Tensorflow実戦ノート(一)多層感知機を実現

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 19 14:44:17 2017

@author: xiaolian
"""

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

mnist = input_data.read_data_sets('Mnist_data', one_hot = True)

sess = tf.InteractiveSession()

in_units = 784
h1_units = 300


# [-1, 784] x [784, 300]
w1 = tf.Variable(tf.truncated_normal([in_units, h1_units], stddev = 0.1))
b1 = tf.Variable(tf.zeros([h1_units]))

# [784, 300] x [300, 10]
w2 = tf.Variable(tf.zeros([h1_units, 10]))
b2 = tf.Variable(tf.zeros([10]))

# define placeholder for input data
x = tf.placeholder(tf.float32, [None, in_units])
y_ = tf.placeholder(tf.float32, [None, 10])
# define placeholder for dropout
keep_prob = tf.placeholder(tf.float32)

# hidden layer
hidden1 = tf.nn.relu(tf.matmul(x, w1) + b1)
hidden1_drop = tf.nn.dropout(hidden1, keep_prob)

# prediction
y = tf.nn.softmax(tf.matmul(hidden1_drop, w2) + b2)

# loss 
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices = [1]))
train_step = tf.train.AdagradOptimizer(0.3).minimize(cross_entropy)

# variable initialize
sess.run(tf.global_variables_initializer())


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})
    if i % 50 == 0 :
        print(sess.run(cross_entropy, feed_dict = {x : batch_xs, y_ : batch_ys, keep_prob : 0.75}))

# output correct prediction
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}))