TensorFlow学习笔记5：神经网络搭建

代码来源：https://github.com/aymericdamien/TensorFlow-Examples

首先看一下用最基本的矩阵运算来搭建神经网络解决MNIST分类问题，比如搭建一个两层的全连接网络。

from __future__ import print_function# Import MNIST datafrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)import tensorflow as tf# Parameterslearning_rate = 0.1num_steps = 500batch_size = 128display_step = 100# Network Parametersn_hidden_1 = 256 # 1st layer number of neuronsn_hidden_2 = 256 # 2nd layer number of neuronsnum_input = 784 # MNIST data input (img shape: 28*28)num_classes = 10 # MNIST total classes (0-9 digits)# tf Graph inputX = tf.placeholder("float", [None, num_input])Y = tf.placeholder("float", [None, num_classes])

这里设置了一些基本的参数，可以看到第一层和第二层都分别有256个神经元，将28*28的输入分成0-9的10类数字。

# Store layers weight & biasweights = {    'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),    'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes]))}biases = {    'b1': tf.Variable(tf.random_normal([n_hidden_1])),    'b2': tf.Variable(tf.random_normal([n_hidden_2])),    'out': tf.Variable(tf.random_normal([num_classes]))}# Create modeldef neural_net(x):    # Hidden fully connected layer with 256 neurons    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])    # Hidden fully connected layer with 256 neurons    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])    # Output fully connected layer with a neuron for each class    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']    return out_layer

这里定义了神经网络，根据weight和biase通过矩阵乘法建立网络

# Construct modellogits = neural_net(X)# Define loss and optimizerloss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(    logits=logits, labels=Y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)train_op = optimizer.minimize(loss_op)# Evaluate model (with test logits, for dropout to be disabled)correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# Initialize the variables (i.e. assign their default value)init = tf.global_variables_initializer()

这里建立了模型，结果为logits。

loss_op表示损失函数，这里损失函数就是交叉熵。

optimizer用Adam算法进行优化，使损失函数最小。

之后就可以开始训练了

# Start trainingwith tf.Session() as sess:    # Run the initializer    sess.run(init)    for step in range(1, num_steps+1):        batch_x, batch_y = mnist.train.next_batch(batch_size)        # Run optimization op (backprop)        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})        if step % display_step == 0 or step == 1:            # Calculate batch loss and accuracy            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,                                                                 Y: batch_y})            print("Step " + str(step) + ", Minibatch Loss= " + \                  "{:.4f}".format(loss) + ", Training Accuracy= " + \                  "{:.3f}".format(acc))    print("Optimization Finished!")    # Calculate accuracy for MNIST test images    print("Testing Accuracy:", \        sess.run(accuracy, feed_dict={X: mnist.test.images,                                      Y: mnist.test.labels}))

运行结果如下：