tf实现多层cnn

# -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

# 用tensorflow 导入数据
mnist = input_data.read_data_sets('../../MNIST_data', one_hot=True)

print mnist.test.labels.shape
print mnist.train.labels.shape

# 权值初始化
def weight_variable(shape):
    # 用正态分布来初始化权值
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    # 本例中用relu激活函数，所以用一个很小的正偏置较好
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

# 定义卷积层
def conv2d(x, W):
    # 默认 strides[0]=strides[3]=1, strides[1]为x方向步长，strides[2]为y方向步长
    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')

# pooling 层
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')


X_ = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])

# 把X转为卷积所需要的形式
X = tf.reshape(X_, [-1, 28, 28, 1])
# 第一层卷积：5×5×1卷积核32个 [5，5，1，32],h_conv1.shape=[-1, 28, 28, 32]
W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(X, W_conv1) + b_conv1)

# 第一个pooling 层[-1, 28, 28, 32]->[-1, 14, 14, 32]
h_pool1 = max_pool_2x2(h_conv1)

# 第二层卷积：5×5×32卷积核64个 [5，5，32，64],h_conv2.shape=[-1, 14, 14, 64]
W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

# 第二个pooling 层,[-1, 14, 14, 64]->[-1, 7, 7, 64]
h_pool2 = max_pool_2x2(h_conv2)

# flatten层，[-1, 7, 7, 64]->[-1, 7*7*64],即每个样本得到一个7*7*64维的样本
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])

# fc1
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# dropout: 输出的维度和h_fc1一样，只是随机部分值被值为零
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# 输出层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)



# 1.损失函数：cross_entropy
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
# 2.优化函数：AdamOptimizer
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

# 3.预测准确结果统计
#　预测值中最大值（１）即分类结果，是否等于原始标签中的（１）的位置。argmax()取最大值所在的下标
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 如果一次性来做测试的话，可能占用的显存会比较多，所以测试的时候也可以设置较小的batch来看准确率
test_acc_sum = tf.Variable(0.0)
batch_acc = tf.placeholder(tf.float32)
new_test_acc_sum = tf.add(test_acc_sum, batch_acc)
update = tf.assign(test_acc_sum, new_test_acc_sum)

with tf.Session() as sess:
    # 定义了变量必须要初始化，或者下面形式
    sess.run(tf.global_variables_initializer())
    # 或者某个变量单独初始化 如：
    # x.initializer.run()
    # 训练
    for i in range(500):
        X_batch, y_batch = mnist.train.next_batch(batch_size=50)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={X_: X_batch, y_: y_batch, keep_prob: 1.0})
            print "step %d, training acc %g" % (i, train_accuracy)
        train_step.run(feed_dict={X_: X_batch, y_: y_batch, keep_prob: 0.5})

    # 全部训练完了再做测试，batch_size=100
    for i in range(100):
        X_batch, y_batch = mnist.test.next_batch(batch_size=100)
        test_acc = accuracy.eval(feed_dict={X_: X_batch, y_: y_batch, keep_prob: 1.0})
        update.eval(feed_dict={batch_acc: test_acc})
        if (i + 1) % 20 == 0:
            print "testing step %d, test_acc_sum %g" % (i + 1, test_acc_sum.eval())
    print " test_accuracy %g" % (test_acc_sum.eval() / 100.0)
    # 显示中间结果
    # 首先应该把 img 转为正确的shape (None, 784)

    X_img = mnist.train.images[1].reshape([-1, 784])
    y_img = mnist.train.labels[1].reshape([-1, 10])
    # 我们要看 Conv1 的结果，即 h_conv1
    result = h_conv1.eval(feed_dict={X_: X_img, y_: y_img, keep_prob: 1.0})
    for _ in xrange(32):
        show_img = result[:, :, :, _]
        show_img.shape = [28, 28]
        plt.subplot(4, 8, _ + 1)
        plt.imshow(show_img, cmap='gray')
        plt.axis('off')
    plt.show()