吴裕雄--天生自然TensorFlow高层封装：Keras-TensorFlow API

# 1. 模型定义。

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist_data = input_data.read_data_sets('F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data', one_hot=True)

# 通过TensorFlow中的placeholder定义输入。

x = tf.placeholder(tf.float32, shape=(None, 784))

y_ = tf.placeholder(tf.float32, shape=(None, 10))

net = tf.keras.layers.Dense(500, activation='relu')(x)

y = tf.keras.layers.Dense(10, activation='softmax')(net)

acc_value = tf.reduce_mean(tf.keras.metrics.categorical_accuracy(y_, y))

loss = tf.reduce_mean(tf.keras.losses.categorical_crossentropy(y_, y))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

吴裕雄--天生自然TensorFlow高层封装：Keras-TensorFlow API-LMLPHP

# 2. 模型训练。

with tf.Session() as sess:

    tf.global_variables_initializer().run()

    for i in range(3000):

        xs, ys = mnist_data.train.next_batch(100)

        _, loss_value = sess.run([train_step, loss], feed_dict={x: xs, y_: ys})

        if i % 1000 == 0:

            print("After %d training step(s), loss on training batch is %g." % (i, loss_value))

    print(acc_value.eval(feed_dict={x: mnist_data.test.images,y_: mnist_data.test.labels}))

吴裕雄--天生自然TensorFlow高层封装：Keras-TensorFlow API-LMLPHP