LearnTensorFlow.py

# -*- coding: utf-8 -*-
"""
Created on Tue Jun 13 12:02:07 2017
TensorFlow MLP
@author: wangx3
"""

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist = input_data.read_data_sets('data/MNIST_data', one_hot=True)

print(mnist.train.images.shape, mnist.train.labels.shape)
print(mnist.test.images.shape, mnist.test.labels.shape)
print(mnist.validation.images.shape, mnist.validation.labels.shape)


sess = tf.InteractiveSession()
in_units = 784
h1_units = 300

# tf.truncated_normal()将Weight初始化为截断的正态分布
w1 = tf.Variable(tf.truncated_normal([in_units, h1_units], stddev=0.1))
b1 = tf.Variable(tf.zeros([h1_units]))
w2 = tf.Variable(tf.zeros([h1_units, 10]))
b2 = tf.Variable(tf.zeros([10]))

x = tf.placeholder(tf.float32, [None, in_units])    # 接收Sample数据
keep_prob = tf.placeholder(tf.float32)
hidden1 = tf.nn.relu(tf.matmul(x, w1)+b1)
hidden1_drop = tf.nn.dropout(hidden1, keep_prob)
y = tf.nn.softmax(tf.matmul(hidden1_drop, w2)+b2)
y_ = tf.placeholder(tf.float32, [None, 10])    # 接收Sample数据
# tf.reduce_mean()是求均值，tf.reduce_sum()是求和
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

tf.global_variables_initializer().run()

for i in range(3000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    train_step.run({x:batch_xs, y_:batch_ys, keep_prob:0.75})
    
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval({x:mnist.test.images, y_:mnist.test.labels, keep_prob:1.0}))