#!/usr/bin/env python # -*- coding: utf-8 -*- """ @date 2018/08/09 20:08:45 """ import sys import numpy as np import sklearn.preprocessing as prep 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 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]) 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]) 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) 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})) if __name__ == '__main__': pass
