第二课第三周 deeplaerning.ai编程实现

前面该课程关于tensorflow的具体函数填写已经不再细说，在本程序中，仅仅包含的是第二个关于用tensorflow实现一个图片手势判断的三层多分类神经网络，该次代码分为两个部分，第一部分是原有代码，存在文件tf_utils.py中，第二部分代码时编程作业的实现。

第一部分代码如下：

import h5pyimport numpy as npimport tensorflow as tfimport mathdef load_dataset():    train_dataset = h5py.File('datasets/train_signs.h5', "r")    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels    test_dataset = h5py.File('datasets/test_signs.h5', "r")    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels    classes = np.array(test_dataset["list_classes"][:]) # the list of classes    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classesdef random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):    """    Creates a list of random minibatches from (X, Y)    Arguments:    X -- input data, of shape (input size, number of examples)    Y -- true "label" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)    mini_batch_size - size of the mini-batches, integer    seed -- this is only for the purpose of grading, so that you're "random minibatches are the same as ours.    Returns:    mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)    """    m = X.shape[1]                  # number of training examples    mini_batches = []    np.random.seed(seed)    # Step 1: Shuffle (X, Y)    permutation = list(np.random.permutation(m))    shuffled_X = X[:, permutation]    shuffled_Y = Y[:, permutation].reshape((Y.shape[0],m))    # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.    num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning    for k in range(0, num_complete_minibatches):        mini_batch_X = shuffled_X[:, k * mini_batch_size : k * mini_batch_size + mini_batch_size]        mini_batch_Y = shuffled_Y[:, k * mini_batch_size : k * mini_batch_size + mini_batch_size]        mini_batch = (mini_batch_X, mini_batch_Y)        mini_batches.append(mini_batch)    # Handling the end case (last mini-batch < mini_batch_size)    if m % mini_batch_size != 0:        mini_batch_X = shuffled_X[:, num_complete_minibatches * mini_batch_size : m]        mini_batch_Y = shuffled_Y[:, num_complete_minibatches * mini_batch_size : m]        mini_batch = (mini_batch_X, mini_batch_Y)        mini_batches.append(mini_batch)    return mini_batchesdef convert_to_one_hot(Y, C):    Y = np.eye(C)[Y.reshape(-1)].T    return Ydef predict(X, parameters):    W1 = tf.convert_to_tensor(parameters["W1"])    b1 = tf.convert_to_tensor(parameters["b1"])    W2 = tf.convert_to_tensor(parameters["W2"])    b2 = tf.convert_to_tensor(parameters["b2"])    W3 = tf.convert_to_tensor(parameters["W3"])    b3 = tf.convert_to_tensor(parameters["b3"])    params = {"W1": W1,              "b1": b1,              "W2": W2,              "b2": b2,              "W3": W3,              "b3": b3}    x = tf.placeholder("float", [12288, 1])    z3 = forward_propagation_for_predict(x, params)    p = tf.argmax(z3)    sess = tf.Session()    prediction = sess.run(p, feed_dict = {x: X})    return predictiondef forward_propagation_for_predict(X, parameters):    """    Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX    Arguments:    X -- input dataset placeholder, of shape (input size, number of examples)    parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3"                  the shapes are given in initialize_parameters    Returns:    Z3 -- the output of the last LINEAR unit    """    # Retrieve the parameters from the dictionary "parameters"     W1 = parameters['W1']    b1 = parameters['b1']    W2 = parameters['W2']    b2 = parameters['b2']    W3 = parameters['W3']    b3 = parameters['b3']                                                            # Numpy Equivalents:    Z1 = tf.add(tf.matmul(W1, X), b1)                      # Z1 = np.dot(W1, X) + b1    A1 = tf.nn.relu(Z1)                                    # A1 = relu(Z1)    Z2 = tf.add(tf.matmul(W2, A1), b2)                     # Z2 = np.dot(W2, a1) + b2    A2 = tf.nn.relu(Z2)                                    # A2 = relu(Z2)    Z3 = tf.add(tf.matmul(W3, A2), b3)                     # Z3 = np.dot(W3,Z2) + b3    return Z3

第二部分代码如下：

import mathimport numpy as npimport h5pyimport matplotlib.pyplot as pltimport tensorflow as tffrom tensorflow.python.framework import opsfrom tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict#%matplotlib inlinenp.random.seed(1)X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()# Flatten the training and test imagesX_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).TX_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T# Normalize image vectorsx_train = X_train_flatten/255.x_test = X_test_flatten/255.# Convert training and test labels to one hot matricesy_train = convert_to_one_hot(Y_train_orig, 6)y_test = convert_to_one_hot(Y_test_orig, 6)print ("number of training examples = " + str(x_train.shape[1]))print ("number of test examples = " + str(x_test.shape[1]))print ("X_train shape: " + str(x_train.shape))print ("Y_train shape: " + str(y_train.shape))print ("X_test shape: " + str(x_test.shape))print ("Y_test shape: " + str(y_test.shape))def create_placeholders(n_x, n_y):    """    Creates the placeholders for the tensorflow session.    Arguments:    n_x -- scalar, size of an image vector (num_px * num_px = 64 * 64 * 3 = 12288)    n_y -- scalar, number of classes (from 0 to 5, so -> 6)    Returns:    X -- placeholder for the data input, of shape [n_x, None] and dtype "float"    Y -- placeholder for the input labels, of shape [n_y, None] and dtype "float"    Tips:    - You will use None because it let's us be flexible on the number of examples you will for the placeholders.      In fact, the number of examples during test/train is different.    """    ### START CODE HERE ### (approx. 2 lines)    X = tf.placeholder(tf.float32,[n_x,None])    Y = tf.placeholder(tf.float32,[n_y,None])    ### END CODE HERE ###    return X, Ydef initialize_parameters():    """    Initializes parameters to build a neural network with tensorflow. The shapes are:                        W1 : [25, 12288]                        b1 : [25, 1]                        W2 : [12, 25]                        b2 : [12, 1]                        W3 : [6, 12]                        b3 : [6, 1]    Returns:    parameters -- a dictionary of tensors containing W1, b1, W2, b2, W3, b3    """    tf.set_random_seed(1)                   # so that your "random" numbers match ours    ### START CODE HERE ### (approx. 6 lines of code)    #W1 = tf.get_variable("W1", [25,12288], initializer = tf.contrib.layers.xavier_initializer(seed = 1))    W1 = tf.get_variable('W1', [25,12288], initializer = tf.contrib.layers.xavier_initializer(seed = 1))    b1 = tf.get_variable("b1",[25,1],initializer=tf.zeros_initializer())    W2 = tf.get_variable("W2",[12,25],initializer=tf.contrib.layers.xavier_initializer(seed=1))    b2 = tf.get_variable("b2",[12,1],initializer=tf.zeros_initializer())    W3 = tf.get_variable("W3",[6,12],initializer=tf.contrib.layers.xavier_initializer(seed=1))    b3 = tf.get_variable("b3",[6,1],initializer=tf.zeros_initializer())    ### END CODE HERE ###    parameters = {"W1": W1,                  "b1": b1,                  "W2": W2,                  "b2": b2,                  "W3": W3,                  "b3": b3}    return parameters# GRADED FUNCTION: forward_propagationdef forward_propagation(X, parameters):    """    Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX    Arguments:    X -- input dataset placeholder, of shape (input size, number of examples)    parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3"                  the shapes are given in initialize_parameters    Returns:    Z3 -- the output of the last LINEAR unit    """    # Retrieve the parameters from the dictionary "parameters"     W1 = parameters['W1']    b1 = parameters['b1']    W2 = parameters['W2']    b2 = parameters['b2']    W3 = parameters['W3']    b3 = parameters['b3']    ### START CODE HERE ### (approx. 5 lines)          # Numpy Equivalents:    Z1 = tf.add(tf.matmul(W1,X),b1)                                              # Z1 = np.dot(W1, X) + b1    A1 = tf.nn.relu(Z1)                                            # A1 = relu(Z1)    Z2 = tf.add(tf.matmul(W2,A1),b2)                                              # Z2 = np.dot(W2, a1) + b2    A2 = tf.nn.relu(Z2)                                             # A2 = relu(Z2)    Z3 = tf.add(tf.matmul(W3,A2),b3)                                              # Z3 = np.dot(W3,Z2) + b3    ### END CODE HERE ###    return Z3def compute_cost(Z3, Y):    """    Computes the cost    Arguments:    Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)    Y -- "true" labels vector placeholder, same shape as Z3    Returns:    cost - Tensor of the cost function    """    # to fit the tensorflow requirement for tf.nn.softmax_cross_entropy_with_logits(...,...)  转置    logits = tf.transpose(Z3)               #m * nL(num of classes )   [batch_size  num_classes]    labels = tf.transpose(Y)                #m * nL( num of classes)   [batch_size  num_classes]    ### START CODE HERE ### (1 line of code)    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels))    ### END CODE HERE ###    return costdef model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,          num_epochs = 1500, minibatch_size = 32, print_cost = True):    """    Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.    Arguments:    X_train -- training set, of shape (input size = 12288, number of training examples = 1080)    Y_train -- test set, of shape (output size = 6, number of training examples = 1080)    X_test -- training set, of shape (input size = 12288, number of training examples = 120)    Y_test -- test set, of shape (output size = 6, number of test examples = 120)    learning_rate -- learning rate of the optimization    num_epochs -- number of epochs of the optimization loop    minibatch_size -- size of a minibatch    print_cost -- True to print the cost every 100 epochs    Returns:    parameters -- parameters learnt by the model. They can then be used to predict.    """    ops.reset_default_graph()                         # to be able to rerun the model without overwriting tf variables    tf.set_random_seed(1)                             # to keep consistent results    seed = 3                                          # to keep consistent results    (n_x, m) = X_train.shape                          # (n_x: input size, m : number of examples in the train set)    n_y = Y_train.shape[0]                            # n_y : output size    costs = []                                        # To keep track of the cost    # Create Placeholders of shape (n_x, n_y)    ### START CODE HERE ### (1 line)    X, Y = create_placeholders(n_x, n_y)    #mini_batch = tf.placeholder([n_x,n_y])    ### END CODE HERE ###    # Initialize parameters    ### START CODE HERE ### (1 line)    parameters = initialize_parameters()    ### END CODE HERE ###    # Forward propagation: Build the forward propagation in the tensorflow graph    ### START CODE HERE ### (1 line)    Z3 = forward_propagation(X,parameters)    ### END CODE HERE ###    # Cost function: Add cost function to tensorflow graph    ### START CODE HERE ### (1 line)    cost = compute_cost(Z3,Y)    ### END CODE HERE ###    # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.    ### START CODE HERE ### (1 line)    optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)    ### END CODE HERE ###    # Initialize all the variables    init = tf.global_variables_initializer()    # Start the session to compute the tensorflow graph    with tf.Session() as sess:        # Run the initialization        sess.run(init)        # Do the training loop        for epoch in range(num_epochs):            epoch_cost = 0.                       # Defines a cost related to an epoch            num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set            seed = seed + 1            minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)            for minibatch in minibatches:                # Select a minibatch                (minibatch_X, minibatch_Y) = minibatch                # IMPORTANT: The line that runs the graph on a minibatch.                # Run the session to execute the "optimizer" and the "cost", the feedict should contain a minibatch for (X,Y).                ### START CODE HERE ### (1 line)                _ , minibatch_cost = sess.run([optimizer,cost],feed_dict={X:minibatch_X,Y:minibatch_Y})                ### END CODE HERE ###                epoch_cost += minibatch_cost / num_minibatches            # Print the cost every epoch            if print_cost == True and epoch % 100 == 0:                print ("Cost after epoch %i: %f" % (epoch, epoch_cost))            if print_cost == True and epoch % 5 == 0:                costs.append(epoch_cost)        # plot the cost        plt.plot(np.squeeze(costs))        plt.ylabel('cost')        plt.xlabel('iterations (per tens)')        plt.title("Learning rate =" + str(learning_rate))        plt.show()        # lets save the parameters in a variable        parameters = sess.run(parameters)        print ("Parameters have been trained!")        # Calculate the correct predictions        correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))        # Calculate accuracy on the test set        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))        print ("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train}))        print ("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))        return parametersparameters = model(x_train, y_train, x_test, y_test)