1.0 - TensorFlow model
导入相关依赖包。
import numpy as np import h5pyimport matplotlib.pyplot as pltimport scipyfrom PIL import Imagefrom scipy import ndimageimport tensorflow as tf from tensorflow.python.framework import opsfrom cnn_utils import *
初始化全局变量。
%matplotlib inlinenp.random.seed(1)
导入数据集。
# Loading the data (signs)X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
输出一张样例图片预览。
# Example of a pictureindex = 6plt.imshow(X_train_orig[index])print("y = " + str(np.squeeze(Y_train_orig[:, index]))) Result:
y = 2
数据预处理。将输入图像像素除以255进行归一化，将标签数据扩充为$one\_hot$编码，并且查看数据规模。
X_train = X_train_orig / 255.X_test = X_test_orig / 255.Y_train = convert_to_one_hot(Y_train_orig, 6).TY_test = convert_to_one_hot(Y_test_orig, 6).Tprint("number of training examples = " + str(X_train.shape[0]))print("number of test examples = " + str(X_test.shape[0]))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))conv_layers = {} Result:number of training examples = 1080number of test examples = 120X_train shape: (1080, 64, 64, 3)Y_train shape: (1080, 6)X_test shape: (120, 64, 64, 3)Y_test shape: (120, 6)
1.1 - Create placeholders
# GRADED FUNCTION: create_placeholdersdef create_placeholders(n_H0, n_W0, n_C0, n_y): """ Creates the placeholders for the tensorflow session. Arguments: n_H0 -- scalar, height of an input image n_W0 -- scalar, width of an input image n_C0 -- scalar, number of channels of the input n_y -- scalar, number of classes Returns: X -- placeholder for the data input, of shape [None, n_H0, n_W0, n_C0] and dtype "float" Y -- placeholder for the input labels, of shape [None, n_y] and dtype "float" """ ### START CODE HERE ### (≈2 lines) # tf.placeholder第一个参数为类型，第二个参数位数据规模，可通过name参数指定变量名 X = tf.placeholder(tf.float32, [None, n_H0, n_W0, n_C0]) Y = tf.placeholder(tf.float32, [None, n_y]) ### END CODE HERE ### return X, Y
X, Y = create_placeholders(64, 64, 3, 6)print ("X = " + str(X))print ("Y = " + str(Y)) Result:X = Tensor("Placeholder_2:0", shape=(?, 64, 64, 3), dtype=float32)Y = Tensor("Placeholder_3:0", shape=(?, 6), dtype=float32) 1.2 - Initialize parameters
通过$tf.contrib.layers.xavier\_initializer(seed=0)$初始化权重/过滤器/卷积核$W_1$和$W_2$，不用关心$bias\_variables$的初始化，因为TensorFlow方法会帮助我们做这件事，所以我们只需要初始化卷积方法的卷积核。TensorFlow也会自动初始化全连接层的参数。
使用TensorFlow初始化参数有如下语法：
W = tf.get_variable("W", [1,2,3,4], initializer = ...) # GRADED FUNCTION: initialize_parametersdef initialize_parameters(): """ Initializes weight parameters to build a neural network with tensorflow. The shapes are: W1 : [4, 4, 3, 8] W2 : [2, 2, 8, 16] Returns: parameters -- a dictionary of tensors containing W1, W2 """ tf.set_random_seed(1) # so that your "random" numbers match ours ### START CODE HERE ### (approx. 2 lines of code) W1 = tf.get_variable("W1", [4, 4, 3, 8], initializer=tf.contrib.layers.xavier_initializer(seed=0)) W2 = tf.get_variable("W2", [2, 2, 8, 16], initializer=tf.contrib.layers.xavier_initializer(seed=0)) ### END CODE HERE ### parameters = { "W1": W1, "W2": W2} return parameters tf.reset_default_graph()with tf.Session() as sess_test: parameters = initialize_parameters() init = tf.global_variables_initializer() sess_test.run(init) print("W1 = " + str(parameters["W1"].eval()[1,1,1])) print("W2 = " + str(parameters["W2"].eval()[1,1,1])) Result:W1 = [ 0.00131723 0.14176141 -0.04434952 0.09197326 0.14984085 -0.03514394 -0.06847463 0.05245192]W2 = [-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058 -0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228 -0.22779644 -0.1601823 -0.16117483 -0.10286498]
1.3 - Forward propagation
在TensorFlow中，可以通过如下一些函数（语法）实现前向传播。
tf.nn.conv2d(X,W1, strides = [1,s,s,1], padding = 'SAME')tf.nn.max_pool(A, ksize = [1,f,f,1], strides = [1,s,s,1], padding = 'SAME')tf.nn.relu(Z1)tf.contrib.layers.flatten(P)tf.contrib.layers.fully_connected(F, num_outputs)
注意到，使用$tf.contrib.layers.fully\_connected$将会自动初始化全连接层的参数（权重），并且在训练模型的时候训练参数。因此我们无需初始化其参数。
实现方法$forward_propagation$，使其构造模型：CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED。
# GRADED FUNCTION: forward_propagationdef forward_propagation(X, parameters): """ Implements the forward propagation for the model: CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED Arguments: X -- input dataset placeholder, of shape (input size, number of examples) parameters -- python dictionary containing your parameters "W1", "W2" 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'] W2 = parameters['W2'] ### START CODE HERE ### # CONV2D: stride of 1, padding 'SAME' Z1 = tf.nn.conv2d(X, W1, strides=[1, 1, 1, 1], padding="SAME") # RELU A1 = tf.nn.relu(Z1) # MAXPOOL: window 8x8, sride 8, padding 'SAME' P1 = tf.nn.max_pool(A1, ksize=[1, 8, 8, 1], strides=[1, 8, 8, 1], padding="SAME") # CONV2D: filters W2, stride 1, padding 'SAME' Z2 = tf.nn.conv2d(P1, W2, strides=[1, 1, 1, 1], padding="SAME") # RELU A2 = tf.nn.relu(Z2) # MAXPOOL: window 4x4, stride 4, padding 'SAME' P2 = tf.nn.max_pool(A2, ksize=[1, 4, 4, 1], strides=[1, 4, 4, 1], padding="SAME") # FLATTEN P2 = tf.contrib.layers.flatten(P2) # FULLY-CONNECTED without non-linear activation function (not not call softmax). # 6 neurons in output layer. Hint: one of the arguments should be "activation_fn=None" Z3 = tf.contrib.layers.fully_connected(P2, 6, activation_fn=None) ### END CODE HERE ### return Z3
tf.reset_default_graph()with tf.Session() as sess: np.random.seed(1) X, Y = create_placeholders(64, 64, 3, 6) parameters = initialize_parameters() Z3 = forward_propagation(X, parameters) init = tf.global_variables_initializer() sess.run(init) a = sess.run(Z3, {X: np.random.randn(2,64,64,3), Y: np.random.randn(2,6)}) print("Z3 = " + str(a)) Result:Z3 = [[ 1.44169843 -0.24909666 5.45049906 -0.26189619 -0.20669907 1.36546707] [ 1.40708458 -0.02573211 5.08928013 -0.48669922 -0.40940708 1.26248586]]
1.3 - Compute cost
# GRADED FUNCTION: compute_cost def 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 """ ### START CODE HERE ### (1 line of code) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=Z3, labels=Y)) ### END CODE HERE ### return cost
tf.reset_default_graph()with tf.Session() as sess: np.random.seed(1) X, Y = create_placeholders(64, 64, 3, 6) parameters = initialize_parameters() Z3 = forward_propagation(X, parameters) cost = compute_cost(Z3, Y) init = tf.global_variables_initializer() sess.run(init) a = sess.run(cost, {X: np.random.randn(4,64,64,3), Y: np.random.randn(4,6)}) print("cost = " + str(a)) Result:cost = 4.66487
1.4 - Model
整合上面实现了的有用的方法去构建一个模型在SIGNS数据集上进行训练。
将有几个步骤：
* 1 create placeholders
* 2 initialize parameters
* 3 forward propagate
* 4 compute the cost
* 5 create an optimizer
最后，创建一个$session$然后循环$num\_epochs$，每一次获得一个$mini-batches$并且通过模型预测出结果计算损失并且优化他们。
# GRADED FUNCTION: modeldef model(X_train, Y_train, X_test, Y_test, learning_rate = 0.009, num_epochs = 100, minibatch_size = 64, print_cost = True): """ Implements a three-layer ConvNet in Tensorflow: CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED Arguments: X_train -- training set, of shape (None, 64, 64, 3) Y_train -- test set, of shape (None, n_y = 6) X_test -- training set, of shape (None, 64, 64, 3) Y_test -- test set, of shape (None, n_y = 6) 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: train_accuracy -- real number, accuracy on the train set (X_train) test_accuracy -- real number, testing accuracy on the test set (X_test) 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 results consistent (tensorflow seed) seed = 3 # to keep results consistent (numpy seed) (m, n_H0, n_W0, n_C0) = X_train.shape n_y = Y_train.shape[1] costs = [] # To keep track of the cost # Create Placeholders of the correct shape ### START CODE HERE ### (1 line) # 1 create placeholders X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y) ### END CODE HERE ### # Initialize parameters ### START CODE HERE ### (1 line) # 2 initialize parameters parameters = initialize_parameters() ### END CODE HERE ### # Forward propagation: Build the forward propagation in the tensorflow graph ### START CODE HERE ### (1 line) # 3 forward propagate Z3 = forward_propagation(X, parameters) ### END CODE HERE ### # Cost function: Add cost function to tensorflow graph ### START CODE HERE ### (1 line) # 4 compute the cost cost = compute_cost(Z3, Y) ### END CODE HERE ### # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost. ### START CODE HERE ### (1 line) # 5 create an optimizer optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) ### END CODE HERE ### # Initialize all the variables globally # 初始化全部变量 init = tf.global_variables_initializer() # Start the session to compute the tensorflow graph # 创建一个会话，并开始执行 with tf.Session() as sess: # Run the initialization sess.run(init) # Do the training loop # 训练num_epoches轮 for epoch in range(num_epochs): minibatch_cost = 0. # 保存当前mini_batch的cost 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) # 随机从训练集中获取一个mini_batch for minibatch in minibatches: # 遍历mini_batch中的训练数据，计算损失 # 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) _ , temp_cost = sess.run([optimizer, cost], feed_dict={X:minibatch_X, Y:minibatch_Y}) ### END CODE HERE ### minibatch_cost += temp_cost / num_minibatches # Print the cost every epoch if print_cost == True and epoch % 5 == 0: # 每5轮输出一次当前损失值 print ("Cost after epoch %i: %f" % (epoch, minibatch_cost)) if print_cost == True and epoch % 1 == 0: # 取偶数轮数的损失值来画折线图 costs.append(minibatch_cost) # plot the cost # 画出损失值折线图 plt.plot(np.squeeze(costs)) plt.ylabel('cost') plt.xlabel('iterations (per tens)') plt.title("Learning rate =" + str(learning_rate)) plt.show() # Calculate the correct predictions # 计算预测准确率 predict_op = tf.argmax(Z3, 1) correct_prediction = tf.equal(predict_op, tf.argmax(Y, 1)) # Calculate accuracy on the test set # 计算训练集准确率以及测试集准确率并且打印出来 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print(accuracy) train_accuracy = accuracy.eval({X: X_train, Y: Y_train}) test_accuracy = accuracy.eval({X: X_test, Y: Y_test}) print("Train Accuracy:", train_accuracy) print("Test Accuracy:", test_accuracy) return train_accuracy, test_accuracy, parameters _, _, parameters = model(X_train, Y_train, X_test, Y_test)
Result:
fname = "images/thumbs_up.jpg"image = np.array(ndimage.imread(fname, flatten=False))my_image = scipy.misc.imresize(image, size=(64,64))plt.imshow(my_image)
