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Initialization.json
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Initialization.json
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Initialization\n",
"\n",
"Welcome to the first assignment of Improving Deep Neural Networks!\n",
"\n",
"Training your neural network requires specifying an initial value of the weights. A well-chosen initialization method helps the learning process.\n",
"\n",
"If you completed the previous course of this specialization, you probably followed the instructions for weight initialization, and seen that it's worked pretty well so far. But how do you choose the initialization for a new neural network? In this notebook, you'll try out a few different initializations, including random, zeros, and He initialization, and see how each leads to different results.\n",
"\n",
"A well-chosen initialization can:\n",
"- Speed up the convergence of gradient descent\n",
"- Increase the odds of gradient descent converging to a lower training (and generalization) error \n",
"\n",
"Let's get started!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of Contents\n",
"- [1 - Packages](#1)\n",
"- [2 - Loading the Dataset](#2)\n",
"- [3 - Neural Network Model](#3)\n",
"- [4 - Zero Initialization](#4)\n",
" - [Exercise 1 - initialize_parameters_zeros](#ex-1)\n",
"- [5 - Random Initialization](#5)\n",
" - [Exercise 2 - initialize_parameters_random](#ex-2)\n",
"- [6 - He Initialization](#6)\n",
" - [Exercise 3 - initialize_parameters_he](#ex-3)\n",
"- [7 - Conclusions](#7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name='1'></a>\n",
"## 1 - Packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import sklearn\n",
"import sklearn.datasets\n",
"from public_tests import *\n",
"from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation\n",
"from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec\n",
"\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"# load image dataset: blue/red dots in circles\n",
"# train_X, train_Y, test_X, test_Y = load_dataset()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name='2'></a>\n",
"## 2 - Loading the Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 504x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"train_X, train_Y, test_X, test_Y = load_dataset()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this classifier, you want to separate the blue dots from the red dots."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name='3'></a>\n",
"## 3 - Neural Network Model "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You'll use a 3-layer neural network (already implemented for you). These are the initialization methods you'll experiment with: \n",
"- *Zeros initialization* -- setting `initialization = \"zeros\"` in the input argument.\n",
"- *Random initialization* -- setting `initialization = \"random\"` in the input argument. This initializes the weights to large random values. \n",
"- *He initialization* -- setting `initialization = \"he\"` in the input argument. This initializes the weights to random values scaled according to a paper by He et al., 2015. \n",
"\n",
"**Instructions**: Instructions: Read over the code below, and run it. In the next part, you'll implement the three initialization methods that this `model()` calls."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def model(X, Y, learning_rate = 0.01, num_iterations = 15000, print_cost = True, initialization = \"he\"):\n",
" \"\"\"\n",
" Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n",
" \n",
" Arguments:\n",
" X -- input data, of shape (2, number of examples)\n",
" Y -- true \"label\" vector (containing 0 for red dots; 1 for blue dots), of shape (1, number of examples)\n",
" learning_rate -- learning rate for gradient descent \n",
" num_iterations -- number of iterations to run gradient descent\n",
" print_cost -- if True, print the cost every 1000 iterations\n",
" initialization -- flag to choose which initialization to use (\"zeros\",\"random\" or \"he\")\n",
" \n",
" Returns:\n",
" parameters -- parameters learnt by the model\n",
" \"\"\"\n",
" \n",
" grads = {}\n",
" costs = [] # to keep track of the loss\n",
" m = X.shape[1] # number of examples\n",
" layers_dims = [X.shape[0], 10, 5, 1]\n",
" \n",
" # Initialize parameters dictionary.\n",
" if initialization == \"zeros\":\n",
" parameters = initialize_parameters_zeros(layers_dims)\n",
" elif initialization == \"random\":\n",
" parameters = initialize_parameters_random(layers_dims)\n",
" elif initialization == \"he\":\n",
" parameters = initialize_parameters_he(layers_dims)\n",
"\n",
" # Loop (gradient descent)\n",
"\n",
" for i in range(num_iterations):\n",
"\n",
" # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n",
" a3, cache = forward_propagation(X, parameters)\n",
" \n",
" # Loss\n",
" cost = compute_loss(a3, Y)\n",
"\n",
" # Backward propagation.\n",
" grads = backward_propagation(X, Y, cache)\n",
" \n",
" # Update parameters.\n",
" parameters = update_parameters(parameters, grads, learning_rate)\n",
" \n",
" # Print the loss every 1000 iterations\n",
" if print_cost and i % 1000 == 0:\n",
" print(\"Cost after iteration {}: {}\".format(i, cost))\n",
" costs.append(cost)\n",
" \n",
" # plot the loss\n",
" plt.plot(costs)\n",
" plt.ylabel('cost')\n",
" plt.xlabel('iterations (per hundreds)')\n",
" plt.title(\"Learning rate =\" + str(learning_rate))\n",
" plt.show()\n",
" \n",
" return parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name='4'></a>\n",
"## 4 - Zero Initialization\n",
"\n",
"There are two types of parameters to initialize in a neural network:\n",
"- the weight matrices $(W^{[1]}, W^{[2]}, W^{[3]}, ..., W^{[L-1]}, W^{[L]})$\n",
"- the bias vectors $(b^{[1]}, b^{[2]}, b^{[3]}, ..., b^{[L-1]}, b^{[L]})$\n",
"\n",
"<a name='ex-1'></a>\n",
"### Exercise 1 - initialize_parameters_zeros\n",
"\n",
"Implement the following function to initialize all parameters to zeros. You'll see later that this does not work well since it fails to \"break symmetry,\" but try it anyway and see what happens. Use `np.zeros((..,..))` with the correct shapes."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"deletable": false,
"nbgrader": {
"cell_type": "code",
"checksum": "27eb20f17301310c34489a2e99dccb72",
"grade": false,
"grade_id": "cell-0ebbf9140df0c623",
"locked": false,
"schema_version": 3,
"solution": true,
"task": false
}
},
"outputs": [],
"source": [
"# GRADED FUNCTION: initialize_parameters_zeros \n",
"\n",
"def initialize_parameters_zeros(layers_dims):\n",
" \"\"\"\n",
" Arguments:\n",
" layer_dims -- python array (list) containing the size of each layer.\n",
" \n",
" Returns:\n",
" parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
" W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
" b1 -- bias vector of shape (layers_dims[1], 1)\n",
" ...\n",
" WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
" bL -- bias vector of shape (layers_dims[L], 1)\n",
" \"\"\"\n",
" \n",
" parameters = {}\n",
" L = len(layers_dims) # number of layers in the network\n",
" \n",
" for l in range(1, L):\n",
" #(≈ 2 lines of code)\n",
" # parameters['W' + str(l)] = \n",
" # parameters['b' + str(l)] = \n",
" # YOUR CODE STARTS HERE\n",
" parameters['W' + str(l)] = np.zeros((layers_dims[l],layers_dims[l-1]))\n",
" parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
" \n",
" # YOUR CODE ENDS HERE\n",
" return parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"deletable": false,
"editable": false,
"nbgrader": {
"cell_type": "code",
"checksum": "3f658c06a0a076ada919152a16148743",
"grade": true,
"grade_id": "cell-cca4e25452117a41",
"locked": true,
"points": 10,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"W1 = [[0. 0. 0.]\n",
" [0. 0. 0.]]\n",
"b1 = [[0.]\n",
" [0.]]\n",
"W2 = [[0. 0.]]\n",
"b2 = [[0.]]\n",
"\u001b[92m All tests passed.\n"
]
}
],
"source": [
"parameters = initialize_parameters_zeros([3, 2, 1])\n",
"print(\"W1 = \" + str(parameters[\"W1\"]))\n",
"print(\"b1 = \" + str(parameters[\"b1\"]))\n",
"print(\"W2 = \" + str(parameters[\"W2\"]))\n",
"print(\"b2 = \" + str(parameters[\"b2\"]))\n",
"initialize_parameters_zeros_test(initialize_parameters_zeros)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the following code to train your model on 15,000 iterations using zeros initialization."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cost after iteration 0: 0.6931471805599453\n",
"Cost after iteration 1000: 0.6931471805599453\n",
"Cost after iteration 2000: 0.6931471805599453\n",
"Cost after iteration 3000: 0.6931471805599453\n",
"Cost after iteration 4000: 0.6931471805599453\n",
"Cost after iteration 5000: 0.6931471805599453\n",
"Cost after iteration 6000: 0.6931471805599453\n",
"Cost after iteration 7000: 0.6931471805599453\n",
"Cost after iteration 8000: 0.6931471805599453\n",
"Cost after iteration 9000: 0.6931471805599453\n",
"Cost after iteration 10000: 0.6931471805599455\n",
"Cost after iteration 11000: 0.6931471805599453\n",
"Cost after iteration 12000: 0.6931471805599453\n",
"Cost after iteration 13000: 0.6931471805599453\n",
"Cost after iteration 14000: 0.6931471805599453\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"On the train set:\n",
"Accuracy: 0.5\n",
"On the test set:\n",
"Accuracy: 0.5\n"
]
}
],
"source": [
"parameters = model(train_X, train_Y, initialization = \"zeros\")\n",
"print (\"On the train set:\")\n",
"predictions_train = predict(train_X, train_Y, parameters)\n",
"print (\"On the test set:\")\n",
"predictions_test = predict(test_X, test_Y, parameters)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The performance is terrible, the cost doesn't decrease, and the algorithm performs no better than random guessing. Why? Take a look at the details of the predictions and the decision boundary:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predictions_train = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0]]\n",
"predictions_test = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
]
}
],
"source": [
"print (\"predictions_train = \" + str(predictions_train))\n",
"print (\"predictions_test = \" + str(predictions_test))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"Model with Zeros initialization\")\n",
"axes = plt.gca()\n",
"axes.set_xlim([-1.5,1.5])\n",
"axes.set_ylim([-1.5,1.5])\n",
"plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Note__: For sake of simplicity calculations below are done using only one example at a time.\n",
"\n",
"Since the weights and biases are zero, multiplying by the weights creates the zero vector which gives 0 when the activation function is ReLU. As `z = 0`\n",
"\n",
"$$a = ReLU(z) = max(0, z) = 0$$\n",
"\n",
"At the classification layer, where the activation function is sigmoid you then get (for either input): \n",
"\n",
"$$\\sigma(z) = \\frac{1}{ 1 + e^{-(z)}} = \\frac{1}{2} = y_{pred}$$\n",
"\n",
"As for every example you are getting a 0.5 chance of it being true our cost function becomes helpless in adjusting the weights.\n",
"\n",
"Your loss function:\n",
"$$ \\mathcal{L}(a, y) = - y \\ln(y_{pred}) - (1-y) \\ln(1-y_{pred})$$\n",
"\n",
"For `y=1`, `y_pred=0.5` it becomes:\n",
"\n",
"$$ \\mathcal{L}(0, 1) = - (1) \\ln(\\frac{1}{2}) = 0.6931471805599453$$\n",
"\n",
"For `y=0`, `y_pred=0.5` it becomes:\n",
"\n",
"$$ \\mathcal{L}(0, 0) = - (1) \\ln(\\frac{1}{2}) = 0.6931471805599453$$\n",
"\n",
"As you can see with the prediction being 0.5 whether the actual (`y`) value is 1 or 0 you get the same loss value for both, so none of the weights get adjusted and you are stuck with the same old value of the weights. \n",
"\n",
"This is why you can see that the model is predicting 0 for every example! No wonder it's doing so badly.\n",
"\n",
"In general, initializing all the weights to zero results in the network failing to break symmetry. This means that every neuron in each layer will learn the same thing, so you might as well be training a neural network with $n^{[l]}=1$ for every layer. This way, the network is no more powerful than a linear classifier like logistic regression. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<font color='blue'>\n",
" \n",
"**What you should remember**:\n",
"- The weights $W^{[l]}$ should be initialized randomly to break symmetry. \n",
"- However, it's okay to initialize the biases $b^{[l]}$ to zeros. Symmetry is still broken so long as $W^{[l]}$ is initialized randomly. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name='5'></a>\n",
"## 5 - Random Initialization\n",
"\n",
"To break symmetry, initialize the weights randomly. Following random initialization, each neuron can then proceed to learn a different function of its inputs. In this exercise, you'll see what happens when the weights are initialized randomly, but to very large values.\n",
"\n",
"<a name='ex-2'></a>\n",
"### Exercise 2 - initialize_parameters_random\n",
"\n",
"Implement the following function to initialize your weights to large random values (scaled by \\*10) and your biases to zeros. Use `np.random.randn(..,..) * 10` for weights and `np.zeros((.., ..))` for biases. You're using a fixed `np.random.seed(..)` to make sure your \"random\" weights match ours, so don't worry if running your code several times always gives you the same initial values for the parameters. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"deletable": false,
"nbgrader": {
"cell_type": "code",
"checksum": "21b040c1991d62855342338b0213efaf",
"grade": false,
"grade_id": "cell-b111fbe746a03ac8",
"locked": false,
"schema_version": 3,
"solution": true,
"task": false
}
},
"outputs": [],
"source": [
"# GRADED FUNCTION: initialize_parameters_random\n",
"\n",
"def initialize_parameters_random(layers_dims):\n",
" \"\"\"\n",
" Arguments:\n",
" layer_dims -- python array (list) containing the size of each layer.\n",
" \n",
" Returns:\n",
" parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
" W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
" b1 -- bias vector of shape (layers_dims[1], 1)\n",
" ...\n",
" WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
" bL -- bias vector of shape (layers_dims[L], 1)\n",
" \"\"\"\n",
" \n",
" np.random.seed(3) # This seed makes sure your \"random\" numbers will be the as ours\n",
" parameters = {}\n",
" L = len(layers_dims) # integer representing the number of layers\n",
" \n",
" for l in range(1, L):\n",
" #(≈ 2 lines of code)\n",
" # parameters['W' + str(l)] = \n",
" # parameters['b' + str(l)] =\n",
" # YOUR CODE STARTS HERE\n",
" parameters['W' + str(l)] = np.random.randn(layers_dims[l],layers_dims[l-1])*10\n",
" parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
" \n",
" # YOUR CODE ENDS HERE\n",
"\n",
" return parameters"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"deletable": false,
"editable": false,
"nbgrader": {
"cell_type": "code",
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"grade": true,
"grade_id": "cell-f5d0f829aa0eb6ff",
"locked": true,
"points": 10,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"W1 = [[ 17.88628473 4.36509851 0.96497468]\n",
" [-18.63492703 -2.77388203 -3.54758979]]\n",
"b1 = [[0.]\n",
" [0.]]\n",
"W2 = [[-0.82741481 -6.27000677]]\n",
"b2 = [[0.]]\n",
"\u001b[92m All tests passed.\n"
]
}
],
"source": [
"parameters = initialize_parameters_random([3, 2, 1])\n",
"print(\"W1 = \" + str(parameters[\"W1\"]))\n",
"print(\"b1 = \" + str(parameters[\"b1\"]))\n",
"print(\"W2 = \" + str(parameters[\"W2\"]))\n",
"print(\"b2 = \" + str(parameters[\"b2\"]))\n",
"initialize_parameters_random_test(initialize_parameters_random)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the following code to train your model on 15,000 iterations using random initialization."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cost after iteration 0: inf\n",
"Cost after iteration 1000: 0.6247924745506072\n",
"Cost after iteration 2000: 0.5980258056061102\n",
"Cost after iteration 3000: 0.5637539062842213\n",
"Cost after iteration 4000: 0.5501256393526495\n",
"Cost after iteration 5000: 0.5443826306793814\n",
"Cost after iteration 6000: 0.5373895855049121\n",
"Cost after iteration 7000: 0.47157999220550006\n",
"Cost after iteration 8000: 0.39770475516243037\n",
"Cost after iteration 9000: 0.3934560146692851\n",
"Cost after iteration 10000: 0.3920227137490125\n",
"Cost after iteration 11000: 0.38913700035966736\n",
"Cost after iteration 12000: 0.3861358766546214\n",
"Cost after iteration 13000: 0.38497629552893475\n",
"Cost after iteration 14000: 0.38276694641706693\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"On the train set:\n",
"Accuracy: 0.83\n",
"On the test set:\n",
"Accuracy: 0.86\n"
]
}
],
"source": [
"parameters = model(train_X, train_Y, initialization = \"random\")\n",
"print (\"On the train set:\")\n",
"predictions_train = predict(train_X, train_Y, parameters)\n",
"print (\"On the test set:\")\n",
"predictions_test = predict(test_X, test_Y, parameters)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you see \"inf\" as the cost after the iteration 0, this is because of numerical roundoff. A more numerically sophisticated implementation would fix this, but for the purposes of this notebook, it isn't really worth worrying about.\n",
"\n",
"In any case, you've now broken the symmetry, and this gives noticeably better accuracy than before. The model is no longer outputting all 0s. Progress!"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 0 1 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 1\n",
" 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0\n",
" 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0\n",
" 1 0 1 1 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0\n",
" 0 0 1 0 1 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1\n",
" 1 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 1\n",
" 0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 1 1 0 1 1\n",
" 1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1\n",
" 1 1 1 1 0 0 0 1 1 1 1 0]]\n",
"[[1 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 1\n",
" 0 1 1 0 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0\n",
" 1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0]]\n"
]
}
],
"source": [
"print (predictions_train)\n",
"print (predictions_test)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"Model with large random initialization\")\n",
"axes = plt.gca()\n",
"axes.set_xlim([-1.5,1.5])\n",
"axes.set_ylim([-1.5,1.5])\n",
"plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Observations**:\n",
"- The cost starts very high. This is because with large random-valued weights, the last activation (sigmoid) outputs results that are very close to 0 or 1 for some examples, and when it gets that example wrong it incurs a very high loss for that example. Indeed, when $\\log(a^{[3]}) = \\log(0)$, the loss goes to infinity.\n",
"- Poor initialization can lead to vanishing/exploding gradients, which also slows down the optimization algorithm. \n",
"- If you train this network longer you will see better results, but initializing with overly large random numbers slows down the optimization.\n",
"\n",
"<font color='blue'>\n",
" \n",
"**In summary**:\n",
"- Initializing weights to very large random values doesn't work well. \n",
"- Initializing with small random values should do better. The important question is, how small should be these random values be? Let's find out up next!\n",
"\n",
"<font color='black'> \n",
" \n",
"**Optional Read:**\n",
"\n",
"\n",
"The main difference between Gaussian variable (`numpy.random.randn()`) and uniform random variable is the distribution of the generated random numbers:\n",
"\n",
"- numpy.random.rand() produces numbers in a [uniform distribution](https://raw.githubusercontent.com/jahnog/deeplearning-notes/master/Course2/images/rand.jpg).\n",
"- and numpy.random.randn() produces numbers in a [normal distribution](https://raw.githubusercontent.com/jahnog/deeplearning-notes/master/Course2/images/randn.jpg).\n",
"\n",
"When used for weight initialization, randn() helps most the weights to Avoid being close to the extremes, allocating most of them in the center of the range.\n",
"\n",
"An intuitive way to see it is, for example, if you take the [sigmoid() activation function](https://raw.githubusercontent.com/jahnog/deeplearning-notes/master/Course2/images/sigmoid.jpg).\n",
"\n",
"You’ll remember that the slope near 0 or near 1 is extremely small, so the weights near those extremes will converge much more slowly to the solution, and having most of them near the center will speed the convergence."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name='6'></a>\n",
"## 6 - He Initialization\n",
"\n",
"Finally, try \"He Initialization\"; this is named for the first author of He et al., 2015. (If you have heard of \"Xavier initialization\", this is similar except Xavier initialization uses a scaling factor for the weights $W^{[l]}$ of `sqrt(1./layers_dims[l-1])` where He initialization would use `sqrt(2./layers_dims[l-1])`.)\n",
"\n",
"<a name='ex-3'></a>\n",
"### Exercise 3 - initialize_parameters_he\n",
"\n",
"Implement the following function to initialize your parameters with He initialization. This function is similar to the previous `initialize_parameters_random(...)`. The only difference is that instead of multiplying `np.random.randn(..,..)` by 10, you will multiply it by $\\sqrt{\\frac{2}{\\text{dimension of the previous layer}}}$, which is what He initialization recommends for layers with a ReLU activation. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"deletable": false,
"nbgrader": {
"cell_type": "code",
"checksum": "dc6e68563172d4db3892e0f99b19e75f",
"grade": false,
"grade_id": "cell-028d29f9550d2487",
"locked": false,
"schema_version": 3,
"solution": true,
"task": false
}
},
"outputs": [],
"source": [
"# GRADED FUNCTION: initialize_parameters_he\n",
"\n",
"def initialize_parameters_he(layers_dims):\n",
" \"\"\"\n",
" Arguments:\n",
" layer_dims -- python array (list) containing the size of each layer.\n",
" \n",
" Returns:\n",
" parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
" W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
" b1 -- bias vector of shape (layers_dims[1], 1)\n",
" ...\n",
" WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
" bL -- bias vector of shape (layers_dims[L], 1)\n",
" \"\"\"\n",
" \n",
" np.random.seed(3)\n",
" parameters = {}\n",
" L = len(layers_dims) - 1 # integer representing the number of layers\n",
" \n",
" for l in range(1, L + 1):\n",
" #(≈ 2 lines of code)\n",
" # parameters['W' + str(l)] = \n",
" # parameters['b' + str(l)] =\n",
" # YOUR CODE STARTS HERE\n",
" parameters['W' + str(l)] = np.random.randn(layers_dims[l],layers_dims[l-1])*np.sqrt(2.0/(layers_dims[l-1]))\n",
" parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
" \n",
" # YOUR CODE ENDS HERE\n",
" \n",
" return parameters"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"deletable": false,
"editable": false,
"nbgrader": {
"cell_type": "code",
"checksum": "95bcbb6d1a4775f98da73563c218d4bf",
"grade": true,
"grade_id": "cell-bcf384daddbdb4db",
"locked": true,
"points": 10,
"schema_version": 3,
"solution": false,
"task": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"W1 = [[ 1.78862847 0.43650985]\n",
" [ 0.09649747 -1.8634927 ]\n",
" [-0.2773882 -0.35475898]\n",
" [-0.08274148 -0.62700068]]\n",
"b1 = [[0.]\n",
" [0.]\n",
" [0.]\n",
" [0.]]\n",
"W2 = [[-0.03098412 -0.33744411 -0.92904268 0.62552248]]\n",
"b2 = [[0.]]\n",
"\u001b[92m All tests passed.\n"
]
}
],
"source": [
"parameters = initialize_parameters_he([2, 4, 1])\n",
"print(\"W1 = \" + str(parameters[\"W1\"]))\n",
"print(\"b1 = \" + str(parameters[\"b1\"]))\n",
"print(\"W2 = \" + str(parameters[\"W2\"]))\n",
"print(\"b2 = \" + str(parameters[\"b2\"]))\n",
"\n",
"initialize_parameters_he_test(initialize_parameters_he)\n",
"# parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected output**\n",
"\n",
"```\n",
"W1 = [[ 1.78862847 0.43650985]\n",
" [ 0.09649747 -1.8634927 ]\n",
" [-0.2773882 -0.35475898]\n",
" [-0.08274148 -0.62700068]]\n",
"b1 = [[0.] [0.] [0.] [0.]]\n",
"W2 = [[-0.03098412 -0.33744411 -0.92904268 0.62552248]]\n",
"b2 = [[0.]]\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the following code to train your model on 15,000 iterations using He initialization."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cost after iteration 0: 0.8830537463419761\n",
"Cost after iteration 1000: 0.6879825919728063\n",
"Cost after iteration 2000: 0.6751286264523371\n",
"Cost after iteration 3000: 0.6526117768893805\n",
"Cost after iteration 4000: 0.6082958970572938\n",
"Cost after iteration 5000: 0.5304944491717495\n",
"Cost after iteration 6000: 0.4138645817071794\n",
"Cost after iteration 7000: 0.3117803464844441\n",
"Cost after iteration 8000: 0.23696215330322562\n",
"Cost after iteration 9000: 0.1859728720920684\n",
"Cost after iteration 10000: 0.15015556280371808\n",
"Cost after iteration 11000: 0.12325079292273551\n",
"Cost after iteration 12000: 0.09917746546525937\n",
"Cost after iteration 13000: 0.08457055954024283\n",
"Cost after iteration 14000: 0.07357895962677366\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"On the train set:\n",
"Accuracy: 0.9933333333333333\n",
"On the test set:\n",
"Accuracy: 0.96\n"
]
}
],
"source": [
"parameters = model(train_X, train_Y, initialization = \"he\")\n",
"print (\"On the train set:\")\n",
"predictions_train = predict(train_X, train_Y, parameters)\n",
"print (\"On the test set:\")\n",
"predictions_test = predict(test_X, test_Y, parameters)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"Model with He initialization\")\n",
"axes = plt.gca()\n",
"axes.set_xlim([-1.5,1.5])\n",
"axes.set_ylim([-1.5,1.5])\n",
"plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Observations**:\n",
"- The model with He initialization separates the blue and the red dots very well in a small number of iterations.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"<a name='7'></a>\n",
"## 7 - Conclusions"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"You've tried three different types of initializations. For the same number of iterations and same hyperparameters, the comparison is:\n",
"\n",
"<table> \n",
" <tr>\n",
" <td>\n",
" <b>Model</b>\n",
" </td>\n",
" <td>\n",
" <b>Train accuracy</b>\n",
" </td>\n",
" <td>\n",
" <b>Problem/Comment</b>\n",
" </td>\n",