在上一講中，我們學習了如何利用numpy手動搭建卷積神經網絡。但在實際的圖像識別中，使用numpy去手寫 CNN 未免有些吃力不討好。在 DNN 的學習中，我們也是在手動搭建之后利用Tensorflow去重新實現一遍，一來為了能夠對神經網絡的傳播機制能夠理解更加透徹，二來也是為了更加高效使用開源框架快速搭建起深度學習項目。本節就繼續和大家一起學習如何利用Tensorflow搭建一個卷積神經網絡。

我們繼續以 NG 課題組提供的 sign 手勢數據集為例，學習如何通過Tensorflow快速搭建起一個深度學習項目。數據集標簽共有零到五總共 6 類標簽，示例如下：

先對數據進行簡單的預處理并查看訓練集和測試集維度：

X_train = X_train_orig/255.X_test = X_test_orig/255.Y_train = convert_to_one_hot(Y_train_orig, 6).T Y_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))

可見我們總共有 1080 張 64643 訓練集圖像，120 張 64643 的測試集圖像，共有 6 類標簽。下面我們開始搭建過程。

創建placeholder

首先需要為訓練集預測變量和目標變量創建占位符變量placeholder，定義創建占位符變量函數：

def 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" """ X = tf.placeholder(tf.float32, shape=(None, n_H0, n_W0, n_C0), name='X') Y = tf.placeholder(tf.float32, shape=(None, n_y), name='Y') return X, Y

參數初始化

然后需要對濾波器權值參數進行初始化：

def initialize_parameters(): """ Initializes weight parameters to build a neural network with tensorflow. Returns: parameters -- a dictionary of tensors containing W1, W2 """ tf.set_random_seed(1) 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)) parameters = {"W1": W1, "W2": W2} return parameters

執行卷積網絡的前向傳播過程

前向傳播過程如下所示：CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED

可見我們要搭建的是一個典型的 CNN 過程，經過兩次的卷積-relu激活-最大池化，然后展開接上一個全連接層。利用Tensorflow搭建上述傳播過程如下：

def forward_propagation(X, parameters): """ Implements the forward propagation for the model 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'] # 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) Z3 = tf.contrib.layers.fully_connected(P2, 6, activation_fn = None) return Z3

計算當前損失

在Tensorflow中計算損失函數非常簡單，一行代碼即可：

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 """ cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=Z3, labels=Y)) return cost

定義好上述過程之后，就可以封裝整體的訓練過程模型。可能你會問為什么沒有反向傳播，這里需要注意的是Tensorflow幫助我們自動封裝好了反向傳播過程，無需我們再次定義，在實際搭建過程中我們只需將前向傳播的網絡結構定義清楚即可。

封裝模型

def 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() tf.set_random_seed(1) seed = 3 (m, n_H0, n_W0, n_C0) = X_train.shape n_y = Y_train.shape[1] costs = [] # Create Placeholders of the correct shape X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y) # Initialize parameters parameters = initialize_parameters() # Forward propagation Z3 = forward_propagation(X, parameters) # Cost function cost = compute_cost(Z3, Y) # Backpropagation optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost) # Initialize all the variables globally 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): minibatch_cost = 0. num_minibatches = int(m / minibatch_size) 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 _ , temp_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y}) minibatch_cost += temp_cost / num_minibatches # Print the cost every epoch if print_cost == True and epoch % 5 == 0: print ("Cost after epoch %i: %f" % (epoch, minibatch_cost)) if print_cost == True and epoch % 1 == 0: costs.append(minibatch_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() # Calculate the correct predictions predict_op = tf.argmax(Z3, 1) correct_prediction = tf.equal(predict_op, tf.argmax(Y, 1)) # Calculate accuracy on the test set 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

對訓練集執行模型訓練：

_, _, parameters = model(X_train, Y_train, X_test, Y_test)

訓練迭代過程如下：

我們在訓練集上取得了 0.67 的準確率，在測試集上的預測準確率為 0.58 ，雖然效果并不顯著，模型也有待深度調優，但我們已經學會了如何用Tensorflow快速搭建起一個深度學習系統了。

注：本深度學習筆記系作者學習 Andrew NG 的 deeplearningai 五門課程所記筆記，其中代碼為每門課的課后assignments作業整理而成。