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Learning/week three/assignment3/.ipynb_checkpoints/assignment3-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4b596e8190ede829d8349d8dc51781e0a34f80e2 --- /dev/null +++ b/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week three/assignment3/.ipynb_checkpoints/assignment3-checkpoint.ipynb @@ -0,0 +1,1565 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Planar data classification with one hidden layer\n", + "\n", + "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \n", + "\n", + "**You will learn how to:**\n", + "- Implement a 2-class classification neural network with a single hidden layer\n", + "- Use units with a non-linear activation function, such as tanh \n", + "- Compute the cross entropy loss \n", + "- Implement forward and backward propagation\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Packages ##\n", + "\n", + "Let's first import all the packages that you will need during this assignment.\n", + "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n", + "- [sklearn](http://scikit-learn.org/stable/) provides simple and efficient tools for data mining and data analysis. \n", + "- [matplotlib](http://matplotlib.org) is a library for plotting graphs in Python.\n", + "- testCases provides some test examples to assess the correctness of your functions\n", + "- planar_utils provide various useful functions used in this assignment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Package imports\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from testCases import *\n", + "import sklearn\n", + "import sklearn.datasets\n", + "import sklearn.linear_model\n", + "from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets\n", + "\n", + "%matplotlib inline\n", + "\n", + "np.random.seed(1) # set a seed so that the results are consistent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Dataset ##\n", + "\n", + "First, let's get the dataset you will work on. The following code will load a \"flower\" 2-class dataset into variables `X` and `Y`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X, Y = load_planar_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize the dataset using matplotlib. The data looks like a \"flower\" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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toITNj86k/0cP1YFlNU9JkZVXnllCQb4Fa7kDo1FmztfxTJzaiYnXdMZsNnBg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the data:\n", + "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You have:\n", + " - a numpy-array (matrix) X that contains your features (x1, x2)\n", + " - a numpy-array (vector) Y that contains your labels (red:0, blue:1).\n", + "\n", + "Lets first get a better sense of what our data is like. \n", + "\n", + "**Exercise**: How many training examples do you have? In addition, what is the `shape` of the variables `X` and `Y`? \n", + "\n", + "**Hint**: How do you get the shape of a numpy array? [(help)](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The shape of X is: (2, 400)\n", + "The shape of Y is: (1, 400)\n", + "I have m = 400 training examples!\n" + ] + } + ], + "source": [ + "### START CODE HERE ### (≈ 3 lines of code)\n", + "shape_X = X.shape\n", + "shape_Y = Y.shape\n", + "m = shape_X[1] # training set size\n", + "### END CODE HERE ###\n", + "\n", + "print ('The shape of X is: ' + str(shape_X))\n", + "print ('The shape of Y is: ' + str(shape_Y))\n", + "print ('I have m = %d training examples!' % (m))" + ] + }, + { + "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", + "
**shape of X** (2, 400)
**shape of Y**(1, 400)
**m** 400
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Simple Logistic Regression\n", + "\n", + "Before building a full neural network, lets first see how logistic regression performs on this problem. You can use sklearn's built-in functions to do that. Run the code below to train a logistic regression classifier on the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n" + ] + } + ], + "source": [ + "# Train the logistic regression classifier\n", + "clf = sklearn.linear_model.LogisticRegressionCV();\n", + "clf.fit(X.T, Y.T);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now plot the decision boundary of these models. Run the code below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n" + ] + }, + { + "data": { + "image/png": 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Jzqy/mf2Moa/1FtjFNNLL6Mew8yP0NRRPzL3KwwunSfkixMwMwVtUyxxbvsSp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decision boundary for logistic regression\n", + "plot_decision_boundary(lambda x: clf.predict(x), X, Y)\n", + "plt.title(\"Logistic Regression\")\n", + "\n", + "# Print accuracy\n", + "LR_predictions = clf.predict(X.T)\n", + "print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +\n", + " '% ' + \"(percentage of correctly labelled datapoints)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Accuracy** 47%
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interpretation**: The dataset is not linearly separable, so logistic regression doesn't perform well. Hopefully a neural network will do better. Let's try this now! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Neural Network model\n", + "\n", + "Logistic regression did not work well on the \"flower dataset\". You are going to train a Neural Network with a single hidden layer.\n", + "\n", + "**Here is our model**:\n", + "\n", + "\n", + "**Mathematically**:\n", + "\n", + "For one example $x^{(i)}$:\n", + "$$z^{[1] (i)} = W^{[1]} x^{(i)} + b^{[1] (i)}\\tag{1}$$ \n", + "$$a^{[1] (i)} = \\tanh(z^{[1] (i)})\\tag{2}$$\n", + "$$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2] (i)}\\tag{3}$$\n", + "$$\\hat{y}^{(i)} = a^{[2] (i)} = \\sigma(z^{ [2] (i)})\\tag{4}$$\n", + "$$y^{(i)}_{prediction} = \\begin{cases} 1 & \\mbox{if } a^{[2](i)} > 0.5 \\\\ 0 & \\mbox{otherwise } \\end{cases}\\tag{5}$$\n", + "\n", + "Given the predictions on all the examples, you can also compute the cost $J$ as follows: \n", + "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large\\left(\\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large \\right) \\small \\tag{6}$$\n", + "\n", + "**Reminder**: The general methodology to build a Neural Network is to:\n", + " 1. Define the neural network structure ( # of input units, # of hidden units, etc). \n", + " 2. Initialize the model's parameters\n", + " 3. Loop:\n", + " - Implement forward propagation\n", + " - Compute loss\n", + " - Implement backward propagation to get the gradients\n", + " - Update parameters (gradient descent)\n", + "\n", + "You often build helper functions to compute steps 1-3 and then merge them into one function we call `nn_model()`. Once you've built `nn_model()` and learnt the right parameters, you can make predictions on new data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 - Defining the neural network structure ####\n", + "\n", + "**Exercise**: Define three variables:\n", + " - n_x: the size of the input layer\n", + " - n_h: the size of the hidden layer (set this to 4) \n", + " - n_y: the size of the output layer\n", + "\n", + "**Hint**: Use shapes of X and Y to find n_x and n_y. Also, hard code the hidden layer size to be 4." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: layer_sizes\n", + "\n", + "def layer_sizes(X, Y):\n", + " \"\"\"\n", + " Arguments:\n", + " X -- input dataset of shape (input size, number of examples)\n", + " Y -- labels of shape (output size, number of examples)\n", + " \n", + " Returns:\n", + " n_x -- the size of the input layer\n", + " n_h -- the size of the hidden layer\n", + " n_y -- the size of the output layer\n", + " \"\"\"\n", + " ### START CODE HERE ### (≈ 3 lines of code)\n", + " n_x = X.shape[0] # size of input layer\n", + " n_h = 4\n", + " n_y = Y.shape[0] # size of output layer\n", + " ### END CODE HERE ###\n", + " return (n_x, n_h, n_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The size of the input layer is: n_x = 5\n", + "The size of the hidden layer is: n_h = 4\n", + "The size of the output layer is: n_y = 2\n" + ] + } + ], + "source": [ + "X_assess, Y_assess = layer_sizes_test_case()\n", + "(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)\n", + "print(\"The size of the input layer is: n_x = \" + str(n_x))\n", + "print(\"The size of the hidden layer is: n_h = \" + str(n_h))\n", + "print(\"The size of the output layer is: n_y = \" + str(n_y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output** (these are not the sizes you will use for your network, they are just used to assess the function you've just coded).\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**n_x** 5
**n_h** 4
**n_y** 2
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 - Initialize the model's parameters ####\n", + "\n", + "**Exercise**: Implement the function `initialize_parameters()`.\n", + "\n", + "**Instructions**:\n", + "- Make sure your parameters' sizes are right. Refer to the neural network figure above if needed.\n", + "- You will initialize the weights matrices with random values. \n", + " - Use: `np.random.randn(a,b) * 0.01` to randomly initialize a matrix of shape (a,b).\n", + "- You will initialize the bias vectors as zeros. \n", + " - Use: `np.zeros((a,b))` to initialize a matrix of shape (a,b) with zeros." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_parameters\n", + "\n", + "def initialize_parameters(n_x, n_h, n_y):\n", + " \"\"\"\n", + " Argument:\n", + " n_x -- size of the input layer\n", + " n_h -- size of the hidden layer\n", + " n_y -- size of the output layer\n", + " \n", + " Returns:\n", + " params -- python dictionary containing your parameters:\n", + " W1 -- weight matrix of shape (n_h, n_x)\n", + " b1 -- bias vector of shape (n_h, 1)\n", + " W2 -- weight matrix of shape (n_y, n_h)\n", + " b2 -- bias vector of shape (n_y, 1)\n", + " \"\"\"\n", + " \n", + " np.random.seed(2) # we set up a seed so that your output matches ours although the initialization is random.\n", + " \n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 = np.random.randn(n_h,n_x)*0.01\n", + " b1 = np.zeros((n_h,1))\n", + " W2 = np.random.randn(n_y,n_h)*0.01\n", + " b2 = np.zeros((n_y,1))\n", + " ### END CODE HERE ###\n", + " \n", + " assert (W1.shape == (n_h, n_x))\n", + " assert (b1.shape == (n_h, 1))\n", + " assert (W2.shape == (n_y, n_h))\n", + " assert (b2.shape == (n_y, 1))\n", + " \n", + " parameters = {\"W1\": W1,\n", + " \"b1\": b1,\n", + " \"W2\": W2,\n", + " \"b2\": b2}\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[-0.00416758 -0.00056267]\n", + " [-0.02136196 0.01640271]\n", + " [-0.01793436 -0.00841747]\n", + " [ 0.00502881 -0.01245288]]\n", + "b1 = [[ 0.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 0.]]\n", + "W2 = [[-0.01057952 -0.00909008 0.00551454 0.02292208]]\n", + "b2 = [[ 0.]]\n" + ] + } + ], + "source": [ + "n_x, n_h, n_y = initialize_parameters_test_case()\n", + "\n", + "parameters = initialize_parameters(n_x, n_h, n_y)\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", + " \n", + " \n", + " \n", + "
**W1** [[-0.00416758 -0.00056267]\n", + " [-0.02136196 0.01640271]\n", + " [-0.01793436 -0.00841747]\n", + " [ 0.00502881 -0.01245288]]
**b1** [[ 0.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 0.]]
**W2** [[-0.01057952 -0.00909008 0.00551454 0.02292208]]
**b2** [[ 0.]]
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 - The Loop ####\n", + "\n", + "**Question**: Implement `forward_propagation()`.\n", + "\n", + "**Instructions**:\n", + "- Look above at the mathematical representation of your classifier.\n", + "- You can use the function `sigmoid()`. It is built-in (imported) in the notebook.\n", + "- You can use the function `np.tanh()`. It is part of the numpy library.\n", + "- The steps you have to implement are:\n", + " 1. Retrieve each parameter from the dictionary \"parameters\" (which is the output of `initialize_parameters()`) by using `parameters[\"..\"]`.\n", + " 2. Implement Forward Propagation. Compute $Z^{[1]}, A^{[1]}, Z^{[2]}$ and $A^{[2]}$ (the vector of all your predictions on all the examples in the training set).\n", + "- Values needed in the backpropagation are stored in \"`cache`\". The `cache` will be given as an input to the backpropagation function." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: forward_propagation\n", + "\n", + "def forward_propagation(X, parameters):\n", + " \"\"\"\n", + " Argument:\n", + " X -- input data of size (n_x, m)\n", + " parameters -- python dictionary containing your parameters (output of initialization function)\n", + " \n", + " Returns:\n", + " A2 -- The sigmoid output of the second activation\n", + " cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\"\n", + " \"\"\"\n", + " # Retrieve each parameter from the dictionary \"parameters\"\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 = parameters[\"W1\"]\n", + " b1 = parameters[\"b1\"]\n", + " W2 = parameters[\"W2\"]\n", + " b2 = parameters[\"b2\"]\n", + " ### END CODE HERE ###\n", + " \n", + " # Implement Forward Propagation to calculate A2 (probabilities)\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " Z1 = np.dot(W1,X)+b1\n", + " A1 = np.tanh(Z1)\n", + " Z2 = np.dot(W2,A1)+b2\n", + " A2 = sigmoid(Z2)\n", + " ### END CODE HERE ###\n", + " \n", + " assert(A2.shape == (1, X.shape[1]))\n", + " \n", + " cache = {\"Z1\": Z1,\n", + " \"A1\": A1,\n", + " \"Z2\": Z2,\n", + " \"A2\": A2}\n", + " \n", + " return A2, cache" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852\n" + ] + } + ], + "source": [ + "X_assess, parameters = forward_propagation_test_case()\n", + "\n", + "A2, cache = forward_propagation(X_assess, parameters)\n", + "\n", + "# Note: we use the mean here just to make sure that your output matches ours. \n", + "print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + "
-0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you have computed $A^{[2]}$ (in the Python variable \"`A2`\"), which contains $a^{[2](i)}$ for every example, you can compute the cost function as follows:\n", + "\n", + "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\n", + "\n", + "**Exercise**: Implement `compute_cost()` to compute the value of the cost $J$.\n", + "\n", + "**Instructions**:\n", + "- There are many ways to implement the cross-entropy loss. To help you, we give you how we would have implemented\n", + "$- \\sum\\limits_{i=0}^{m} y^{(i)}\\log(a^{[2](i)})$:\n", + "```python\n", + "logprobs = np.multiply(np.log(A2),Y)\n", + "cost = - np.sum(logprobs) # no need to use a for loop!\n", + "```\n", + "\n", + "(you can use either `np.multiply()` and then `np.sum()` or directly `np.dot()`).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: compute_cost\n", + "\n", + "def compute_cost(A2, Y, parameters):\n", + " \"\"\"\n", + " Computes the cross-entropy cost given in equation (13)\n", + " \n", + " Arguments:\n", + " A2 -- The sigmoid output of the second activation, of shape (1, number of examples)\n", + " Y -- \"true\" labels vector of shape (1, number of examples)\n", + " parameters -- python dictionary containing your parameters W1, b1, W2 and b2\n", + " \n", + " Returns:\n", + " cost -- cross-entropy cost given equation (13)\n", + " \"\"\"\n", + " \n", + " m = Y.shape[1] # number of example\n", + "\n", + " # Compute the cross-entropy cost\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " logprobs = np.multiply(Y,np.log(A2))+np.multiply((1-Y),np.log(1-A2))\n", + " cost = -np.sum(logprobs)/m\n", + " ### END CODE HERE ###\n", + " \n", + " cost = np.squeeze(cost) # makes sure cost is the dimension we expect. \n", + " # E.g., turns [[17]] into 17 \n", + " assert(isinstance(cost, float))\n", + " \n", + " return cost" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cost = 0.692919893776\n" + ] + } + ], + "source": [ + "A2, Y_assess, parameters = compute_cost_test_case()\n", + "\n", + "print(\"cost = \" + str(compute_cost(A2, Y_assess, parameters)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**cost** 0.692919893776
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the cache computed during forward propagation, you can now implement backward propagation.\n", + "\n", + "**Question**: Implement the function `backward_propagation()`.\n", + "\n", + "**Instructions**:\n", + "Backpropagation is usually the hardest (most mathematical) part in deep learning. To help you, here again is the slide from the lecture on backpropagation. You'll want to use the six equations on the right of this slide, since you are building a vectorized implementation. \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "- Tips:\n", + " - To compute dZ1 you'll need to compute $g^{[1]'}(Z^{[1]})$. Since $g^{[1]}(.)$ is the tanh activation function, if $a = g^{[1]}(z)$ then $g^{[1]'}(z) = 1-a^2$. So you can compute \n", + " $g^{[1]'}(Z^{[1]})$ using `(1 - np.power(A1, 2))`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: backward_propagation\n", + "\n", + "def backward_propagation(parameters, cache, X, Y):\n", + " \"\"\"\n", + " Implement the backward propagation using the instructions above.\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing our parameters \n", + " cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\".\n", + " X -- input data of shape (2, number of examples)\n", + " Y -- \"true\" labels vector of shape (1, number of examples)\n", + " \n", + " Returns:\n", + " grads -- python dictionary containing your gradients with respect to different parameters\n", + " \"\"\"\n", + " m = X.shape[1]\n", + " \n", + " # First, retrieve W1 and W2 from the dictionary \"parameters\".\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " W1 = parameters[\"W1\"]\n", + " W2 = parameters[\"W2\"]\n", + " ### END CODE HERE ###\n", + " \n", + " # Retrieve also A1 and A2 from dictionary \"cache\".\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " A1 = cache[\"A1\"]\n", + " A2 = cache[\"A2\"]\n", + " ### END CODE HERE ###\n", + " \n", + " # Backward propagation: calculate dW1, db1, dW2, db2. \n", + " ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above)\n", + " dZ2 = A2-Y\n", + " dW2 = np.dot(dZ2,A1.T)/m\n", + " db2 = np.sum(dZ2,axis=1,keepdims=True)/m\n", + " dZ1 = np.dot(W2.T,dZ2)*(1-np.power(A1,2))\n", + " dW1 = np.dot(dZ1,X.T)/m\n", + " db1 = np.sum(dZ1,axis=1,keepdims=True)/m\n", + " ### END CODE HERE ###\n", + " \n", + " grads = {\"dW1\": dW1,\n", + " \"db1\": db1,\n", + " \"dW2\": dW2,\n", + " \"db2\": db2}\n", + " \n", + " return grads" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dW1 = [[ 0.01018708 -0.00708701]\n", + " [ 0.00873447 -0.0060768 ]\n", + " [-0.00530847 0.00369379]\n", + " [-0.02206365 0.01535126]]\n", + "db1 = [[-0.00069728]\n", + " [-0.00060606]\n", + " [ 0.000364 ]\n", + " [ 0.00151207]]\n", + "dW2 = [[ 0.00363613 0.03153604 0.01162914 -0.01318316]]\n", + "db2 = [[ 0.06589489]]\n" + ] + } + ], + "source": [ + "parameters, cache, X_assess, Y_assess = backward_propagation_test_case()\n", + "\n", + "grads = backward_propagation(parameters, cache, X_assess, Y_assess)\n", + "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n", + "print (\"db1 = \"+ str(grads[\"db1\"]))\n", + "print (\"dW2 = \"+ str(grads[\"dW2\"]))\n", + "print (\"db2 = \"+ str(grads[\"db2\"]))" + ] + }, + { + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**dW1** [[ 0.01018708 -0.00708701]\n", + " [ 0.00873447 -0.0060768 ]\n", + " [-0.00530847 0.00369379]\n", + " [-0.02206365 0.01535126]]
**db1** [[-0.00069728]\n", + " [-0.00060606]\n", + " [ 0.000364 ]\n", + " [ 0.00151207]]
**dW2** [[ 0.00363613 0.03153604 0.01162914 -0.01318316]]
**db2** [[ 0.06589489]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question**: Implement the update rule. Use gradient descent. You have to use (dW1, db1, dW2, db2) in order to update (W1, b1, W2, b2).\n", + "\n", + "**General gradient descent rule**: $ \\theta = \\theta - \\alpha \\frac{\\partial J }{ \\partial \\theta }$ where $\\alpha$ is the learning rate and $\\theta$ represents a parameter.\n", + "\n", + "**Illustration**: The gradient descent algorithm with a good learning rate (converging) and a bad learning rate (diverging). Images courtesy of Adam Harley.\n", + "\n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: update_parameters\n", + "\n", + "def update_parameters(parameters, grads, learning_rate = 1.2):\n", + " \"\"\"\n", + " Updates parameters using the gradient descent update rule given above\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters \n", + " grads -- python dictionary containing your gradients \n", + " \n", + " Returns:\n", + " parameters -- python dictionary containing your updated parameters \n", + " \"\"\"\n", + " # Retrieve each parameter from the dictionary \"parameters\"\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 = parameters[\"W1\"]\n", + " b1 = parameters[\"b1\"]\n", + " W2 = parameters[\"W2\"]\n", + " b2 = parameters[\"b2\"]\n", + " ### END CODE HERE ###\n", + " \n", + " # Retrieve each gradient from the dictionary \"grads\"\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " dW1 = grads[\"dW1\"]\n", + " db1 = grads[\"db1\"]\n", + " dW2 = grads[\"dW2\"]\n", + " db2 = grads[\"db2\"]\n", + " ## END CODE HERE ###\n", + " \n", + " # Update rule for each parameter\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 -= learning_rate*dW1\n", + " b1 -= learning_rate*db1\n", + " W2 -= learning_rate*dW2\n", + " b2 -= learning_rate*db2\n", + " ### END CODE HERE ###\n", + " \n", + " parameters = {\"W1\": W1,\n", + " \"b1\": b1,\n", + " \"W2\": W2,\n", + " \"b2\": b2}\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[-0.00643025 0.01936718]\n", + " [-0.02410458 0.03978052]\n", + " [-0.01653973 -0.02096177]\n", + " [ 0.01046864 -0.05990141]]\n", + "b1 = [[ -1.02420756e-06]\n", + " [ 1.27373948e-05]\n", + " [ 8.32996807e-07]\n", + " [ -3.20136836e-06]]\n", + "W2 = [[-0.01041081 -0.04463285 0.01758031 0.04747113]]\n", + "b2 = [[ 0.00010457]]\n" + ] + } + ], + "source": [ + "parameters, grads = update_parameters_test_case()\n", + "parameters = update_parameters(parameters, grads)\n", + "\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", + " \n", + " \n", + " \n", + " \n", + "
**W1** [[-0.00643025 0.01936718]\n", + " [-0.02410458 0.03978052]\n", + " [-0.01653973 -0.02096177]\n", + " [ 0.01046864 -0.05990141]]
**b1** [[ -1.02420756e-06]\n", + " [ 1.27373948e-05]\n", + " [ 8.32996807e-07]\n", + " [ -3.20136836e-06]]
**W2** [[-0.01041081 -0.04463285 0.01758031 0.04747113]]
**b2** [[ 0.00010457]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4 - Integrate parts 4.1, 4.2 and 4.3 in nn_model() ####\n", + "\n", + "**Question**: Build your neural network model in `nn_model()`.\n", + "\n", + "**Instructions**: The neural network model has to use the previous functions in the right order." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: nn_model\n", + "\n", + "def nn_model(X, Y, n_h, num_iterations = 10000, print_cost=False):\n", + " \"\"\"\n", + " Arguments:\n", + " X -- dataset of shape (2, number of examples)\n", + " Y -- labels of shape (1, number of examples)\n", + " n_h -- size of the hidden layer\n", + " num_iterations -- Number of iterations in gradient descent loop\n", + " print_cost -- if True, print the cost every 1000 iterations\n", + " \n", + " Returns:\n", + " parameters -- parameters learnt by the model. They can then be used to predict.\n", + " \"\"\"\n", + " \n", + " np.random.seed(3)\n", + " n_x = layer_sizes(X, Y)[0]\n", + " n_y = layer_sizes(X, Y)[2]\n", + " \n", + " # Initialize parameters, then retrieve W1, b1, W2, b2. Inputs: \"n_x, n_h, n_y\". Outputs = \"W1, b1, W2, b2, parameters\".\n", + " ### START CODE HERE ### (≈ 5 lines of code)\n", + " parameters = initialize_parameters(n_x, n_h, n_y)\n", + " W1 = parameters[\"W1\"]\n", + " b1 = parameters[\"b1\"]\n", + " W2 = parameters[\"W2\"]\n", + " b2 = parameters[\"b2\"]\n", + " ### END CODE HERE ###\n", + " \n", + " # Loop (gradient descent)\n", + "\n", + " for i in range(0, num_iterations):\n", + " \n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\".\n", + " A2, cache = forward_propagation(X,parameters)\n", + " \n", + " # Cost function. Inputs: \"A2, Y, parameters\". Outputs: \"cost\".\n", + " cost = compute_cost(A2,Y,parameters)\n", + " \n", + " # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\".\n", + " grads = backward_propagation(parameters,cache,X,Y)\n", + " \n", + " # Gradient descent parameter update. Inputs: \"parameters, grads\". Outputs: \"parameters\".\n", + " parameters = update_parameters(parameters, grads)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " # Print the cost every 1000 iterations\n", + " if print_cost and i % 1000 == 0:\n", + " print (\"Cost after iteration %i: %f\" %(i, cost))\n", + "\n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:20: RuntimeWarning: divide by zero encountered in log\n", + "C:\\Users\\BD\\第一课编程作业\\第一课第三周编程作业\\assignment3\\planar_utils.py:34: RuntimeWarning: overflow encountered in exp\n", + " s = 1/(1+np.exp(-x))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[-4.18497897 5.33206142]\n", + " [-7.53803882 1.20755762]\n", + " [-4.19298806 5.32617188]\n", + " [ 7.53798331 -1.20758933]]\n", + "b1 = [[ 2.32932918]\n", + " [ 3.81001746]\n", + " [ 2.33008879]\n", + " [-3.81011387]]\n", + "W2 = [[-6033.8235662 -6008.1429712 -6033.08779759 6008.07951848]]\n", + "b2 = [[-52.67923259]]\n" + ] + } + ], + "source": [ + "X_assess, Y_assess = nn_model_test_case()\n", + "\n", + "parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=False)\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", + " \n", + " \n", + " \n", + "
**W1** [[-4.18494056 5.33220609]\n", + " [-7.52989382 1.24306181]\n", + " [-4.1929459 5.32632331]\n", + " [ 7.52983719 -1.24309422]]
**b1** [[ 2.32926819]\n", + " [ 3.79458998]\n", + " [ 2.33002577]\n", + " [-3.79468846]]
**W2** [[-6033.83672146 -6008.12980822 -6033.10095287 6008.06637269]]
**b2** [[-52.66607724]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.5 Predictions\n", + "\n", + "**Question**: Use your model to predict by building predict().\n", + "Use forward propagation to predict results.\n", + "\n", + "**Reminder**: predictions = $y_{prediction} = \\mathbb 1 \\text{{activation > 0.5}} = \\begin{cases}\n", + " 1 & \\text{if}\\ activation > 0.5 \\\\\n", + " 0 & \\text{otherwise}\n", + " \\end{cases}$ \n", + " \n", + "As an example, if you would like to set the entries of a matrix X to 0 and 1 based on a threshold you would do: ```X_new = (X > threshold)```" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: predict\n", + "\n", + "def predict(parameters, X):\n", + " \"\"\"\n", + " Using the learned parameters, predicts a class for each example in X\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters \n", + " X -- input data of size (n_x, m)\n", + " \n", + " Returns\n", + " predictions -- vector of predictions of our model (red: 0 / blue: 1)\n", + " \"\"\"\n", + " \n", + " # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold.\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " A2, cache = forward_propagation(X,parameters)\n", + " predictions = (A2>0.5)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predictions mean = 0.666666666667\n" + ] + } + ], + "source": [ + "parameters, X_assess = predict_test_case()\n", + "\n", + "predictions = predict(parameters, X_assess)\n", + "print(\"predictions mean = \" + str(np.mean(predictions)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**predictions mean** 0.666666666667
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is time to run the model and see how it performs on a planar dataset. Run the following code to test your model with a single hidden layer of $n_h$ hidden units." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: 0.693048\n", + "Cost after iteration 1000: 0.288083\n", + "Cost after iteration 2000: 0.254385\n", + "Cost after iteration 3000: 0.233864\n", + "Cost after iteration 4000: 0.226792\n", + "Cost after iteration 5000: 0.222644\n", + "Cost after iteration 6000: 0.219731\n", + "Cost after iteration 7000: 0.217504\n", + "Cost after iteration 8000: 0.219475\n", + "Cost after iteration 9000: 0.218612\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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5jpYXdsQkddB45PzD8K5B9+LWJhAIAU32WjCICJYFtWrv7e6A1jcWEe4pPsfC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\noBsKkluVExGEBsMoDJHodiXT88iJCYJS6kf2el5Efg74ceADanejXc3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Build a model with a n_h-dimensional hidden layer\n", + "parameters = nn_model(X, Y, n_h = 4, num_iterations = 10000, print_cost=True)\n", + "\n", + "# Plot the decision boundary\n", + "plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n", + "plt.title(\"Decision Boundary for hidden layer size \" + str(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Cost after iteration 9000** 0.218607
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 90%\n" + ] + } + ], + "source": [ + "# Print accuracy\n", + "predictions = predict(parameters, X)\n", + "print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
**Accuracy** 90%
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Accuracy is really high compared to Logistic Regression. The model has learnt the leaf patterns of the flower! Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression. \n", + "\n", + "Now, let's try out several hidden layer sizes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.6 - Tuning hidden layer size (optional/ungraded exercise) ###\n", + "\n", + "Run the following code. It may take 1-2 minutes. You will observe different behaviors of the model for various hidden layer sizes." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for 1 hidden units: 67.5 %\n", + "Accuracy for 2 hidden units: 67.25 %\n", + "Accuracy for 3 hidden units: 90.75 %\n", + "Accuracy for 4 hidden units: 90.5 %\n", + "Accuracy for 5 hidden units: 91.25 %\n", + "Accuracy for 10 hidden units: 90.25 %\n", + "Accuracy for 20 hidden units: 90.0 %\n" + ] + }, + { + "data": { + "image/png": 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sL3jN5S+GAecuxshvBqyvdu7EhuF8Oi/GQRAm0EmNmtaqet+zCS0LJi44+J5G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\nIoUascUK9jpujhRqdTdXPeySw2JnmHzX3nXzzo8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aipPZq1xIPtDcxGgpP6XNH2xsVSMj\nBUyEepiz08TdAkPFyZbUn51O0i2SdIsbbieAh/M3eTh/E4B30yc5n36k5ff0tdmNRxmVNZtb0QEE\nisHiBGFZ29axfxlYlsaRB0K8F/MPxjQkZT1ExKvS229iGIKFjIv0wA4Jevst7NDuO6EtBbHxuRKp\n+XrDkKUuffVvgCI9W8KqeivzXjXB9GACu+Quj98pxa09X+saEBCwfXQUj2SvcSl5bEtujq/ymALG\nwz3MW2kSToHB0vY76n9ZJJ0CSaew4XYCeCR/g0fyNwB4p+MMn6YebPk9fd0n22otZd3mVqTu5qHi\nBKHd5Ga77uZ3Y/5t2nUkFd0mLGv0DZiYpmBh3kVKCIUFPX0Wtr373LzEudQjfJx+BIHEFQZKCAxP\ngadIzzTc3Jj3qjTB9FCCUMnFCtwcsA2CQHaH8GTmIjejB8iaqwYTCwFKgmykUKySgSFdzmYuAvX2\n5X8/8AIZK4lEQ0diSYc/GP/Vnu3au5qnFy4S8aqc6zhBTbOIuiW+NvshQ+WpdV/3RWyIN7ufRDQC\nftX1BF+f/ZAHCrfux2HfFYQQHKzO8Lkda+n8rICkk1/erqvHpKtn97ZYX11XY5ccUvM+LegbaAqi\nuSrZrvBK6rAQVKMm1eju/R0EBATcX57OfMpIdICcGW91s2r06F7r5oVLQH1G6N8NvMiiFcdbcrNX\n4w/Hf7Vnu/au5pnMecJumY87TlDTTGINNw9u4ObP44d5q+uJuptVfazPCzPvc6y4eyY2CCE4UJ3l\nihVtncqgIOEUl7fb7W5ezXi4p1FXrUOj8dfa2aXRbJVsZ7ObK1GTSuDmgG0QBLI7BA1F0Yi2rvAK\njXqLtsbjSmEol+dmP1qeEXcufYJ5K7W8YizRcYXOaz1P74ka2o0QwOncVU7nrm76NQU9zJvdZ5tO\ntgC/6T7LQHlmV11kPLZ4meuxQRzNWBamIV2emv8UQ+2uu/LtWNscIrZYaTsrbRlRn11aXmdUTkB7\nhCeJ5mvojqQaNuoXGUE6V8A+Q0NRaOdm1exmU7n1QK0xW/2DjpNkrOTy5IElN7/e89SeqKHdCAE8\nmrvCo7nNd9/NGVHe6nq8xc2v9TzFwO1ZIt7uGUnz+MJn3IwebHHz0/Pnd13G3Gb5LHEUV2zgXAFW\nNXDzdgnc3EwQyO4g2l6Xr/6CCoFUguHS+PJDV33mqiqhMR3q2rNde++UG7FD+I2HlkLnbw98gz8c\n/yXRbQrT8xTlkkTTIBzREPf4BBN3S3x/7OecSz/CRLiXqFvm0cXLDDUupnY7fh0ONW8TrULU7p+P\n9mVhVlz6budAqeWa4ppdb4AVpHwF7DvafeXXuNlTYjmIBbgaH2qZq6qExkS4Z8927b1TbsQO+TaC\nkkLnbxpujmxz9vmym3UIh++9mxNuseHmE0yEe4i6JR5bvMxgaf070ruZqmZtHFSp9ee9BrTHqrj0\nrnVzyGD6UGLfTgYIAtkdxOHiGDdih5AbrGbpSnI1OshsqIOCEaW2TofAvdq1907xhIb0O9kKQcGI\n8JP+r/P9sV8gqA96d4VBxCtvGDwtzDvMTrvL53FNg4ND9j2vRY27JV6Y/ZBiWXHFPsjFziFGQv2c\nKNxYt3HWTma9Fv2lhEW42H4w+lLXw5odrPiuRUhFNFshVHJxTY18OtTcuVkpuifyiFWzYIWqr6DH\nM+VdP1A+IGCrDBfHuRk92JoiugZDSa7FDjFjd1EwIjhivUus/XnRuRGe0OrXLWsRgrwR4ad9X+d7\n468CW3NzZs5hbqbuZgXoGhwctu95LWrcLfH8zPsUK4IroYNc6BhmJDTAicJ1OmvZe/re95tHf9fl\nf8weoWO6tL6bbR0nFIQfaxFSEV2sECq7OKZGwcfNXeN5NLlyUS9UPbiNL5TJd+5PNwffpB3EV+Y+\nYSbUSVkP4QgDgaqvTK45qXtC492ux5CNuWTLtTqrt1OSzurCnu7aeycMFSc4lz6B53dhIgQ5M86k\n3c3HHQ8zEe5BKEXYq/LCzPscqMz47rNcksxOuyi10gtEShi7VeXIA6F7uvqrlGJiwuONky9RSHTU\nOyVKydXkYb4699FyY6zdwKO/6/Id7b9dd5tiwiaeqWBVvRZhKurz0WYPxPd1uo0fmifpu7mI7ioa\nRQvEFyrMHEosD1TXXYnutE6E1BTEstUgkA3Yd3x17iNm7A6qur2umx2h807n4xu6uacyjxlkSvky\nXJzg49TDbdyssWglmAh18VH6BJPhboSCiFfmhZn3Gaj4z10tlTzmZprd7EoYG7k/bh6b8Hjz1MsU\n46llN19JHua52XM8VBi5Z+99P1leeE4q4gtVzJq/mythg7mDG3Qk3odorqT/5iKat+LmxBo3G45E\nd1tT0uturu3bQDa4t7+DCMsqf3L7p7ww8z5nFy5yNnMRfW3qkZJIoeNp+srqcOP/tca2RmPszIsz\n793Pw99VdDg5TmWv1C80fBBK8nrPk4yHexq/b4OCGeVn/V8ja/h3CV7MuE2D0JfwZD3IvZcU8pJr\n8aGVIBZA0/A0g992PU5t3TsDO4cfvvKDDYNYAIRgajhJOWrW56E1/icF5NI200NJ3xl0+53UdBGj\nEcRC/Z6QpqBrPI/vl3ctwbpAwD4k4lX5s1VufiJzqdXNsj5ndSM3h2SNF2ffv5+Hv6vorC1yIntt\nXTe/1v00E8tu1smbMX7a/3Xyhv+F/OK819bNlfI9dnNOci05vBLEwoqbu5/A2aiedBfQlD2lCSYP\nJylHfNzcEWJmKLnnxr/cDdLTRXSv1c2dE5t08z5md1zd7iN0FEeKY9Do3h/xKvy263FEYxh5xC1R\nMsK4Yk13NyEIOVWOF0ZIOkWOFm4HtbEb8HTmAo4w+Cx5rCVlzNN0SiKEWiMZTwje7zhFzoqRsVLY\nXpVHFy5zKncVz/M/2SgJE2M1Ekmdjq76GJy7TW7RZeaR4ZaZdfVj1vn3Q9/lwfxNzmYu3rPvhZSK\n+VmHxQUPJSES1ejpN7GsjaXlNyh9Q4Rg9lACq+wQzdVHMxQTFrVw0PnQF6WI5Wq+sajmKXRH4lk6\nnqnjmnrLiroU9eH1AQH7ER3Z5OawV+GdrseW3Rx1SxTbuDnsVDhWuEXSKXKscDu4G7sBz2bO42gG\nnyeO+Lq5LOyWx6UQvNtxmqyVIGMlCTfcfDJ3FU+2d/P4aN3NnV0m+j1wc3bRZebUYV83u0Ln3w39\nPg/mb/Bk5tI9+15IqZibccgubt3NG+FbAiQEs4Nr3WxTCwchhy9KEc37u1l3FborG17W8EwNUZOB\nm1cRfKt2OA/lb3KscJs5O43t1TClw48GX/HdNu4WeSZz4T4f4e7myYWL3IoeoKyHlptyGNJlsDjO\naKSfta04lNC5GVuplSobYd7tPMOl5DEGoqOkLl4mVGqdt+e5sDDvkc96DB8Loet3X5i6U2tNYwMQ\ngppucSlxjIlwD99r1P7ebSZGa5SKcnnxsFiQ3LpR5fCx0LrB+w9f+QH85fbftxY2g+B1E5jV9o1l\nBDQ1cZo7EKP3Vg6xpqFELh2+D0caELDzeSR/g+OFW8zbaWyviq4kf3Ho277bJpxC4OYt8lTmArej\nA5Q1G7nWzdGBFjdLoXMzdmjZzSUjzDsNN/eFb9Px2WXsUuuces+FhYxHPucxfPTeuNnYwM2fddmL\nGAAAIABJREFUJY4zFermPxv/5T1x8/jtGuVSq5uPHAttO3hfr4fFEoGbN4e1WTcLwexAnN7bzW6u\nhg3yHaH7c7A7kCCQ3QUYyqOvMrf8c1dtgRm7o2lF0pAOp7Obb3EfUMeWDt8b+wWfph5gJHqQkFfl\n1OIVumoL3IoebH2Bki0dFZWmk7MSFHoeQrzwAKfffZVkxr+O1vPqKcid3Xf35J5IGRy4fYX5vkO+\nK78AUtPJmTHGwn0c2mCO31apVmVTELuEkpDNuHT6zMjbTC1swN1DKIUS+I4tUoKmVGzHNhg/liaS\nr6G7kmrIoBoxgprjgIBVmGvc3FHLMWelUNpqN7ucym5+NFxAnZCs8b3Rn/Np6iFuRQcIuRVOZ6+Q\nrmW35OaslSDf9zCi50HOvPNzEgtzPq+tB7TZBZeOrrvr5mTK4MCtL8j0HGjrZk/T67W/4R4OlP2v\nHbZLtSKbgtgllITFha1fiwTevvsI2d7NUqMpFdsJrXFz2KAa3t9uDhLVdyHfnPot6VoWQ7qYXg1d\nepxavMrh4tiXfWi7kpCs8VTmIn8y+jN+f+I1DpfGibsljhVuYciVVB+hGqmWbU4YUtPxDJMrZ5/D\nDPlvoxQUi3e/JicW1zhYm2Hw6qdonluPmH1whc68nbrr71+rKN/6SaWg7FODtOla2IDtoeqpwquv\nXmohw7cbqKJeu9TyuCYoJm1ynWGq+3xOXUDAZvjW1FuknByGdLAabj69+DmHV43LC9g8YVnj6cyn\ndTdPvs5waYKkW+RwcbTJzdom3Xz17HNY67m5cA/cnNA4VJ7m0LWLiHXc7KExb919N1er0vfXotTW\n64MDb98FZKubqyHD//oJyHW0ZkE1uTkSuDm4I7sLiXoVvj/2C+atFCU9RHd1gbDc3ly1gPY8P/sB\nXdUFLiaP42gGQ8UJFs0Ek+HudU8cRTvO69/4Rxy8cpHBq5+2nJ9M8+6fdIQQuI5i+OoFBm5fZeT4\nGaaGjiP15j9xQ3rEndbU5zvFtNuMeRJgr7lw2ExKUsDm0B0P3VU4tl5PP1KK5FyZRKZc30DAYmeY\nfEcYhGC+P0bXRB7RWHeQoj6mKLdPux0GBNxNol6ZPx77+So3ZwjL2pd9WHuOF2fe51Iiw6XkMRzN\nYLg4zryVYjrcve7r8qEkr33jH3HwygUGr15odbN1bwICx1UcvnKegVtXGHnwUaYOHUWtcbOOJO60\npj7fKZal+fYKEoItjQUMvL01NnazaLg5BJpgri9G12Sh2c227hvIBjQTBLK7FAG7dj7obkEAJ3PX\nOJm7tvzYlN3JPwy8gLteF2AhcHSLW8dP4ek6Rz7/uOlpw4TrV8pID6yQoLffJBS6s86F1YqkWqnb\nyqpWOHr5Q2YPDCOFVh9mS73bo6kchosTd/RefoRCGqGwoFJWTdLUBKQ66qlLgQjvHsJTdE3kCZWc\nRhENZDvrwktkymhL/wYKUnNllCYopMOU4xaTh1PEFivorqQcsyjFNzHAPiAgYFMEbr73aChO5a5y\nKreSsj0Z6uIn/c/japtx82mk0Dh85XzT04ax4ma74Wb7jt2sqFXrJ2S7WubopQ+YHRjCXe1mKbG8\nGoOlu+9mOySwQ4JKRTUtNgsBqfTGIUCQSrw1hCfpHi9gl1fcvNgVQVNqjZsVqbkSUhcUUyHKCZvJ\nkBG4eRsEgWwAClg04wgUSacQTNhYh77qPC9PvcXbXY+xaCbqD7ZLZzJMxo88wuGr59FUPb3HtiEz\nu5JaVCkpbl2vcWjYIhLdvjAdR9UHvTdOkrrn8dhbP+GLR58jl+4GAf3lWV6YfR+dezNu4OCgzfSk\nwy27l2uPnKUUSxJ1y8iFi/yb5/0blAVsj87JehCrLc02AJJzZRCsiLKBpiA5X6HQaNTkWjqLPdH7\ne8ABAQFbpu7mBBqSRODmdemvzPHNqd/ydtdjZM3GnNJ13Dx27CTD1z5FUwqhgW0L5le5uVxSjFyv\nMXjYIhy5e242PJfH3/wJnz/2VfKpupsHKjO8MPM+um9a050hhODgkM3MpMPNUB/XHz5LOZYg6pZg\n4SLHC7fbvjZYfN46XZMF7JJTr9ts/HOmZkvruLlMMVUv7QncvD2CQHafM2V38su+r1DVLAAibpmX\np39LZy0LQFmz+DxxhHkrRXd1gYfyN7Cl82Ue8pfOofI0fzr6MzwEr/U8zUj0AJ7QfaUpNEHfg0nC\n1RJCg5tX/VPAx27VOPrg9jsm2qHW9KFIMc/jb/+URI9NZ7eJuXbu4TZRSuHUFLoumjoearrAO36I\ni33P4TVWxQtWjF/3PUNsobwcSAXcGZoniRSdlsYQGu3HzfkNUQ8ICNi5TIa6+GXvs9Q0ExBE3RLf\nmvotaScHQFm3uRw/QsZK0l3N8FD+5r5382B5isHRn+Ih+HXvM9yKHMATmn9AqwkGHkxg1yogFCPX\n/FPAR0fuzM2hkPBxc44nfvtTkr0hOrqMe+5mXRc4xwf5rO+rq9wc543uJ3GFwcP5Gy37CoLYraO5\nknDRaVlwCtx8bwmaPe1jyprFTwaep2hEcDUDVzPImTH+buBFXKGzYMb50eArnEuf4Hp8iA86TvKj\nwVfIGcGKEdRn/r408y5/PPoz0o3Afy0CRVzUsEMa5VL7E5ZScPtmFbXNwdemKUgk9RZfaxp0pcRd\nE2Vu0eXaFxVGrle5fqXC2K1q0/zc9ztOL4ty+Rga6a3BUO+7g1V2/euR16Fm31l6XEBAwP2jpNv8\npP/rlIwIrmbiagZZM8aPD7yIh0bGTPCjQ9/ho/QjXI8P8WHHKX506DvkjaDWHepu/ub0O/zx2M9I\nNQL/lm2UJKY52CGNSqn9CVUpGB25AzdbGvFEOzdz19ycXXS59vmKm8dvV5Gr3dzZ6mZXM3i/41SL\nToIgdntYpa272QncfMcEgew+5lp8CLl27UgIXKEzEj3Am91nqWnG8snP0wyqmslvux77Eo5255J0\ni3x99kN02TzM3JAujy1cWk7l3WhFt1ZVTI7VcJ3tCbN3wKS718C0BLoOiZTO0NH1Z7huhVLJY3Lc\nQXp1uS91YJ4YXVnJXrTivq/VPIVoM5Q+YPNYZZfu8bzvcwqohHXkmn9uKWAhSFcKCNg1XI0Nt4yS\nQWg4wuBWdIA31rjZ1QwqusXbnY9+CUe7c0k6Bb4++2FTh2Oou/mJzCW0RtShbeDmakUxOe5s2819\nB0y6ltxsQDKl39WZtaWix9S4g5Sr3FyQTIytuHk53XoNVd3CFfVg6oev/CAIYreJVXbonlzPzUbg\n5ntEkFq8jynq4ZYVOgBP6FyLHmIq1NqdVwmN0XDfpt9j6bS/12t7+qrzfGfyDd7tfJR5K0nEq/D4\nwiUeyt9c3iYa1ZpqZfzI5yTFQoXBw/aWOgpCvRYm3WmS7rw3A8inJ3xSrxSUSxKnJjEtjWI45Dvc\nW2piZah3wLZJzxRb6myg/ncmNcF8fxzTkSRnS5iOh2PpLHZH6i36AwICdgVFY303z4Q6Qazxg9AY\njfRv+j32i5v7K3N8e/JN3u06w4KZJOKVeXzhEg/mR5a3ica05cY87chnPYp5b9tu7ug06biPblYK\nSkWJ6ygMUxBziyxayZbtLOlgKC8IYO+QjukN3DwQw6x5JGfLmI5Hza67uRYO3HynBIHsPqa/Mst5\n9aCPEAXj4V6UkiC2l/ZQ0m3e6nqCW9EDKGCoOMFzc+eIepU7P/AdykBllj8af7Xt80ITHDpscfvG\n+uMYpKyLafBI62zPL4t8zqPWZsKTEKD9by/ww9dOEs7X6JrIN53QpYBsZyjovncXsCpu2+cmhxN4\nlo5n6VSirRcsAQEBu4OB8gwXksd93TwW6UO1ibha7uL6UNRDDTcPIICh4jjPzZ0j4u3dEX4HKjN8\nb6y9mzVNMDhscfvmJtw86TB42L7bh7ht8lmPWpvDFgJctx7IPpW5wK97nmnq6mxIl8czn/HfB0Hs\nHWNV2qeITw4n8Ewdz9SpRK37eFT7gyC1eB9zqDTVVntKaKRrOf/bh0KQNWNt9+sh+JsDLzESPYAU\nGkpo3IoO8DcHXsLb51+5cFjn2EP2Utf9tpTLats1OfeC+dn2TURqQud/+NkDAJTjFvP9MVyjnrTl\n6YLF7kh9juk+Q3MlZsWFu5hSLXX/L44S4JlBrU1AwF5gsDTZ1s2eppOsFXzdrIRYt4eFh8bfHHiJ\nW9EBlNCQQmMkeqDh5v290BiO6Bx9cBNuLskd5ea5ddwsJVh2/d/1cHGcr8++T9QpglKEvArGSyY/\neu4b9+tQdwz6PXGz/9+P0gI332uCO7L7GA1FR3WR+VCHz3OSiFdhwecumiE95q0kSafgu99b0QEq\nuo1atZqshEZVt7gZPcCx4ujd+xC7EF3XGD5aH1VTLPg3gNppNy+dWrs7APDZE4/j2iurjKWETSlh\nr1xo7bQPc48RnqR7okCo5KAa6WoLPZG70rU51xEiNVtqueOdTwd3vAMC9goailQtx4KdannOkB4R\nr0JWtGZdGNIlYyVJuEXf/d6MHqCqWy1urug2t6IDHCmO370PsQsxDI2hhptL67hZ7KBz7Xp1ux1d\nOtqqkp7jhVGOF0bxEPzz7/w3MCH2fm75KjRP0jVeIFRuuBnI9ESXx9/cCdmOMKm5Vjfn0uHAzfeY\nIJDd5zy1cIFXe7/akm5yZuFzXM1gMtyD1JpXk5QQbYNYqM+9c3xSkh1hsGgloAh5I8K7HWcYi/Rh\nSoeT2auczl5Zbr6w1zEtjYNDNtMTVRYXmoUpBI0OxNs/+bmOYn62HijrBnR0mcQTm1sVVEqRz3os\nLriAIJnSsUOCsk9nR9c0+eTrz/nvaJ+evLsnCtiNFvxLI3LSMyVc687TivLpELoriS9UQNT3X0za\nLHYH3UoDAvYST2Uu8KveZ1vc/OjiZSqazXS4C7nGs1Jo67vZSuCI1ss+R+iNuejj5Iwo73WeYSzc\niyUdTmavcCp7dd+42bI0Dg3ZTE1Uyfq5OXVnd9echptL23RzLuuSXfBYcrNlCyrl1n8bTYOuHv/6\ny3/+yj+9k4+wa+kaz2OX3CY3d0wXcU2davTOalXzHQ03L1aW662LSZts1/7LRrvfBIHsPmewNMUL\nM+/xbuejFIwItqzx6MJlzmS/oKiHuZQ8hmTlJKtLj67qwvKcWT/StRym8nDW1PeY0iVdy1LWbf7y\n4MtUNROERk23+LDjJAt2khdn3r9nn3Un0t1nUXNqlIty+eQXCmv09K+cVGs1SakgERrE4vqGnQ5d\nVzFyvYLXKNlwHJgcq1HtNujqXv9kLT3FzWsV3OVSTEW5JLFDoqVRlWsYfPA7L6A2ysXaR+iuJFTy\nmSOnIDFfufP6GCFY7ImS7YpgOF49hbtNunFAQMDuZbg0wfMz7/Nu5xmKDTc/tvAZp7NXKBgRPkse\nbQpkNenRW51fnjPrR7qWxZQujt7sAVN5pGs5inqIvzr4MjXNQC27+RQLZoIX5j68Z591J9LTZ+HU\navWxeQ03hyMaPX2tbtYabt6o+7HrKG75uLnWbdC5gZs9TzGySTcLAT19Zsti+H/8l/+Y8z9uvcu/\nH9AdD7vsthS3aQoSmTKzdxjIIgSLvQ03u4Gb7ydBIBvA0eIYR4tjeGhoqwbyxLwy3x1/jTd6zjJv\npRAoDhdH+drsuXX3N1SaIOxV8IS2LFqhJCFZZbg4wUfph+vt3lcFup5mcD06yFnjInG3dK8+6o5D\n0wSHhmyqVUmtorBs0dQRcXbaYWG+YS4B0xMOBwYtorH2K7gLcw7emqwopSAz65LuMNoGwkopblxd\nkexqqlXFwL94gnP/aoaOmRmK8Tjnv/oVbj9wfMufeS9hVlxSsyWsiotr6RQTFgr/bC3dvTvzAgGU\nJnDs4PQdELCXOVYc5VhxtMXNcbfEdyde543us2SsJEIpjhVv89zsR+vub7g4wXudVVxNX04v1pRH\n2KswVJrgg46TOEJvSj12NYNr8WGeXLi4p5s1rkXTBIeGbaoVSa3q4+apGguZxjldAJMOBwctItH2\nbs7MOy1+VQrmG25uFwhLKblxtYps4+buXoNCTlKtSExT0NnTepf3h6/8AH68qY+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YAAAg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\nYPkHqVSG9oj7Dk8Xv92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TcBxNteJhmtHbu7RaDreE7wcpSrF4+FjjjctXr9zEN8zgLKGB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3/0I7jrxvcRyeYwf+sWFMOo7R9wJIrs2Bh2Lsz39X31sAJblaAYjmdmDQNQSETw1MrjePuX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\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BjFHE4hSt9mTaS6noAwRYWNInM7gRLCzpSKbZ0DmYDpwqOK5yzOfGt6r5odoD5Jwq3s48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\n9j6VUiJVybjVCg5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GVMmC2XTh6gpqeXOoOCIAaCOKGAk3u6FuPriZweLjCjTb77qZUYLj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\nIqtF0W2TgZDN0KgXfEspyWUd0lnJ3MA4aiKIabq8vPcuL1vbZTuKWcW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qahEo0OPC73mB3wnUtR8DGsFPI+T8koweHCAj78+38IRw+iFEuCcA5OKZJ7m8inBmuMPumsr67h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\nrGojvVlDtGp3fL7pwYX5c0hEREQ0KFyRpVA3dIpWbYw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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This may take about 2 minutes to run\n", + "\n", + "plt.figure(figsize=(16, 32))\n", + "hidden_layer_sizes = [1, 2, 3, 4, 5, 10, 20]\n", + "for i, n_h in enumerate(hidden_layer_sizes):\n", + " plt.subplot(5, 2, i+1)\n", + " plt.title('Hidden Layer of size %d' % n_h)\n", + " parameters = nn_model(X, Y, n_h, num_iterations = 5000)\n", + " plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n", + " predictions = predict(parameters, X)\n", + " accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)\n", + " print (\"Accuracy for {} hidden units: {} %\".format(n_h, accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interpretation**:\n", + "- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. \n", + "- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to fits the data well without also incurring noticable overfitting.\n", + "- You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Optional questions**:\n", + "\n", + "**Note**: Remember to submit the assignment but clicking the blue \"Submit Assignment\" button at the upper-right. \n", + "\n", + "Some optional/ungraded questions that you can explore if you wish: \n", + "- What happens when you change the tanh activation for a sigmoid activation or a ReLU activation?\n", + "- Play with the learning_rate. What happens?\n", + "- What if we change the dataset? (See part 5 below!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**You've learnt to:**\n", + "- Build a complete neural network with a hidden layer\n", + "- Make a good use of a non-linear unit\n", + "- Implemented forward propagation and backpropagation, and trained a neural network\n", + "- See the impact of varying the hidden layer size, including overfitting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice work! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5) Performance on other datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "If you want, you can rerun the whole notebook (minus the dataset part) for each of the following datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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NLQTnm7Qo+ruzwMaBuf9XbumEqmIK8kMxlPJfyDJn2nQm8robWL8jhXde3sAH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\ntnrrEVQmefszyEo+gHlGmcPVy/aTsifTVx67iJVL9/PC1Dk4s/PI2Z2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Datasets\n", + "noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()\n", + "\n", + "datasets = {\"noisy_circles\": noisy_circles,\n", + " \"noisy_moons\": noisy_moons,\n", + " \"blobs\": blobs,\n", + " \"gaussian_quantiles\": gaussian_quantiles}\n", + "\n", + "### START CODE HERE ### (choose your dataset)\n", + "dataset = \"noisy_circles\"\n", + "### END CODE HERE ###\n", + "\n", + "X, Y = datasets[dataset]\n", + "X, Y = X.T, Y.reshape(1, Y.shape[0])\n", + "\n", + "# make blobs binary\n", + "if dataset == \"blobs\":\n", + " Y = Y%2\n", + "\n", + "# Visualize the data\n", + "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congrats on finishing this Programming Assignment!\n", + "\n", + "Reference:\n", + "- http://scs.ryerson.ca/~aharley/neural-networks/\n", + "- http://cs231n.github.io/neural-networks-case-study/" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "neural-networks-deep-learning", + "graded_item_id": "wRuwL", + "launcher_item_id": "NI888" + }, + "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": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week three/assignment3/__pycache__/planar_utils.cpython-36.pyc b/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week three/assignment3/__pycache__/planar_utils.cpython-36.pyc new file mode 100644 index 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three/assignment3/assignment3.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4b596e8190ede829d8349d8dc51781e0a34f80e2 --- /dev/null +++ b/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week three/assignment3/assignment3.ipynb @@ -0,0 +1,1565 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Planar data classification with one hidden layer\n", + "\n", + "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \n", + "\n", + "**You will learn how to:**\n", + "- Implement a 2-class classification neural network with a single hidden layer\n", + "- Use units with a non-linear activation function, such as tanh \n", + "- Compute the cross entropy loss \n", + "- Implement forward and backward propagation\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Packages ##\n", + "\n", + "Let's first import all the packages that you will need during this assignment.\n", + "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n", + "- [sklearn](http://scikit-learn.org/stable/) provides simple and efficient tools for data mining and data analysis. \n", + "- [matplotlib](http://matplotlib.org) is a library for plotting graphs in Python.\n", + "- testCases provides some test examples to assess the correctness of your functions\n", + "- planar_utils provide various useful functions used in this assignment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Package imports\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from testCases import *\n", + "import sklearn\n", + "import sklearn.datasets\n", + "import sklearn.linear_model\n", + "from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets\n", + "\n", + "%matplotlib inline\n", + "\n", + "np.random.seed(1) # set a seed so that the results are consistent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Dataset ##\n", + "\n", + "First, let's get the dataset you will work on. The following code will load a \"flower\" 2-class dataset into variables `X` and `Y`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X, Y = load_planar_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize the dataset using matplotlib. The data looks like a \"flower\" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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toITNj86k/0cP1YFlNU9JkZVXnllCQb4Fa7kDo1FmztfxTJzaiYnXdMZsNnBg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the data:\n", + "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You have:\n", + " - a numpy-array (matrix) X that contains your features (x1, x2)\n", + " - a numpy-array (vector) Y that contains your labels (red:0, blue:1).\n", + "\n", + "Lets first get a better sense of what our data is like. \n", + "\n", + "**Exercise**: How many training examples do you have? In addition, what is the `shape` of the variables `X` and `Y`? \n", + "\n", + "**Hint**: How do you get the shape of a numpy array? [(help)](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The shape of X is: (2, 400)\n", + "The shape of Y is: (1, 400)\n", + "I have m = 400 training examples!\n" + ] + } + ], + "source": [ + "### START CODE HERE ### (≈ 3 lines of code)\n", + "shape_X = X.shape\n", + "shape_Y = Y.shape\n", + "m = shape_X[1] # training set size\n", + "### END CODE HERE ###\n", + "\n", + "print ('The shape of X is: ' + str(shape_X))\n", + "print ('The shape of Y is: ' + str(shape_Y))\n", + "print ('I have m = %d training examples!' % (m))" + ] + }, + { + "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", + "
**shape of X** (2, 400)
**shape of Y**(1, 400)
**m** 400
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Simple Logistic Regression\n", + "\n", + "Before building a full neural network, lets first see how logistic regression performs on this problem. You can use sklearn's built-in functions to do that. Run the code below to train a logistic regression classifier on the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n" + ] + } + ], + "source": [ + "# Train the logistic regression classifier\n", + "clf = sklearn.linear_model.LogisticRegressionCV();\n", + "clf.fit(X.T, Y.T);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now plot the decision boundary of these models. Run the code below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n" + ] + }, + { + "data": { + "image/png": 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Jzqy/mf2Moa/1FtjFNNLL6Mew8yP0NRRPzL3KwwunSfkixMwMwVtUyxxbvsSp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decision boundary for logistic regression\n", + "plot_decision_boundary(lambda x: clf.predict(x), X, Y)\n", + "plt.title(\"Logistic Regression\")\n", + "\n", + "# Print accuracy\n", + "LR_predictions = clf.predict(X.T)\n", + "print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +\n", + " '% ' + \"(percentage of correctly labelled datapoints)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Accuracy** 47%
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interpretation**: The dataset is not linearly separable, so logistic regression doesn't perform well. Hopefully a neural network will do better. Let's try this now! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Neural Network model\n", + "\n", + "Logistic regression did not work well on the \"flower dataset\". You are going to train a Neural Network with a single hidden layer.\n", + "\n", + "**Here is our model**:\n", + "\n", + "\n", + "**Mathematically**:\n", + "\n", + "For one example $x^{(i)}$:\n", + "$$z^{[1] (i)} = W^{[1]} x^{(i)} + b^{[1] (i)}\\tag{1}$$ \n", + "$$a^{[1] (i)} = \\tanh(z^{[1] (i)})\\tag{2}$$\n", + "$$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2] (i)}\\tag{3}$$\n", + "$$\\hat{y}^{(i)} = a^{[2] (i)} = \\sigma(z^{ [2] (i)})\\tag{4}$$\n", + "$$y^{(i)}_{prediction} = \\begin{cases} 1 & \\mbox{if } a^{[2](i)} > 0.5 \\\\ 0 & \\mbox{otherwise } \\end{cases}\\tag{5}$$\n", + "\n", + "Given the predictions on all the examples, you can also compute the cost $J$ as follows: \n", + "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large\\left(\\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large \\right) \\small \\tag{6}$$\n", + "\n", + "**Reminder**: The general methodology to build a Neural Network is to:\n", + " 1. Define the neural network structure ( # of input units, # of hidden units, etc). \n", + " 2. Initialize the model's parameters\n", + " 3. Loop:\n", + " - Implement forward propagation\n", + " - Compute loss\n", + " - Implement backward propagation to get the gradients\n", + " - Update parameters (gradient descent)\n", + "\n", + "You often build helper functions to compute steps 1-3 and then merge them into one function we call `nn_model()`. Once you've built `nn_model()` and learnt the right parameters, you can make predictions on new data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 - Defining the neural network structure ####\n", + "\n", + "**Exercise**: Define three variables:\n", + " - n_x: the size of the input layer\n", + " - n_h: the size of the hidden layer (set this to 4) \n", + " - n_y: the size of the output layer\n", + "\n", + "**Hint**: Use shapes of X and Y to find n_x and n_y. Also, hard code the hidden layer size to be 4." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: layer_sizes\n", + "\n", + "def layer_sizes(X, Y):\n", + " \"\"\"\n", + " Arguments:\n", + " X -- input dataset of shape (input size, number of examples)\n", + " Y -- labels of shape (output size, number of examples)\n", + " \n", + " Returns:\n", + " n_x -- the size of the input layer\n", + " n_h -- the size of the hidden layer\n", + " n_y -- the size of the output layer\n", + " \"\"\"\n", + " ### START CODE HERE ### (≈ 3 lines of code)\n", + " n_x = X.shape[0] # size of input layer\n", + " n_h = 4\n", + " n_y = Y.shape[0] # size of output layer\n", + " ### END CODE HERE ###\n", + " return (n_x, n_h, n_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The size of the input layer is: n_x = 5\n", + "The size of the hidden layer is: n_h = 4\n", + "The size of the output layer is: n_y = 2\n" + ] + } + ], + "source": [ + "X_assess, Y_assess = layer_sizes_test_case()\n", + "(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)\n", + "print(\"The size of the input layer is: n_x = \" + str(n_x))\n", + "print(\"The size of the hidden layer is: n_h = \" + str(n_h))\n", + "print(\"The size of the output layer is: n_y = \" + str(n_y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output** (these are not the sizes you will use for your network, they are just used to assess the function you've just coded).\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**n_x** 5
**n_h** 4
**n_y** 2
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 - Initialize the model's parameters ####\n", + "\n", + "**Exercise**: Implement the function `initialize_parameters()`.\n", + "\n", + "**Instructions**:\n", + "- Make sure your parameters' sizes are right. Refer to the neural network figure above if needed.\n", + "- You will initialize the weights matrices with random values. \n", + " - Use: `np.random.randn(a,b) * 0.01` to randomly initialize a matrix of shape (a,b).\n", + "- You will initialize the bias vectors as zeros. \n", + " - Use: `np.zeros((a,b))` to initialize a matrix of shape (a,b) with zeros." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_parameters\n", + "\n", + "def initialize_parameters(n_x, n_h, n_y):\n", + " \"\"\"\n", + " Argument:\n", + " n_x -- size of the input layer\n", + " n_h -- size of the hidden layer\n", + " n_y -- size of the output layer\n", + " \n", + " Returns:\n", + " params -- python dictionary containing your parameters:\n", + " W1 -- weight matrix of shape (n_h, n_x)\n", + " b1 -- bias vector of shape (n_h, 1)\n", + " W2 -- weight matrix of shape (n_y, n_h)\n", + " b2 -- bias vector of shape (n_y, 1)\n", + " \"\"\"\n", + " \n", + " np.random.seed(2) # we set up a seed so that your output matches ours although the initialization is random.\n", + " \n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 = np.random.randn(n_h,n_x)*0.01\n", + " b1 = np.zeros((n_h,1))\n", + " W2 = np.random.randn(n_y,n_h)*0.01\n", + " b2 = np.zeros((n_y,1))\n", + " ### END CODE HERE ###\n", + " \n", + " assert (W1.shape == (n_h, n_x))\n", + " assert (b1.shape == (n_h, 1))\n", + " assert (W2.shape == (n_y, n_h))\n", + " assert (b2.shape == (n_y, 1))\n", + " \n", + " parameters = {\"W1\": W1,\n", + " \"b1\": b1,\n", + " \"W2\": W2,\n", + " \"b2\": b2}\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[-0.00416758 -0.00056267]\n", + " [-0.02136196 0.01640271]\n", + " [-0.01793436 -0.00841747]\n", + " [ 0.00502881 -0.01245288]]\n", + "b1 = [[ 0.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 0.]]\n", + "W2 = [[-0.01057952 -0.00909008 0.00551454 0.02292208]]\n", + "b2 = [[ 0.]]\n" + ] + } + ], + "source": [ + "n_x, n_h, n_y = initialize_parameters_test_case()\n", + "\n", + "parameters = initialize_parameters(n_x, n_h, n_y)\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", + " \n", + " \n", + " \n", + "
**W1** [[-0.00416758 -0.00056267]\n", + " [-0.02136196 0.01640271]\n", + " [-0.01793436 -0.00841747]\n", + " [ 0.00502881 -0.01245288]]
**b1** [[ 0.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 0.]]
**W2** [[-0.01057952 -0.00909008 0.00551454 0.02292208]]
**b2** [[ 0.]]
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 - The Loop ####\n", + "\n", + "**Question**: Implement `forward_propagation()`.\n", + "\n", + "**Instructions**:\n", + "- Look above at the mathematical representation of your classifier.\n", + "- You can use the function `sigmoid()`. It is built-in (imported) in the notebook.\n", + "- You can use the function `np.tanh()`. It is part of the numpy library.\n", + "- The steps you have to implement are:\n", + " 1. Retrieve each parameter from the dictionary \"parameters\" (which is the output of `initialize_parameters()`) by using `parameters[\"..\"]`.\n", + " 2. Implement Forward Propagation. Compute $Z^{[1]}, A^{[1]}, Z^{[2]}$ and $A^{[2]}$ (the vector of all your predictions on all the examples in the training set).\n", + "- Values needed in the backpropagation are stored in \"`cache`\". The `cache` will be given as an input to the backpropagation function." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: forward_propagation\n", + "\n", + "def forward_propagation(X, parameters):\n", + " \"\"\"\n", + " Argument:\n", + " X -- input data of size (n_x, m)\n", + " parameters -- python dictionary containing your parameters (output of initialization function)\n", + " \n", + " Returns:\n", + " A2 -- The sigmoid output of the second activation\n", + " cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\"\n", + " \"\"\"\n", + " # Retrieve each parameter from the dictionary \"parameters\"\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 = parameters[\"W1\"]\n", + " b1 = parameters[\"b1\"]\n", + " W2 = parameters[\"W2\"]\n", + " b2 = parameters[\"b2\"]\n", + " ### END CODE HERE ###\n", + " \n", + " # Implement Forward Propagation to calculate A2 (probabilities)\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " Z1 = np.dot(W1,X)+b1\n", + " A1 = np.tanh(Z1)\n", + " Z2 = np.dot(W2,A1)+b2\n", + " A2 = sigmoid(Z2)\n", + " ### END CODE HERE ###\n", + " \n", + " assert(A2.shape == (1, X.shape[1]))\n", + " \n", + " cache = {\"Z1\": Z1,\n", + " \"A1\": A1,\n", + " \"Z2\": Z2,\n", + " \"A2\": A2}\n", + " \n", + " return A2, cache" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852\n" + ] + } + ], + "source": [ + "X_assess, parameters = forward_propagation_test_case()\n", + "\n", + "A2, cache = forward_propagation(X_assess, parameters)\n", + "\n", + "# Note: we use the mean here just to make sure that your output matches ours. \n", + "print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + "
-0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you have computed $A^{[2]}$ (in the Python variable \"`A2`\"), which contains $a^{[2](i)}$ for every example, you can compute the cost function as follows:\n", + "\n", + "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\n", + "\n", + "**Exercise**: Implement `compute_cost()` to compute the value of the cost $J$.\n", + "\n", + "**Instructions**:\n", + "- There are many ways to implement the cross-entropy loss. To help you, we give you how we would have implemented\n", + "$- \\sum\\limits_{i=0}^{m} y^{(i)}\\log(a^{[2](i)})$:\n", + "```python\n", + "logprobs = np.multiply(np.log(A2),Y)\n", + "cost = - np.sum(logprobs) # no need to use a for loop!\n", + "```\n", + "\n", + "(you can use either `np.multiply()` and then `np.sum()` or directly `np.dot()`).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: compute_cost\n", + "\n", + "def compute_cost(A2, Y, parameters):\n", + " \"\"\"\n", + " Computes the cross-entropy cost given in equation (13)\n", + " \n", + " Arguments:\n", + " A2 -- The sigmoid output of the second activation, of shape (1, number of examples)\n", + " Y -- \"true\" labels vector of shape (1, number of examples)\n", + " parameters -- python dictionary containing your parameters W1, b1, W2 and b2\n", + " \n", + " Returns:\n", + " cost -- cross-entropy cost given equation (13)\n", + " \"\"\"\n", + " \n", + " m = Y.shape[1] # number of example\n", + "\n", + " # Compute the cross-entropy cost\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " logprobs = np.multiply(Y,np.log(A2))+np.multiply((1-Y),np.log(1-A2))\n", + " cost = -np.sum(logprobs)/m\n", + " ### END CODE HERE ###\n", + " \n", + " cost = np.squeeze(cost) # makes sure cost is the dimension we expect. \n", + " # E.g., turns [[17]] into 17 \n", + " assert(isinstance(cost, float))\n", + " \n", + " return cost" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cost = 0.692919893776\n" + ] + } + ], + "source": [ + "A2, Y_assess, parameters = compute_cost_test_case()\n", + "\n", + "print(\"cost = \" + str(compute_cost(A2, Y_assess, parameters)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**cost** 0.692919893776
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the cache computed during forward propagation, you can now implement backward propagation.\n", + "\n", + "**Question**: Implement the function `backward_propagation()`.\n", + "\n", + "**Instructions**:\n", + "Backpropagation is usually the hardest (most mathematical) part in deep learning. To help you, here again is the slide from the lecture on backpropagation. You'll want to use the six equations on the right of this slide, since you are building a vectorized implementation. \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "- Tips:\n", + " - To compute dZ1 you'll need to compute $g^{[1]'}(Z^{[1]})$. Since $g^{[1]}(.)$ is the tanh activation function, if $a = g^{[1]}(z)$ then $g^{[1]'}(z) = 1-a^2$. So you can compute \n", + " $g^{[1]'}(Z^{[1]})$ using `(1 - np.power(A1, 2))`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: backward_propagation\n", + "\n", + "def backward_propagation(parameters, cache, X, Y):\n", + " \"\"\"\n", + " Implement the backward propagation using the instructions above.\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing our parameters \n", + " cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\".\n", + " X -- input data of shape (2, number of examples)\n", + " Y -- \"true\" labels vector of shape (1, number of examples)\n", + " \n", + " Returns:\n", + " grads -- python dictionary containing your gradients with respect to different parameters\n", + " \"\"\"\n", + " m = X.shape[1]\n", + " \n", + " # First, retrieve W1 and W2 from the dictionary \"parameters\".\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " W1 = parameters[\"W1\"]\n", + " W2 = parameters[\"W2\"]\n", + " ### END CODE HERE ###\n", + " \n", + " # Retrieve also A1 and A2 from dictionary \"cache\".\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " A1 = cache[\"A1\"]\n", + " A2 = cache[\"A2\"]\n", + " ### END CODE HERE ###\n", + " \n", + " # Backward propagation: calculate dW1, db1, dW2, db2. \n", + " ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above)\n", + " dZ2 = A2-Y\n", + " dW2 = np.dot(dZ2,A1.T)/m\n", + " db2 = np.sum(dZ2,axis=1,keepdims=True)/m\n", + " dZ1 = np.dot(W2.T,dZ2)*(1-np.power(A1,2))\n", + " dW1 = np.dot(dZ1,X.T)/m\n", + " db1 = np.sum(dZ1,axis=1,keepdims=True)/m\n", + " ### END CODE HERE ###\n", + " \n", + " grads = {\"dW1\": dW1,\n", + " \"db1\": db1,\n", + " \"dW2\": dW2,\n", + " \"db2\": db2}\n", + " \n", + " return grads" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dW1 = [[ 0.01018708 -0.00708701]\n", + " [ 0.00873447 -0.0060768 ]\n", + " [-0.00530847 0.00369379]\n", + " [-0.02206365 0.01535126]]\n", + "db1 = [[-0.00069728]\n", + " [-0.00060606]\n", + " [ 0.000364 ]\n", + " [ 0.00151207]]\n", + "dW2 = [[ 0.00363613 0.03153604 0.01162914 -0.01318316]]\n", + "db2 = [[ 0.06589489]]\n" + ] + } + ], + "source": [ + "parameters, cache, X_assess, Y_assess = backward_propagation_test_case()\n", + "\n", + "grads = backward_propagation(parameters, cache, X_assess, Y_assess)\n", + "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n", + "print (\"db1 = \"+ str(grads[\"db1\"]))\n", + "print (\"dW2 = \"+ str(grads[\"dW2\"]))\n", + "print (\"db2 = \"+ str(grads[\"db2\"]))" + ] + }, + { + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**dW1** [[ 0.01018708 -0.00708701]\n", + " [ 0.00873447 -0.0060768 ]\n", + " [-0.00530847 0.00369379]\n", + " [-0.02206365 0.01535126]]
**db1** [[-0.00069728]\n", + " [-0.00060606]\n", + " [ 0.000364 ]\n", + " [ 0.00151207]]
**dW2** [[ 0.00363613 0.03153604 0.01162914 -0.01318316]]
**db2** [[ 0.06589489]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question**: Implement the update rule. Use gradient descent. You have to use (dW1, db1, dW2, db2) in order to update (W1, b1, W2, b2).\n", + "\n", + "**General gradient descent rule**: $ \\theta = \\theta - \\alpha \\frac{\\partial J }{ \\partial \\theta }$ where $\\alpha$ is the learning rate and $\\theta$ represents a parameter.\n", + "\n", + "**Illustration**: The gradient descent algorithm with a good learning rate (converging) and a bad learning rate (diverging). Images courtesy of Adam Harley.\n", + "\n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: update_parameters\n", + "\n", + "def update_parameters(parameters, grads, learning_rate = 1.2):\n", + " \"\"\"\n", + " Updates parameters using the gradient descent update rule given above\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters \n", + " grads -- python dictionary containing your gradients \n", + " \n", + " Returns:\n", + " parameters -- python dictionary containing your updated parameters \n", + " \"\"\"\n", + " # Retrieve each parameter from the dictionary \"parameters\"\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 = parameters[\"W1\"]\n", + " b1 = parameters[\"b1\"]\n", + " W2 = parameters[\"W2\"]\n", + " b2 = parameters[\"b2\"]\n", + " ### END CODE HERE ###\n", + " \n", + " # Retrieve each gradient from the dictionary \"grads\"\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " dW1 = grads[\"dW1\"]\n", + " db1 = grads[\"db1\"]\n", + " dW2 = grads[\"dW2\"]\n", + " db2 = grads[\"db2\"]\n", + " ## END CODE HERE ###\n", + " \n", + " # Update rule for each parameter\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 -= learning_rate*dW1\n", + " b1 -= learning_rate*db1\n", + " W2 -= learning_rate*dW2\n", + " b2 -= learning_rate*db2\n", + " ### END CODE HERE ###\n", + " \n", + " parameters = {\"W1\": W1,\n", + " \"b1\": b1,\n", + " \"W2\": W2,\n", + " \"b2\": b2}\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[-0.00643025 0.01936718]\n", + " [-0.02410458 0.03978052]\n", + " [-0.01653973 -0.02096177]\n", + " [ 0.01046864 -0.05990141]]\n", + "b1 = [[ -1.02420756e-06]\n", + " [ 1.27373948e-05]\n", + " [ 8.32996807e-07]\n", + " [ -3.20136836e-06]]\n", + "W2 = [[-0.01041081 -0.04463285 0.01758031 0.04747113]]\n", + "b2 = [[ 0.00010457]]\n" + ] + } + ], + "source": [ + "parameters, grads = update_parameters_test_case()\n", + "parameters = update_parameters(parameters, grads)\n", + "\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", + " \n", + " \n", + " \n", + " \n", + "
**W1** [[-0.00643025 0.01936718]\n", + " [-0.02410458 0.03978052]\n", + " [-0.01653973 -0.02096177]\n", + " [ 0.01046864 -0.05990141]]
**b1** [[ -1.02420756e-06]\n", + " [ 1.27373948e-05]\n", + " [ 8.32996807e-07]\n", + " [ -3.20136836e-06]]
**W2** [[-0.01041081 -0.04463285 0.01758031 0.04747113]]
**b2** [[ 0.00010457]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4 - Integrate parts 4.1, 4.2 and 4.3 in nn_model() ####\n", + "\n", + "**Question**: Build your neural network model in `nn_model()`.\n", + "\n", + "**Instructions**: The neural network model has to use the previous functions in the right order." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: nn_model\n", + "\n", + "def nn_model(X, Y, n_h, num_iterations = 10000, print_cost=False):\n", + " \"\"\"\n", + " Arguments:\n", + " X -- dataset of shape (2, number of examples)\n", + " Y -- labels of shape (1, number of examples)\n", + " n_h -- size of the hidden layer\n", + " num_iterations -- Number of iterations in gradient descent loop\n", + " print_cost -- if True, print the cost every 1000 iterations\n", + " \n", + " Returns:\n", + " parameters -- parameters learnt by the model. They can then be used to predict.\n", + " \"\"\"\n", + " \n", + " np.random.seed(3)\n", + " n_x = layer_sizes(X, Y)[0]\n", + " n_y = layer_sizes(X, Y)[2]\n", + " \n", + " # Initialize parameters, then retrieve W1, b1, W2, b2. Inputs: \"n_x, n_h, n_y\". Outputs = \"W1, b1, W2, b2, parameters\".\n", + " ### START CODE HERE ### (≈ 5 lines of code)\n", + " parameters = initialize_parameters(n_x, n_h, n_y)\n", + " W1 = parameters[\"W1\"]\n", + " b1 = parameters[\"b1\"]\n", + " W2 = parameters[\"W2\"]\n", + " b2 = parameters[\"b2\"]\n", + " ### END CODE HERE ###\n", + " \n", + " # Loop (gradient descent)\n", + "\n", + " for i in range(0, num_iterations):\n", + " \n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\".\n", + " A2, cache = forward_propagation(X,parameters)\n", + " \n", + " # Cost function. Inputs: \"A2, Y, parameters\". Outputs: \"cost\".\n", + " cost = compute_cost(A2,Y,parameters)\n", + " \n", + " # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\".\n", + " grads = backward_propagation(parameters,cache,X,Y)\n", + " \n", + " # Gradient descent parameter update. Inputs: \"parameters, grads\". Outputs: \"parameters\".\n", + " parameters = update_parameters(parameters, grads)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " # Print the cost every 1000 iterations\n", + " if print_cost and i % 1000 == 0:\n", + " print (\"Cost after iteration %i: %f\" %(i, cost))\n", + "\n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:20: RuntimeWarning: divide by zero encountered in log\n", + "C:\\Users\\BD\\第一课编程作业\\第一课第三周编程作业\\assignment3\\planar_utils.py:34: RuntimeWarning: overflow encountered in exp\n", + " s = 1/(1+np.exp(-x))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[-4.18497897 5.33206142]\n", + " [-7.53803882 1.20755762]\n", + " [-4.19298806 5.32617188]\n", + " [ 7.53798331 -1.20758933]]\n", + "b1 = [[ 2.32932918]\n", + " [ 3.81001746]\n", + " [ 2.33008879]\n", + " [-3.81011387]]\n", + "W2 = [[-6033.8235662 -6008.1429712 -6033.08779759 6008.07951848]]\n", + "b2 = [[-52.67923259]]\n" + ] + } + ], + "source": [ + "X_assess, Y_assess = nn_model_test_case()\n", + "\n", + "parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=False)\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", + " \n", + " \n", + " \n", + "
**W1** [[-4.18494056 5.33220609]\n", + " [-7.52989382 1.24306181]\n", + " [-4.1929459 5.32632331]\n", + " [ 7.52983719 -1.24309422]]
**b1** [[ 2.32926819]\n", + " [ 3.79458998]\n", + " [ 2.33002577]\n", + " [-3.79468846]]
**W2** [[-6033.83672146 -6008.12980822 -6033.10095287 6008.06637269]]
**b2** [[-52.66607724]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.5 Predictions\n", + "\n", + "**Question**: Use your model to predict by building predict().\n", + "Use forward propagation to predict results.\n", + "\n", + "**Reminder**: predictions = $y_{prediction} = \\mathbb 1 \\text{{activation > 0.5}} = \\begin{cases}\n", + " 1 & \\text{if}\\ activation > 0.5 \\\\\n", + " 0 & \\text{otherwise}\n", + " \\end{cases}$ \n", + " \n", + "As an example, if you would like to set the entries of a matrix X to 0 and 1 based on a threshold you would do: ```X_new = (X > threshold)```" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: predict\n", + "\n", + "def predict(parameters, X):\n", + " \"\"\"\n", + " Using the learned parameters, predicts a class for each example in X\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters \n", + " X -- input data of size (n_x, m)\n", + " \n", + " Returns\n", + " predictions -- vector of predictions of our model (red: 0 / blue: 1)\n", + " \"\"\"\n", + " \n", + " # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold.\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " A2, cache = forward_propagation(X,parameters)\n", + " predictions = (A2>0.5)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predictions mean = 0.666666666667\n" + ] + } + ], + "source": [ + "parameters, X_assess = predict_test_case()\n", + "\n", + "predictions = predict(parameters, X_assess)\n", + "print(\"predictions mean = \" + str(np.mean(predictions)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**predictions mean** 0.666666666667
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is time to run the model and see how it performs on a planar dataset. Run the following code to test your model with a single hidden layer of $n_h$ hidden units." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: 0.693048\n", + "Cost after iteration 1000: 0.288083\n", + "Cost after iteration 2000: 0.254385\n", + "Cost after iteration 3000: 0.233864\n", + "Cost after iteration 4000: 0.226792\n", + "Cost after iteration 5000: 0.222644\n", + "Cost after iteration 6000: 0.219731\n", + "Cost after iteration 7000: 0.217504\n", + "Cost after iteration 8000: 0.219475\n", + "Cost after iteration 9000: 0.218612\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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5jpYXdsQkddB45PzD8K5B9+LWJhAIAU32WjCICJYFtWrv7e6A1jcWEe4pPsfC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\noBsKkluVExGEBsMoDJHodiXT88iJCYJS6kf2el5Efg74ceADanejXc3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Build a model with a n_h-dimensional hidden layer\n", + "parameters = nn_model(X, Y, n_h = 4, num_iterations = 10000, print_cost=True)\n", + "\n", + "# Plot the decision boundary\n", + "plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n", + "plt.title(\"Decision Boundary for hidden layer size \" + str(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Cost after iteration 9000** 0.218607
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 90%\n" + ] + } + ], + "source": [ + "# Print accuracy\n", + "predictions = predict(parameters, X)\n", + "print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
**Accuracy** 90%
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Accuracy is really high compared to Logistic Regression. The model has learnt the leaf patterns of the flower! Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression. \n", + "\n", + "Now, let's try out several hidden layer sizes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.6 - Tuning hidden layer size (optional/ungraded exercise) ###\n", + "\n", + "Run the following code. It may take 1-2 minutes. You will observe different behaviors of the model for various hidden layer sizes." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for 1 hidden units: 67.5 %\n", + "Accuracy for 2 hidden units: 67.25 %\n", + "Accuracy for 3 hidden units: 90.75 %\n", + "Accuracy for 4 hidden units: 90.5 %\n", + "Accuracy for 5 hidden units: 91.25 %\n", + "Accuracy for 10 hidden units: 90.25 %\n", + "Accuracy for 20 hidden units: 90.0 %\n" + ] + }, + { + "data": { + "image/png": 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sL3jN5S+GAecuxshvBqyvdu7EhuF8Oi/GQRAm0EmNmtaqet+zCS0LJi44+J5G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\nIoUascUK9jpujhRqdTdXPeySw2JnmHzX3nXzzo8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aipPZq1xIPtDcxGgpP6XNH2xsVSMj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YAAAg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\nYPkHqVSG9oj7Dk8Xv92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TcBxNteJhmtHbu7RaDreE7wcpSrF4+FjjjctXr9zEN8zgLKGB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3/0I7jrxvcRyeYwf+sWFMOo7R9wJIrs2Bh2Lsz39X31sAJblaAYjmdmDQNQSETw1MrjePuX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\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BjFHE4hSt9mTaS6noAwRYWNInM7gRLCzpSKbZ0DmYDpwqOK5yzOfGt6r5odoD5Jwq3s48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\n9j6VUiJVybjVCg5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GVMmC2XTh6gpqeXOoOCIAaCOKGAk3u6FuPriZweLjCjTb77qZUYLj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\nIqtF0W2TgZDN0KgXfEspyWUd0lnJ3MA4aiKIabq8vPcuL1vbZTuKWcW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qahEo0OPC73mB3wnUtR8DGsFPI+T8koweHCAj78+38IRw+iFEuCcA5OKZJ7m8inBmuMPumsr67h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\nrGojvVlDtGp3fL7pwYX5c0hEREQ0KFyRpVA3dIpWbYw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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This may take about 2 minutes to run\n", + "\n", + "plt.figure(figsize=(16, 32))\n", + "hidden_layer_sizes = [1, 2, 3, 4, 5, 10, 20]\n", + "for i, n_h in enumerate(hidden_layer_sizes):\n", + " plt.subplot(5, 2, i+1)\n", + " plt.title('Hidden Layer of size %d' % n_h)\n", + " parameters = nn_model(X, Y, n_h, num_iterations = 5000)\n", + " plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n", + " predictions = predict(parameters, X)\n", + " accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)\n", + " print (\"Accuracy for {} hidden units: {} %\".format(n_h, accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interpretation**:\n", + "- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. \n", + "- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to fits the data well without also incurring noticable overfitting.\n", + "- You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Optional questions**:\n", + "\n", + "**Note**: Remember to submit the assignment but clicking the blue \"Submit Assignment\" button at the upper-right. \n", + "\n", + "Some optional/ungraded questions that you can explore if you wish: \n", + "- What happens when you change the tanh activation for a sigmoid activation or a ReLU activation?\n", + "- Play with the learning_rate. What happens?\n", + "- What if we change the dataset? (See part 5 below!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**You've learnt to:**\n", + "- Build a complete neural network with a hidden layer\n", + "- Make a good use of a non-linear unit\n", + "- Implemented forward propagation and backpropagation, and trained a neural network\n", + "- See the impact of varying the hidden layer size, including overfitting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice work! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5) Performance on other datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "If you want, you can rerun the whole notebook (minus the dataset part) for each of the following datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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NLQTnm7Qo+ruzwMaBuf9XbumEqmIK8kMxlPJfyDJn2nQm8robWL8jhXde3sAH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\ntnrrEVQmefszyEo+gHlGmcPVy/aTsifTVx67iJVL9/PC1Dk4s/PI2Z2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Datasets\n", + "noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()\n", + "\n", + "datasets = {\"noisy_circles\": noisy_circles,\n", + " \"noisy_moons\": noisy_moons,\n", + " \"blobs\": blobs,\n", + " \"gaussian_quantiles\": gaussian_quantiles}\n", + "\n", + "### START CODE HERE ### (choose your dataset)\n", + "dataset = \"noisy_circles\"\n", + "### END CODE HERE ###\n", + "\n", + "X, Y = datasets[dataset]\n", + "X, Y = X.T, Y.reshape(1, Y.shape[0])\n", + "\n", + "# make blobs binary\n", + "if dataset == \"blobs\":\n", + " Y = Y%2\n", + "\n", + "# Visualize the data\n", + "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congrats on finishing this Programming Assignment!\n", + "\n", + "Reference:\n", + "- http://scs.ryerson.ca/~aharley/neural-networks/\n", + "- http://cs231n.github.io/neural-networks-case-study/" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "neural-networks-deep-learning", + "graded_item_id": "wRuwL", + "launcher_item_id": "NI888" + }, + "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": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week three/assignment3/images/classification_kiank.png b/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week three/assignment3/images/classification_kiank.png new file mode 100644 index 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0000000000000000000000000000000000000000..3009f265ead51ed9b8f2601d6251046b446c0862 --- /dev/null +++ b/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week three/assignment3/planar_utils.py @@ -0,0 +1,66 @@ +import matplotlib.pyplot as plt +import numpy as np +import sklearn +import sklearn.datasets +import sklearn.linear_model + +def plot_decision_boundary(model, X, y): + # Set min and max values and give it some padding + x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1 + y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1 + h = 0.01 + # Generate a grid of points with distance h between them + xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) + # Predict the function value for the whole grid + Z = model(np.c_[xx.ravel(), yy.ravel()]) + Z = Z.reshape(xx.shape) + # Plot the contour and training examples + plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) + plt.ylabel('x2') + plt.xlabel('x1') + plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral) + + +def sigmoid(x): + """ + Compute the sigmoid of x + + Arguments: + x -- A scalar or numpy array of any size. + + Return: + s -- sigmoid(x) + """ + s = 1/(1+np.exp(-x)) + return s + +def load_planar_dataset(): + np.random.seed(1) + m = 400 # number of examples + N = int(m/2) # number of points per class + D = 2 # dimensionality + X = np.zeros((m,D)) # data matrix where each row is a single example + Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue) + a = 4 # maximum ray of the flower + + for j in range(2): + ix = range(N*j,N*(j+1)) + t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta + r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius + X[ix] = np.c_[r*np.sin(t), r*np.cos(t)] + Y[ix] = j + + X = X.T + Y = Y.T + + return X, Y + +def load_extra_datasets(): + N = 200 + noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3) + noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2) + blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6) + gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None) + no_structure = np.random.rand(N, 2), np.random.rand(N, 2) + + return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure \ No newline at end of file diff --git a/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week three/assignment3/testCases.py b/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week three/assignment3/testCases.py new file mode 100644 index 0000000000000000000000000000000000000000..b0550f1c491506216d9e1b27be8c8073c489bbbc --- /dev/null +++ b/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week three/assignment3/testCases.py @@ -0,0 +1,119 @@ +import numpy as np + +def layer_sizes_test_case(): + np.random.seed(1) + X_assess = np.random.randn(5, 3) + Y_assess = np.random.randn(2, 3) + return X_assess, Y_assess + +def initialize_parameters_test_case(): + n_x, n_h, n_y = 2, 4, 1 + return n_x, n_h, n_y + +def forward_propagation_test_case(): + np.random.seed(1) + X_assess = np.random.randn(2, 3) + + parameters = {'W1': np.array([[-0.00416758, -0.00056267], + [-0.02136196, 0.01640271], + [-0.01793436, -0.00841747], + [ 0.00502881, -0.01245288]]), + 'W2': np.array([[-0.01057952, -0.00909008, 0.00551454, 0.02292208]]), + 'b1': np.array([[ 0.], + [ 0.], + [ 0.], + [ 0.]]), + 'b2': np.array([[ 0.]])} + + return X_assess, parameters + +def compute_cost_test_case(): + np.random.seed(1) + Y_assess = np.random.randn(1, 3) + parameters = {'W1': np.array([[-0.00416758, -0.00056267], + [-0.02136196, 0.01640271], + [-0.01793436, -0.00841747], + [ 0.00502881, -0.01245288]]), + 'W2': np.array([[-0.01057952, -0.00909008, 0.00551454, 0.02292208]]), + 'b1': np.array([[ 0.], + [ 0.], + [ 0.], + [ 0.]]), + 'b2': np.array([[ 0.]])} + + a2 = (np.array([[ 0.5002307 , 0.49985831, 0.50023963]])) + + return a2, Y_assess, parameters + +def backward_propagation_test_case(): + np.random.seed(1) + X_assess = np.random.randn(2, 3) + Y_assess = np.random.randn(1, 3) + parameters = {'W1': np.array([[-0.00416758, -0.00056267], + [-0.02136196, 0.01640271], + [-0.01793436, -0.00841747], + [ 0.00502881, -0.01245288]]), + 'W2': np.array([[-0.01057952, -0.00909008, 0.00551454, 0.02292208]]), + 'b1': np.array([[ 0.], + [ 0.], + [ 0.], + [ 0.]]), + 'b2': np.array([[ 0.]])} + + cache = {'A1': np.array([[-0.00616578, 0.0020626 , 0.00349619], + [-0.05225116, 0.02725659, -0.02646251], + [-0.02009721, 0.0036869 , 0.02883756], + [ 0.02152675, -0.01385234, 0.02599885]]), + 'A2': np.array([[ 0.5002307 , 0.49985831, 0.50023963]]), + 'Z1': np.array([[-0.00616586, 0.0020626 , 0.0034962 ], + [-0.05229879, 0.02726335, -0.02646869], + [-0.02009991, 0.00368692, 0.02884556], + [ 0.02153007, -0.01385322, 0.02600471]]), + 'Z2': np.array([[ 0.00092281, -0.00056678, 0.00095853]])} + return parameters, cache, X_assess, Y_assess + +def update_parameters_test_case(): + parameters = {'W1': np.array([[-0.00615039, 0.0169021 ], + [-0.02311792, 0.03137121], + [-0.0169217 , -0.01752545], + [ 0.00935436, -0.05018221]]), + 'W2': np.array([[-0.0104319 , -0.04019007, 0.01607211, 0.04440255]]), + 'b1': np.array([[ -8.97523455e-07], + [ 8.15562092e-06], + [ 6.04810633e-07], + [ -2.54560700e-06]]), + 'b2': np.array([[ 9.14954378e-05]])} + + grads = {'dW1': np.array([[ 0.00023322, -0.00205423], + [ 0.00082222, -0.00700776], + [-0.00031831, 0.0028636 ], + [-0.00092857, 0.00809933]]), + 'dW2': np.array([[ -1.75740039e-05, 3.70231337e-03, -1.25683095e-03, + -2.55715317e-03]]), + 'db1': np.array([[ 1.05570087e-07], + [ -3.81814487e-06], + [ -1.90155145e-07], + [ 5.46467802e-07]]), + 'db2': np.array([[ -1.08923140e-05]])} + return parameters, grads + +def nn_model_test_case(): + np.random.seed(1) + X_assess = np.random.randn(2, 3) + Y_assess = np.random.randn(1, 3) + return X_assess, Y_assess + +def predict_test_case(): + np.random.seed(1) + X_assess = np.random.randn(2, 3) + parameters = {'W1': np.array([[-0.00615039, 0.0169021 ], + [-0.02311792, 0.03137121], + [-0.0169217 , -0.01752545], + [ 0.00935436, -0.05018221]]), + 'W2': np.array([[-0.0104319 , -0.04019007, 0.01607211, 0.04440255]]), + 'b1': np.array([[ -8.97523455e-07], + [ 8.15562092e-06], + [ 6.04810633e-07], + [ -2.54560700e-06]]), + 'b2': np.array([[ 9.14954378e-05]])} + return parameters, X_assess diff --git a/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week two/__MACOSX/assignment2/._.DS_Store b/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week two/__MACOSX/assignment2/._.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..09fa6bdda3a49951cf3fb7aa68796ee7d5c71310 Binary files /dev/null and b/Coursera-deeplearning/fisrt class-Neural Networks and Deep Learning/week two/__MACOSX/assignment2/._.DS_Store differ diff 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