{ "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 will help learning. \n", "\n", "If you completed the previous course of this specialization, you probably followed our instructions for weight initialization, and it has worked out so far. But how do you choose the initialization for a new neural network? In this notebook, you will see how different initializations lead 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", "To get started, run the following cell to load the packages and the planar dataset you will try to classify." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import sklearn\n", "import sklearn.datasets\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 image dataset: blue/red dots in circles\n", "train_X, train_Y, test_X, test_Y = load_dataset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You would like a classifier to separate the blue dots from the red dots." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 - Neural Network model " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will use a 3-layer neural network (already implemented for you). Here are the initialization methods you will 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**: Please quickly read over the code below, and run it. In the next part you will implement the three initialization methods that this `model()` calls." ] }, { "cell_type": "code", "execution_count": 2, "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(0, 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": [ "## 2 - 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", "**Exercise**: 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 lets try it anyway and see what happens. Use np.zeros((..,..)) with the correct shapes." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "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", " ### START CODE HERE ### (≈ 2 lines of code)\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", " ### END CODE HERE ###\n", " return parameters" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "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" ] } ], "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\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
\n", " **W1**\n", " \n", " [[ 0. 0. 0.]\n", " [ 0. 0. 0.]]\n", "
\n", " **b1**\n", " \n", " [[ 0.]\n", " [ 0.]]\n", "
\n", " **W2**\n", " \n", " [[ 0. 0.]]\n", "
\n", " **b2**\n", " \n", " [[ 0.]]\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": 5, "metadata": { "scrolled": false }, "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", 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" ] }, "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 really bad, and the cost does not really decrease, and the algorithm performs no better than random guessing. Why? Lets look at the details of the predictions and the decision boundary:" ] }, { "cell_type": "code", "execution_count": 6, "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": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": [ "The model is predicting 0 for every example. \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, and you might as well be training a neural network with $n^{[l]}=1$ for every layer, and the network is no more powerful than a linear classifier such as logistic regression. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "**What you should remember**:\n", "- The weights $W^{[l]}$ should be initialized randomly to break symmetry. \n", "- It is however 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": [ "## 3 - Random initialization\n", "\n", "To break symmetry, lets intialize the weights randomly. Following random initialization, each neuron can then proceed to learn a different function of its inputs. In this exercise, you will see what happens if the weights are intialized randomly, but to very large values. \n", "\n", "**Exercise**: 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. We are using a fixed `np.random.seed(..)` to make sure your \"random\" weights match ours, so don't worry if running several times your code gives you always the same initial values for the parameters. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "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", " ### START CODE HERE ### (≈ 2 lines of code)\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", " ### END CODE HERE ###\n", "\n", " return parameters" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "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" ] } ], "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\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
\n", " **W1**\n", " \n", " [[ 17.88628473 4.36509851 0.96497468]\n", " [-18.63492703 -2.77388203 -3.54758979]]\n", "
\n", " **b1**\n", " \n", " [[ 0.]\n", " [ 0.]]\n", "
\n", " **W2**\n", " \n", " [[-0.82741481 -6.27000677]]\n", "
\n", " **b2**\n", " \n", " [[ 0.]]\n", "
" ] }, { "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": 10, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\abdur\\Desktop\\DL\\DL_Course2\\week5\\Initialization\\init_utils.py:145: RuntimeWarning: divide by zero encountered in log\n", " logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n", "C:\\Users\\abdur\\Desktop\\DL\\DL_Course2\\week5\\Initialization\\init_utils.py:145: RuntimeWarning: invalid value encountered in multiply\n", " logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Cost after iteration 0: inf\n", "Cost after iteration 1000: 0.6239567039908781\n", "Cost after iteration 2000: 0.5978043872838292\n", "Cost after iteration 3000: 0.563595830364618\n", "Cost after iteration 4000: 0.5500816882570866\n", "Cost after iteration 5000: 0.5443417928662615\n", "Cost after iteration 6000: 0.5373553777823036\n", "Cost after iteration 7000: 0.4700141958024487\n", "Cost after iteration 8000: 0.3976617665785177\n", "Cost after iteration 9000: 0.39344405717719166\n", "Cost after iteration 10000: 0.39201765232720626\n", "Cost after iteration 11000: 0.38910685278803786\n", "Cost after iteration 12000: 0.38612995897697244\n", "Cost after iteration 13000: 0.3849735792031832\n", "Cost after iteration 14000: 0.38275100578285265\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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 this isn't worth worrying about for our purposes. \n", "\n", "Anyway, it looks like you have broken symmetry, and this gives better results. than before. The model is no longer outputting all 0s. " ] }, { "cell_type": "code", "execution_count": 11, "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": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "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", "\n", "**In summary**:\n", "- Initializing weights to very large random values does not work well. \n", "- Hopefully intializing with small random values does better. The important question is: how small should be these random values be? Lets find out in the next part! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4 - 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", "**Exercise**: Implement the following function to initialize your parameters with He initialization.\n", "\n", "**Hint**: 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": 15, "metadata": {}, "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", " import math\n", " for l in range(1, L + 1):\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l-1])*math.sqrt(2./layers_dims[l-1])\n", " parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))*math.sqrt(2./layers_dims[l-1])\n", " ### END CODE HERE ###\n", " \n", " return parameters" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "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" ] } ], "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\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
\n", " **W1**\n", " \n", " [[ 1.78862847 0.43650985]\n", " [ 0.09649747 -1.8634927 ]\n", " [-0.2773882 -0.35475898]\n", " [-0.08274148 -0.62700068]]\n", "
\n", " **b1**\n", " \n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "
\n", " **W2**\n", " \n", " [[-0.03098412 -0.33744411 -0.92904268 0.62552248]]\n", "
\n", " **b2**\n", " \n", " [[ 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": 17, "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: 0.6526117768893807\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: 0.18597287209206836\n", "Cost after iteration 10000: 0.15015556280371817\n", "Cost after iteration 11000: 0.12325079292273552\n", "Cost after iteration 12000: 0.09917746546525932\n", "Cost after iteration 13000: 0.08457055954024274\n", "Cost after iteration 14000: 0.07357895962677362\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 18, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": [ "## 5 - Conclusions" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "You have seen three different types of initializations. For the same number of iterations and same hyperparameters the comparison is:\n", "\n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " **Model**\n", " \n", " **Train accuracy**\n", " \n", " **Problem/Comment**\n", "
\n", " 3-layer NN with zeros initialization\n", " \n", " 50%\n", " \n", " fails to break symmetry\n", "
\n", " 3-layer NN with large random initialization\n", " \n", " 83%\n", " \n", " too large weights \n", "
\n", " 3-layer NN with He initialization\n", " \n", " 99%\n", " \n", " recommended method\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "**What you should remember from this notebook**:\n", "- Different initializations lead to different results\n", "- Random initialization is used to break symmetry and make sure different hidden units can learn different things\n", "- Don't intialize to values that are too large\n", "- He initialization works well for networks with ReLU activations. " ] } ], "metadata": { "coursera": { "course_slug": "deep-neural-network", "graded_item_id": "XOESP", "launcher_item_id": "8IhFN" }, "kernelspec": { "display_name": "Python 3", "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": "3.8.5" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }