# coding: utf-8 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') if __name__ == '__main__': # 读入数据 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # x为训练图像的占位符、y_为训练图像标签的占位符 x = tf.placeholder(tf.float32, [None, 784]) y_ = tf.placeholder(tf.float32, [None, 10]) # 将单张图片从784维向量重新还原为28x28的矩阵图片 x_image = tf.reshape(x, [-1, 28, 28, 1]) # 第一层卷积层 W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) # 第二层卷积层 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) h_pool2 = max_pool_2x2(h_conv2) # 全连接层，输出为1024维的向量 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) # 使用Dropout，keep_prob是一个占位符，训练时为0.5，测试时为1 keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) # 把1024维的向量转换成10维，对应10个类别 W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2# 最后一层没有relu!!!!!!!!!!!!!!!!!!!!!!!! # 我们不采用先Softmax再计算交叉熵的方法，而是直接用tf.nn.softmax_cross_entropy_with_logits直接计算 cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) # 同样定义train_step train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # 定义测试的准确率 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 创建Session和变量初始化 sess = tf.InteractiveSession() sess.run(tf.global_variables_initializer()) # 训练20000步 for i in range(20000): batch = mnist.train.next_batch(50) # 每100步报告一次在验证集上的准确度 if i % 100 == 0: train_accuracy = accuracy.eval(feed_dict={ x: batch[0], y_: batch[1], keep_prob: 1.0}) print("step %d, training accuracy %g" % (i, train_accuracy)) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) # 训练结束后报告在测试集上的准确度 print("test accuracy %g" % accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
eval() 其实就是tf.Tensor的Session.run() 的另外一种写法。你上面些的那个代码例子，如果稍微修改一下，加上一个Session context manager：
with tf.Session() as sess:
print(accuracy.eval({x:mnist.test.images,y_: mnist.test.labels}))
其效果和下面的代码是等价的：
with tf.Session() as sess:
print(sess.run(accuracy, {x:mnist.test.images,y_: mnist.test.labels}))
但是要注意的是，eval()只能用于tf.Tensor类对象，也就是有输出的Operation。对于没有输出的Operation, 可以用.run()或者Session.run()。Session.run()没有这个限制。