【连载】深度学习笔记12：卷积神经网络的Tensorflow实现

      在上一讲中，我们学习了如何利用 numpy 手动搭建卷积神经网络。但在实际的图像识别中，使用 numpy 去手写 CNN 未免有些吃力不讨好。在 DNN 的学习中，我们也是在手动搭建之后利用 Tensorflow 去重新实现一遍，一来为了能够对神经网络的传播机制能够理解更加透彻，二来也是为了更加高效使用开源框架快速搭建起深度学习项目。本节就继续和大家一起学习如何利用 Tensorflow 搭建一个卷积神经网络。

      我们继续以 NG 课题组提供的 sign 手势数据集为例，学习如何通过 Tensorflow 快速搭建起一个深度学习项目。数据集标签共有零到五总共 6 类标签，示例如下：

      先对数据进行简单的预处理并查看训练集和测试集维度：

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).T

print ("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))

可见我们总共有 1080 张 64643 训练集图像，120 张 64643 的测试集图像，共有 6 类标签。下面我们开始搭建过程。

创建 placeholder

      首先需要为训练集预测变量和目标变量创建占位符变量 placeholder ，定义创建占位符变量函数：

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

参数初始化

      然后需要对滤波器权值参数进行初始化：

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

执行卷积网络的前向传播过程

前向传播过程如下所示：

CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED

可见我们要搭建的是一个典型的 CNN 过程，经过两次的卷积-relu激活-最大池化，然后展开接上一个全连接层。利用

Tensorflow  搭建上述传播过程如下：

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

计算当前损失

      在 Tensorflow  中计算损失函数非常简单，一行代码即可：

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

      定义好上述过程之后，就可以封装整体的训练过程模型。可能你会问为什么没有反向传播，这里需要注意的是 Tensorflow 帮助我们自动封装好了反向传播过程，无需我们再次定义，在实际搭建过程中我们只需将前向传播的网络结构定义清楚即可。

封装模型

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

     对训练集执行模型训练：

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

     训练迭代过程如下：

    我们在训练集上取得了 0.67 的准确率，在测试集上的预测准确率为 0.58 ，虽然效果并不显著，模型也有待深度调优，但我们已经学会了如何用 Tensorflow  快速搭建起一个深度学习系统了