gradient as defined for a given dataset $X=\\{(\\vec x^{[n]}, t^{[n]})\\}$, model_type, parameters $\\Theta$, and the $\\lambda$ parameter for weight decay.\n",
+    "\n",
+    "---\n",
+    "Note:\n",
+    "\n",
+    "You should make sure that both parts of the gradient are computed for $N_2$ (since $\\Theta=(\\mathbf W^{(1)},\\vec w^{(2)})$ here).\n",
+    "\n",
+    "This is a very slow implementation. We will see how to speed this up in the next lecture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "3Vl_dYxcW-VD"
+   },
+   "outputs": [],
+   "source": [
+    "def compute_gradient(X, model_type, Theta, lambda_=1.):\n",
+    "    \"\"\"\n",
+    "    Compute the gradient of the loss function with respect to the weights for each model type.\n",
+    "\n",
+    "    Args:\n",
+    "        X: Dataset containing input-target pairs (x, t).\n",
+    "        model_type: Type of model (0, 1, or 2).\n",
+    "        Theta: Network parameters (W1, w2).\n",
+    "        lambda_: Weight decay parameter. Default is 1.0.\n",
+    "\n",
+    "    Returns:\n",
+    "        Gradients with respect to W1 and w2. For model_type 1 and 2, w2 is None.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # split parameters for easier handling\n",
+    "    W1, w2 = Theta # w2 is None if model_type is 1 or 2\n",
+    "\n",
+    "    # define gradient with respect to both parameters\n",
+    "    dW1 = numpy.zeros_like(W1)\n",
+    "    dw2 = None if w2 is None else numpy.zeros_like(w2) # dw2 is None if model_type is 1 or 2\n",
+    "\n",
+    "    # iterate over dataset\n",
+    "    for x, t in X:\n",
+    "        # get network output\n",
+    "        y, h = network(x, model_type, (W1, w2))\n",
+    "        err = y - t\n",
+    "\n",
+    "        # compute and aggregate the gradient, based on the network type\n",
+    "        if model_type == 0:\n",
+    "            dW1 += err * x\n",
+    "\n",
+    "        elif model_type == 1:\n",
+    "            dW1 += err * (1 - y**2) * x # derivative of tanh is 1 - tanh^2\n",
+    "\n",
+    "        elif model_type == 2:\n",
+    "            dw2 += err * h\n",
+    "            dW1 += (w2[1:].T * err * (1 - h[1:]**2))[:, None] * x # chain rule for two-layer network\n",
+    "\n",
+    "    # Add penalty term/weight decay\n",
+    "    N = len(X)\n",
+    "    dW1 = dW1 / N + lambda_ * W1\n",
+    "\n",
+    "    # anything else?\n",
+    "\n",
+    "    if model_type == 2:\n",
+    "        dw2 = dw2 / N + lambda_ * w2\n",
+    "\n",
+    "    return dW1, dw2\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FqAMeoy5E-aG"
+   },
+   "source": [
+    "#### Task 2.3: Gradient Descent\n",
+    "\n",
+    "The procedure of gradient descent is the repeated application of two steps.\n",
+    "\n",
+    "1. The gradient of loss $\\nabla_{\\Theta}\\mathcal J^{L_2}$ is computed based on the current value of the parameters $\\Theta$.\n",
+    "2. The weights are updated by moving a small step in the direction of the negative gradient:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "    \\Theta = \\Theta - \\eta \\nabla_{\\Theta}\\mathcal J\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "As a stopping criterion, we select the number of training epochs to be 10000.\n",
+    "\n",
+    "Implementation of a function that performs gradient descent for a given dataset $X$, given initial parameters $\\Theta$, a given learning rate $\\eta$, model_type ($N_1$, $N_2$, or $N_3$), and $\\lambda$ parameter for weight decay, and returns the optimized parameters $\\Theta^*$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "Hx6Jjs2e2CFX"
+   },
+   "outputs": [],
+   "source": [
+    "def gradient_descent(X, model_type, Theta, eta, lambda_=1., epochs=10000):\n",
+    "    \"\"\"\n",
+    "    Perform gradient decent for each model type.\n",
+    "\n",
+    "    Args:\n",
+    "        X: Dataset containing input-target pairs (x, t).\n",
+    "        model_type: Type of model (0, 1, or 2).\n",
+    "        Theta: Network parameters (W1, w2).\n",
+    "        eta: Learning rate.\n",
+    "        lambda_: Weight decay parameter. Default is 1.0.\n",
+    "        epochs: The number of epochs to train for\n",
+    "\n",
+    "    Returns:\n",
+    "        The optimized model parameters\n",
+    "    \"\"\"\n",
+    "    W1 = numpy.array(Theta[0], dtype=float)\n",
+    "    w2 = None if Theta[1] is None else numpy.array(Theta[1], dtype=float)\n",
+    "\n",
+    "\n",
+    "    # perform iterative gradient descent\n",
+    "    for _ in range(epochs):\n",
+    "        # compute the gradient\n",
+    "        dW1, dw2 = compute_gradient(X, model_type, (W1, w2), lambda_)\n",
+    "\n",
+    "\n",
+    "        # update the parameters\n",
+    "        W1 = W1 - eta * dW1\n",
+    "        if model_type == 2:\n",
+    "            w2 = w2 - eta * dw2\n",
+    "\n",
+    "    # return optimized parameters\n",
+    "\n",
+    "\n",
+    "        return [W1, w2]\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5gEeh7N8E-aH"
+   },
+   "source": [
+    "### Datasets\n",
+    "\n",
+    "#### Task 2.4: Data Samples\n",
+    "\n",
+    "In total, we will test our gradient descent function with three different datasets. Particularly, we approximate\n",
+    "\n",
+    "1. $X_0: t = \\sin(2x)$ for $x\\in[-2,2]$\n",
+    "2. $X_1: t = e^{-x^2}$ for $x\\in[-3,3]$\n",
+    "3. $X_2: t = -x^5 - 3x^4 + 11x^3 +27x^2 - 10x - 32$ for $x\\in[-4.5,3.5]$\n",
+    "\n",
+    "Generate dataset $X_0$ for $N=45$ samples randomly drawn from range $x\\in[-2,2]$. \n",
+    "Generate dataset $X_1$ for $N=15$ samples randomly drawn from range $x\\in[-3,3]$. \n",
+    "Generate dataset $X_2$ for $N=250$ samples randomly drawn from range $x\\in[-4.5,3.5]$. \n",
+    "Implement all three datasets as lists of tuples: $\\{(\\vec x^{[n]}, t^{[n]})\\mid 1\\leq n\\leq N\\}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "1DdPBymdcNXx"
+   },
+   "outputs": [],
+   "source": [
+    "#X0 = [...]\n",
+    "#X1 = [...]\n",
+    "#X2 = [...]\n",
+    "\n",
+    "\n",
+    "rng = numpy.random.default_rng()\n",
+    "\n",
+    "# X0\n",
+    "X0 = []\n",
+    "for x in rng.uniform(-2,2,45):\n",
+    "    t = numpy.sin(2*x)\n",
+    "    X0.append((numpy.array([1,x]), t))\n",
+    "\n",
+    "\n",
+    "# X1\n",
+    "X1 = []\n",
+    "for x in rng.uniform(-3,3,15):\n",
+    "    t = numpy.exp(-x**2)\n",
+    "    X1.append((numpy.array([1,x]), t))\n",
+    "\n",
+    "\n",
+    "# X2\n",
+    "X2 = []\n",
+    "for x in rng.uniform(-4.5,3.5,250):\n",
+    "    t = -x**5 - 3*x**4 + 11*x**3 +27*x**2 -10*x -32\n",
+    "    X2.append((numpy.array([1,x]), t))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6tGZqaiUE-aH"
+   },
+   "source": [
+    "#### Test 2: Sanity Check\n",
+    "\n",
+    "The test case below ensures that the elements of each generated dataset are tuples with two dimensions.\n",
+    "It does not test that the values are correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WrneyBJLE-aI",
+    "outputId": "96be9dcb-0709-45d3-b209-5a226b429045"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test passed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, X in enumerate((X0, X1, X2)):\n",
+    "    assert all(\n",
+    "        isinstance(x, (tuple, list)) and\n",
+    "        len(x) == 2 and\n",
+    "        isinstance(x[0], (tuple, list, numpy.ndarray)) and\n",
+    "        len(x[0]) == 2 and\n",
+    "        isinstance(x[1], (float, numpy.float32, numpy.float64))\n",
+    "        for x in X\n",
+    "    ), f\"Test failed for dataset X{i}\"\n",
+    "\n",
+    "print('Test passed!')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "v0p9mC4YE-aI"
+   },
+   "source": [
+    "### Function Approximation\n",
+    "Finally, we want to make use of our gradient descent implementation to approximate our functions. In order to see our success, we want to plot the functions together with the data.\n",
+    "\n",
+    "#### Task 2.5: Define Hidden Neurons\n",
+    "How many hidden neurons will we need for $N_2$? Use the answers from Task 1.2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "id": "OLichgq7cNXy"
+   },
+   "outputs": [],
+   "source": [
+    "# Define the number of neurons for each target function based on your discussion\n",
+    "K0 = 8\n",
+    "K1 = 6\n",
+    "K2 = 20"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1fvx31eyE-aI"
+   },
+   "source": [
+    "#### Task 2.6: Random Parameters\n",
+    "\n",
+    "For each of the networks, randomly initialize the parameters $\\Theta_0,\\Theta_1,\\Theta_2\\in[-1,1]$ for each of the datasets.\n",
+    "\n",
+    "For $N_2$, use the number of hidden neurons estimated in Task 1.2 and implemented in Task 2.5.\n",
+    "\n",
+    "---\n",
+    "Note:\n",
+    "\n",
+    "  1. You can use `numpy.random.uniform` to initialize the weights.\n",
+    "  2. Make sure that the weight matrices are instantiated in the correct dimensions.\n",
+    "  3. Theta should always have two elements. The second element can be `None` for one-layer networks.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "Aq768_chcNXy"
+   },
+   "outputs": [],
+   "source": [
+    "D=1\n",
+    "import numpy as np\n",
+    "rng = np.random.default_rng(1)\n",
+    "\n",
+    "# Initialize weights for One-Layer Network (No Activation), once for each dataset\n",
+    "Thetas_N0 = [\n",
+    "    [rng.uniform(-1, 1, size=D+1), None],\n",
+    "    [rng.uniform(-1, 1, size=D+1), None],\n",
+    "    [rng.uniform(-1, 1, size=D+1), None]\n",
+    "]\n",
+    "\n",
+    "# Initialize weights for One-Layer Network (With Activation)\n",
+    "Thetas_N1 = [\n",
+    "    [rng.uniform(-1, 1, size=D+1), None],\n",
+    "    [rng.uniform(-1, 1, size=D+1), None],\n",
+    "    [rng.uniform(-1, 1, size=D+1), None]\n",
+    "]\n",
+    "\n",
+    "# Initialize weights for Two-Layer Network\n",
+    "Thetas_N2 = [\n",
+    "    [rng.uniform(-1, 1, size=(K0, D+1)), rng.uniform(-1, 1, size=K0+1)],\n",
+    "    [rng.uniform(-1, 1, size=(K1, D+1)), rng.uniform(-1, 1, size=K1+1)],\n",
+    "    [rng.uniform(-1, 1, size=(K2, D+1)), rng.uniform(-1, 1, size=K2+1)]\n",
+    "]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1jCM2nyRE-aI"
+   },
+   "source": [
+    "#### Task 2.7: Run Gradient Descent\n",
+    "\n",
+    "For each network, call gradient descent function from Task 2.3 using the datasets $X_0, X_1, X_2$, the according created parameters $\\Theta_0,\\Theta_1,\\Theta_2$.\n",
+    "Store the resulting optimized weights $\\Theta_0^*, \\Theta_1^*, \\Theta_2^*$.\n",
+    "\n",
+    "Based on your chosen learning rates $\\eta$ and weight decay parameter $\\lambda$, you may need to optimize them for these functions. \n",
+    "Do you see any differences? \n",
+    "What are the best learning rates that you can find?\n",
+    "\n",
+    "---\n",
+    "<span style=\"color:red\">WARNING: Depending on the implementation, this might run for several minutes!</span>\n",
+    "\n",
+    "---\n",
+    "Hints:\n",
+    "\n",
+    "1. Start with $\\eta=0.1$ and play around with the learning rate improve adaptation.\n",
+    "2. $\\eta=0.1$ might be too large for $X_2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "I4sjPUH_cNXz"
+   },
+   "outputs": [],
+   "source": [
+    "# Call gradient descent function using the datasets and initial weights that you created above\n",
+    "# Choose appropriate learning rates for each function\n",
+    "\n",
+    "lambda_ = 1e-4\n",
+    "\n",
+    "# N0\n",
+    "Theta0_X0 = gradient_descent(X0, 0, Thetas_N0[0], eta=0.01,  lambda_=lambda_, epochs=10000)\n",
+    "Theta0_X1 = gradient_descent(X1, 0, Thetas_N0[1], eta=0.01,  lambda_=lambda_, epochs=10000)\n",
+    "Theta0_X2 = gradient_descent(X2, 0, Thetas_N0[2], eta=1e-6,  lambda_=lambda_, epochs=10000)\n",
+    "\n",
+    "# N1\n",
+    "Theta1_X0 = gradient_descent(X0, 1, Thetas_N1[0], eta=0.01,  lambda_=lambda_, epochs=10000)\n",
+    "Theta1_X1 = gradient_descent(X1, 1, Thetas_N1[1], eta=0.01,  lambda_=lambda_, epochs=10000)\n",
+    "Theta1_X2 = gradient_descent(X2, 1, Thetas_N1[2], eta=1e-6,  lambda_=lambda_, epochs=10000)\n",
+    "\n",
+    "# N2\n",
+    "Theta2_X0 = gradient_descent(X0, 2, Thetas_N2[0], eta=0.01,  lambda_=lambda_, epochs=10000)\n",
+    "Theta2_X1 = gradient_descent(X1, 2, Thetas_N2[1], eta=0.01,  lambda_=lambda_, epochs=10000)\n",
+    "Theta2_X2 = gradient_descent(X2, 2, Thetas_N2[2], eta=1e-6,  lambda_=lambda_, epochs=10000)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y8_C3TseE-aJ"
+   },
+   "source": [
+    "### Data and Function Plotting\n",
+    "\n",
+    "### Task 2.8: Plotting Function\n",
+    "\n",
+    "Implement a plotting function that takes a given dataset $X$, model_type, parameters $\\Theta$, and a defined range $R=[\\min,\\max]$. \n",
+    "Each data sample $(\\vec x^{[n]}, t^{[n]})$ of the dataset is plotted as an ''x''. \n",
+    "In order to plot the function that is approximated by the network, generate sufficient equally-spaced input values $x\\in R$, compute the network output $y$ for these inputs, and plot them with a line.\n",
+    "\n",
+    "---\n",
+    "Hints:\n",
+    "\n",
+    "  1. The dataset $X$ is defined as above, a list of tuples $(\\vec x, t)$.\n",
+    "  2. Each input in the dataset is defined as $\\vec x = (1,x)^T$.\n",
+    "  3. Equidistant points can be obtained via `numpy.arange`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "6sII26VJcNXz"
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot\n",
+    "def plot(X, model_type, Theta, R):\n",
+    "    \"\"\"\n",
+    "    Plots the data and the network output into one plot\n",
+    "\n",
+    "    Args:\n",
+    "        X: Dataset containing input-target pairs (x, t).\n",
+    "        model_type: Type of model (0, 1, or 2).\n",
+    "        Theta: Network parameters (W1, w2).\n",
+    "        R: The range for which we plot the model output\n",
+    "\n",
+    "    Returns:\n",
+    "        The optimized model parameters\n",
+    "    \"\"\"\n",
+    "    # first, plot data samples\n",
+    "    xs_data = [sample[0][1] for sample in X]\n",
+    "    ts_data = [sample[1] for sample in X]\n",
+    "    pyplot.plot(xs_data, ts_data, \"rx\", label=\"Data\")\n",
+    "\n",
+    "\n",
+    "    # define equidistant points from min (R[0]) to max (R[1]) to evaluate the network\n",
+    "    # equally spaced x values\n",
+    "    x = np.arange(R[0], R[1], 0.01)\n",
+    "    \n",
+    "    # model outputs\n",
+    "    y = [network(np.array([1.0, xi]), model_type, Theta)[0] for xi in x]\n",
+    "\n",
+    "\n",
+    "    # plot network approximation\n",
+    "    pyplot.plot(x, y, \"k-\", label=\"Network\")\n",
+    "    pyplot.legend()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YXcO5e-sE-aJ"
+   },
+   "source": [
+    "#### Task 2.9: Plot Three Functions for three datasets\n",
+    "\n",
+    "For each of the datasets and for each network type including their according optimized parameters, call the plotting function from Task 2.8. \n",
+    "Use range $R=[-3,3]$ for dataset $X_0$, range $R=[-4,4]$ for $X_1$, and range $R=[-6,5]$ for dataset $X_2$.\n",
+    "\n",
+    "Note that the first element of range $R$ should be the lowest $x$-location, and the second element of $R$ the highest value for $x$.\n",
+    "\n",
+    "Repeat for three networks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "id": "CY3YGkXgcNXz"
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1500x900 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create one large figure\n",
+    "figure = pyplot.figure(figsize=(15,9))\n",
+    "trained_thetas = [\n",
+    "    [Theta0_X0, Theta0_X1, Theta0_X2],   # model_type 0\n",
+    "    [Theta1_X0, Theta1_X1, Theta1_X2],   # model_type 1\n",
+    "    [Theta2_X0, Theta2_X1, Theta2_X2]    # model_type 2\n",
+    "]\n",
+    "\n",
+    "ranges = [\n",
+    "    [-3, 3],   # for X0\n",
+    "    [-4, 4],   # for X1\n",
+    "    [-6, 5]    # for X2\n",
+    "]\n",
+    "\n",
+    "# iterate over the model types\n",
+    "for model_type in range(3):\n",
+    "    # iterate over the datasets\n",
+    "    for i, X in enumerate([X0, X1, X2]):\n",
+    "        # activate correct plot\n",
+    "        pyplot.subplot(3, 3, 3*model_type+i+1)\n",
+    "        # call the plot function with the correct dataset with their ranges, and model type with their trained Theta parameters\n",
+    "        plot(X, model_type, trained_thetas[model_type][i], ranges[i])\n",
+    "        pyplot.title(f\"N{model_type} on X{i}\")\n",
+    "        \n",
+    "pyplot.tight_layout()\n",
+    "pyplot.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/DL-Assignment01_Samples.pdf b/DL-Assignment01_Samples.pdf
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From b7c2a2942c77f3af532280761f7ac86947ed6743 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Sun, 8 Mar 2026 20:07:18 +0000
Subject: [PATCH 2/2] Fix gradient_descent: move return statement outside for
 loop so all epochs run

Co-authored-by: sing-git <183478851+sing-git@users.noreply.github.com>
---
 DL-Assignment01.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/DL-Assignment01.ipynb b/DL-Assignment01.ipynb
index 586fbcc..5051d3f 100644
--- a/DL-Assignment01.ipynb
+++ b/DL-Assignment01.ipynb
@@ -157,7 +157,7 @@
     "Use a more powerful model.\n",
     "Increase the number of hidden neurons.\n",
     "Adjust the learning rate.\n",
-    "Adjust Weight decay λ appropriately.\n",
+    "Adjust Weight decay \u03bb appropriately.\n",
     "Train for longer."
    ]
   },
@@ -548,7 +548,7 @@
     "    # return optimized parameters\n",
     "\n",
     "\n",
-    "        return [W1, w2]\n"
+    "    return [W1, w2]\n"
    ]
   },
   {
@@ -947,4 +947,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file