Tensorflow CNN 的简单实现

教学视频：莫烦python

有空自己整理复写一遍~mark

# encoding:utf8import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('MNIST_data', one_hot=True)def compute_accuracy(v_xs, v_ys):    global prediction    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})    correct_predition = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))    accuracy = tf.reduce_mean(tf.cast(correct_predition, tf.float32))  # tf.cast 为数据和转换函数    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})    return resultdef weight_variable(shape):    initial = tf.truncated_normal(shape, stddev=0.1)#tf.truncated_normal生成截断正太分布    return tf.Variable(initial)  #定义变量def bias_variable(shape):    initial = tf.constant(0.1, shape=shape)    return tf.Variable(initial)## 定义一个卷积层 输入为x,w为权重def conv2d(x, W):    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')## padding has another choice "VALID"## 定义池化层def max_pool_2x2(x):    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')# 池化x,ksize[batch,height,width,channels]为池化窗口的大小,不在batch和channels上池化,取值为1# strides为窗口在每个维度滑动的步长# define placeholder for inputs to networkxs = tf.placeholder(tf.float32, [None, 784])/255ys = tf.placeholder(tf.float32, [None, 10])keep_prob = tf.placeholder(tf.float32)x_image = tf.reshape(xs, [-1, 28, 28, 1])# 将输入的变量变为一个4维向量# print x_image.shape## conv1 layer ##W_conv1 = weight_variable([5, 5, 1, 32])  # 卷积核的大小为5x5,1为通道数,32为传入下一个神经元的节点数b_conv1 = bias_variable([32])h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)h_pool1 = max_pool_2x2(h_conv1)## conv2 layer ##W_conv2 = W_conv1 = weight_variable([5, 5, 32, 64])b_conv2 = bias_variable([64])h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)h_pool2 = max_pool_2x2(h_conv2)## fc1 layer ##W_fc1 = weight_variable([7 * 7 * 64, 1024])b_fc1 = bias_variable([1024])h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)h_fc1_dropout = tf.nn.dropout(h_fc1, keep_prob)## fc2 layer ##W_fc2 = weight_variable([1024, 10])b_fc2 = bias_variable([10])prediction = tf.nn.softmax(tf.matmul(h_fc1_dropout, W_fc2) + b_fc2)## op ##cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))#reduction_indices 在哪一维上求解train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)# 选择Adam ,learning rate:0.0001sess = tf.Session()init = tf.global_variables_initializer()sess.run(init)for i in range(1000):    batch_xs, batch_ys = mnist.train.next_batch(100)    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5})    if i % 50 == 0:        print compute_accuracy(mnist.test.images[:1000], mnist.test.labels[:1000])