深度学习笔记8：利用Tensorflow搭建神经网络

本帖最后由 Coldplay。于 2018-10-30 11:44 编辑

在笔记7中，和大家一起入门了 Tensorflow 的基本语法，并举了一些实际的例子进行了说明，终于告别了使用 numpy 手动搭建的日子。所以我们将继续往下走，看看如何利用 Tensorflow 搭建神经网络模型。

尽管对于初学者而言使用 Tensorflow 看起来并不那么习惯，需要各种步骤，但简单来说，Tensorflow 搭建模型实际就是两个过程：创建计算图和执行计算图。在 deeplearningai 课程中，NG和他的课程组给我们提供了 Signs Dataset （手势）数据集，其中训练集包括1080张64x64像素的手势图片，并给定了 6 种标注，测试集包括120张64x64的手势图片，我们需要对训练集构建神经网络模型然后对测试集给出预测。

先来简单看一下数据集：

# Loading the datasetX_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).T
X_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))

下面就根据 NG 给定的找个数据集利用 Tensorflow 搭建神经网络模型。我们选择构建一个包含 2 个隐层的神经网络，网络结构大致如下：
LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX
正如我们之前利用 numpy 手动搭建一样，搭建一个神经网络的主要步骤如下：
-定义网络结构
-初始化模型参数
-执行前向计算/计算当前损失/执行反向传播/权值更新

创建 placeholder

根据 Tensorflow 的语法，我们首先创建输入X 和输出 Y 的占位符变量，这里需要注意 shape 参数的设置。

def create_placeholders(n_x, n_y):
X = tf.placeholder(tf.float32, shape=(n_x, None), name='X')
Y = tf.placeholder(tf.float32, shape=(n_y, None), name='Y')
return X, Y

初始化模型参数

其次就是初始化神经网络的模型参数，三层网络包括六个参数，这里我们采用Xavier初始化方法：

def initialize_parameters():
tf.set_random_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())

parameters = {"W1": W1,
               "b1": b1,
               "W2": W2,
               "b2": b2,
               "W3": W3,
               "b3": b3}
return parameters

执行前向传播

def forward_propagation(X, parameters): """
Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX
"""

W1 = parameters['W1']
b1 = parameters['b1']
W2 = parameters['W2']
b2 = parameters['b2']
W3 = parameters['W3']
b3 = parameters['b3']

Z1 = tf.add(tf.matmul(W1, X), b1)
A1 = tf.nn.relu(Z1)
Z2 = tf.add(tf.matmul(W2, A1), b2)
A2 = tf.nn.relu(Z2)
Z3 = tf.add(tf.matmul(W3, A2), b3)
return Z3

计算损失函数

在 Tensorflow 中损失函数的计算要比手动搭建时方便很多，一行代码即可搞定：

def compute_cost(Z3, Y):
logits = tf.transpose(Z3)
labels = tf.transpose(Y)

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

代码整合：执行反向传播和权值更新

跟计算损失函数类似，Tensorflow 中执行反向传播的梯度优化非常简便，两行代码即可搞定，定义完整的神经网络模型如下：

def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,
      num_epochs = 1500, minibatch_size = 32, print_cost = True):
ops.reset_default_graph()
tf.set_random_seed(1)
seed = 3
(n_x, m) = X_train.shape
n_y = Y_train.shape[0]
costs = []

# Create Placeholders of shape (n_x, n_y)
X, Y = create_placeholders(n_x, n_y) # Initialize parameters
parameters = initialize_parameters() # Forward propagation: Build the forward propagation in the tensorflow graph

Z3 = forward_propagation(X, parameters) # Cost function: Add cost function to tensorflow graph
cost = compute_cost(Z3, Y) # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.
optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(cost) # 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.
         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
            _ , minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})
            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 parameters