1010 lines
268 KiB
Plaintext
1010 lines
268 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Initialization\n",
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"\n",
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"Welcome to the first assignment of \"Improving Deep Neural Networks\". \n",
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"\n",
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"Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help learning. \n",
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"\n",
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"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",
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"\n",
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"A well chosen initialization can:\n",
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"- Speed up the convergence of gradient descent\n",
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"- Increase the odds of gradient descent converging to a lower training (and generalization) error \n",
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"\n",
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"To get started, run the following cell to load the packages and the planar dataset you will try to classify."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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IkLa7oFaGDTyLhO0bsykushEZdfaKeA+kFfHZu6twOY89F7nZZbz+3CL++/m1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TAAAg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\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f7a81d71978>"
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]
|
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},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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}
|
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],
|
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"source": [
|
|
"import numpy as np\n",
|
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"import matplotlib.pyplot as plt\n",
|
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"import sklearn\n",
|
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"import sklearn.datasets\n",
|
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"from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation\n",
|
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"from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec\n",
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"\n",
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"%matplotlib inline\n",
|
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"plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
|
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"plt.rcParams['image.interpolation'] = 'nearest'\n",
|
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"plt.rcParams['image.cmap'] = 'gray'\n",
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"\n",
|
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"# load image dataset: blue/red dots in circles\n",
|
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"train_X, train_Y, test_X, test_Y = load_dataset()"
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]
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},
|
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"You would like a classifier to separate the blue dots from the red dots."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1 - Neural Network model "
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]
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},
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{
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"cell_type": "markdown",
|
|
"metadata": {},
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"source": [
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"You will use a 3-layer neural network (already implemented for you). Here are the initialization methods you will experiment with: \n",
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"- *Zeros initialization* -- setting `initialization = \"zeros\"` in the input argument.\n",
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"- *Random initialization* -- setting `initialization = \"random\"` in the input argument. This initializes the weights to large random values. \n",
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"- *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",
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"\n",
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"**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": {
|
|
"collapsed": true
|
|
},
|
|
"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",
|
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" 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",
|
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" \n",
|
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" Returns:\n",
|
|
" parameters -- parameters learnt by the model\n",
|
|
" \"\"\"\n",
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" \n",
|
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" grads = {}\n",
|
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" 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": 8,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"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": 9,
|
|
"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",
|
|
"<table> \n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **W1**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[ 0. 0. 0.]\n",
|
|
" [ 0. 0. 0.]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **b1**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[ 0.]\n",
|
|
" [ 0.]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **W2**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[ 0. 0.]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **b2**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[ 0.]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
"</table> "
|
|
]
|
|
},
|
|
{
|
|
"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": 10,
|
|
"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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2q5wMrKu87i7bqmYCu0q6QdJySSeX7SuBwyTtLmkSxW2kprbaiaQFkrokdfX09AxxOBER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\nERGNlzCMiIjG+/8qs5fH0fJiOQAAAABJRU5ErkJggg==\n",
|
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"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f7a6c8ac3c8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"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": 11,
|
|
"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 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 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 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 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 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 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 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 0\n",
|
|
" 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 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 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]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print (\"predictions_train = \" + str(predictions_train))\n",
|
|
"print (\"predictions_test = \" + str(predictions_test))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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LCAy4qX9jwe/z3pzC+xM/CnOpWS4L+4z+3P2HJJxOndPYpUsiygKaGbM+PyxZ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\nC363uAqGAFWIAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f7a6c296358>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"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": [
|
|
"<font color='blue'>\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": 31,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"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": 32,
|
|
"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",
|
|
"<table> \n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **W1**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[ 17.88628473 4.36509851 0.96497468]\n",
|
|
" [-18.63492703 -2.77388203 -3.54758979]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **b1**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[ 0.]\n",
|
|
" [ 0.]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **W2**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[-0.82741481 -6.27000677]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **b2**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[ 0.]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
"</table> "
|
|
]
|
|
},
|
|
{
|
|
"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": 33,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/home/jovyan/work/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",
|
|
"/home/jovyan/work/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: 0.6242434241539614\n",
|
|
"Cost after iteration 2000: 0.5978811277755388\n",
|
|
"Cost after iteration 3000: 0.5636242569764779\n",
|
|
"Cost after iteration 4000: 0.5500958254523324\n",
|
|
"Cost after iteration 5000: 0.544339206192789\n",
|
|
"Cost after iteration 6000: 0.5373584514307651\n",
|
|
"Cost after iteration 7000: 0.469574666760224\n",
|
|
"Cost after iteration 8000: 0.39766324943219844\n",
|
|
"Cost after iteration 9000: 0.3934423376823982\n",
|
|
"Cost after iteration 10000: 0.3920158992175907\n",
|
|
"Cost after iteration 11000: 0.38913979237487845\n",
|
|
"Cost after iteration 12000: 0.3861261344766218\n",
|
|
"Cost after iteration 13000: 0.3849694511273874\n",
|
|
"Cost after iteration 14000: 0.3827489017191917\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
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haB9mTh3BlycNpzhPH0OLSPIIvQzNLAJ8AHwOqAEWAhe7+3sxY6YDf+vu53Wy/hqg0t23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\niohIxlMZiohIxlMZiohIxlMZiohIxvv/K0/xdxKTGnUAAAAASUVORK5CYII=\n",
|
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"text/plain": [
|
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"<matplotlib.figure.Figure at 0x7f7a6c836898>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"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": 34,
|
|
"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 1\n",
|
|
" 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 0 0\n",
|
|
" 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 1 0 1\n",
|
|
" 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 0 0 1 0\n",
|
|
" 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 1 0 1 0 1\n",
|
|
" 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 0 1 1 0 1 1\n",
|
|
" 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 1 1 0 1 1 0 1\n",
|
|
" 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 1 1 1 1 0 0 0 1\n",
|
|
" 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 0\n",
|
|
" 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 1 1\n",
|
|
" 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": 35,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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RqBCJGDqucgOuf/lq7fWf3jrROEr8HBzYQrnSQqk5ZywteiwvdK5j6LkKx1FE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\nwVKWppD4lsHkri4M18fwVS3Yp83WKwQARYtO27mrFVnalVfTjFJEiy7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0KHFX8DEM6Oo2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\nAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f7a6c7dbbe0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"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",
|
|
"**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": 27,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"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",
|
|
" ### START CODE HERE ### (≈ 2 lines of code)\n",
|
|
" parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l-1]) * np.sqrt(2./layers_dims[l-1])\n",
|
|
" parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
|
|
" ### END CODE HERE ###\n",
|
|
" \n",
|
|
" return parameters"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"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",
|
|
"<table> \n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **W1**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[ 1.78862847 0.43650985]\n",
|
|
" [ 0.09649747 -1.8634927 ]\n",
|
|
" [-0.2773882 -0.35475898]\n",
|
|
" [-0.08274148 -0.62700068]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **b1**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[ 0.]\n",
|
|
" [ 0.]\n",
|
|
" [ 0.]\n",
|
|
" [ 0.]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **W2**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[-0.03098412 -0.33744411 -0.92904268 0.62552248]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **b2**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" [[ 0.]]\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
"\n",
|
|
"</table> "
|
|
]
|
|
},
|
|
{
|
|
"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": 29,
|
|
"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: 0.18597287209206834\n",
|
|
"Cost after iteration 10000: 0.15015556280371806\n",
|
|
"Cost after iteration 11000: 0.12325079292273546\n",
|
|
"Cost after iteration 12000: 0.09917746546525934\n",
|
|
"Cost after iteration 13000: 0.08457055954024278\n",
|
|
"Cost after iteration 14000: 0.07357895962677369\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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wYLe2ZLVs0Zi7IiLSZCn04kRqShLdc1rRPafV59ZV1zhLN+5g7pptfLS6jLlrtvHW0tID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\nCUOhJyIiCUOhJyIiCeP/A/RfZM1G9hqrAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f7a6c2cd160>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"On the train set:\n",
|
|
"Accuracy: 0.993333333333\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": 30,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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PsnZuEi873LIqlJJbgYnC1EbtaEnIY/ATF78R90Sum0kiApYNSyf6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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f7a6c0eabe0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"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",
|
|
"<table> \n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" **Model**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" **Train accuracy**\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" **Problem/Comment**\n",
|
|
" </td>\n",
|
|
"\n",
|
|
" </tr>\n",
|
|
" <td>\n",
|
|
" 3-layer NN with zeros initialization\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" 50%\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" fails to break symmetry\n",
|
|
" </td>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" 3-layer NN with large random initialization\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" 83%\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" too large weights \n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" 3-layer NN with He initialization\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" 99%\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" recommended method\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
"</table> "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<font color='blue'>\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": "3.6.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|