深度學習筆記8：利用Tensorflow搭建神經網路

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出處：數據科學家養成記 深度學習筆記8：利用Tensorflow搭建神經網路

在筆記7中，筆者和大家一起入門了 Tensorflow 的基本語法，並舉了一些實際的例子進行了說明，終於告別了使用 numpy 手動搭建的日子。所以我們將繼續往下走，看看如何利用 Tensorflow搭建神經網路模型。

儘管對於初學者而言使用 Tensorflow 看起來並不那麼習慣，需要各種步驟，但簡單來說，Tensorflow 搭建模型實際就是兩個過程：創建計算圖和執行計算圖。在 deeplearningai 課程中，NG和他的課程組給我們提供了 Signs Dataset （手勢）數據集，其中訓練集包括1080張64x64像素的手勢圖片，並給定了 6 種標註，測試集包括120張64x64的手勢圖片，我們需要對訓練集構建神經網路模型然後對測試集給出預測。

先來簡單看一下數據集：

# 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).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))

下面就根據 NG 給定的找個數據集利用 Tensorflow 搭建神經網路模型。我們選擇構建一個包含 2 個隱層的神經網路，網路結構大致如下：

正如我們之前利用 numpy 手動搭建一樣，搭建一個神經網路的主要步驟如下：

創建 placeholder

根據 Tensorflow 的語法，我們首先創建輸入X 和輸出 Y 的佔位符變數，這裡需要注意 shape 參數的設置。

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

初始化模型參數

其次就是初始化神經網路的模型參數，三層網路包括六個參數，這裡我們採用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

執行前向傳播

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

計算損失函數

在 Tensorflow 中損失函數的計算要比手動搭建時方便很多，一行代碼即可搞定：

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

代碼整合：執行反向傳播和權值更新

跟計算損失函數類似，Tensorflow 中執行反向傳播的梯度優化非常簡便，兩行代碼即可搞定，定義完整的神經網路模型如下：

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

執行模型：

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

根據模型的訓練誤差和測試誤差可以看到：模型整體效果雖然沒有達到最佳，但基本也能達到預測效果。

總結

• Tensorflow 語法中兩個基本的對象類是 Tensor 和 Operator.
• Tensorflow 執行計算的基本步驟為
• 創建計算圖（張量、變數和佔位符變數等）
• 創建會話
• 初始化會話
• 在計算圖中執行會話

可以看到的是，在 Tensorflow 中編寫神經網路要比我們手動搭建要方便的多，這也正是深度學習框架存在的意義之一。功能強大的深度學習框架能夠幫助我們快速的搭建起複雜的神經網路模型，在經歷了手動搭建神經網路的思維訓練過程之後，這對於我們來說就不再困難了。

參考資料：

https://www.coursera.org/learn/machine-learning

https://www.deeplearning.ai/