first commit

mnist.py

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+import tensorflow as tf
+
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+
+import tensorflow.examples.tutorials.mnist.input_data as input_data
+mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
+
+sess = tf.InteractiveSession()
+
+x = tf.placeholder("float", shape=[None, 784])
+y_ = tf.placeholder("float", shape=[None, 10])
+
+w = tf.Variable(tf.zeros([784,10]))
+b = tf.Variable(tf.zeros([10]))
+
+init = tf.global_variables_initializer()
+sess.run(init)
+
+y = tf.nn.softmax(tf.matmul(x, w) + b)
+
+cross_entropy = -tf.reduce_sum(y_*tf.log(y))
+
+train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
+
+for i in range(1000):
+  batch = mnist.train.next_batch(50)
+  train_step.run(feed_dict={x: batch[0], y_: batch[1]})
+
+correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+
+print (accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

mnist2.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep  8 15:29:48 2016
+
+@author: root
+"""
+import tensorflow as tf
+import tensorflow.examples.tutorials.mnist.input_data as input_data
+
+mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  # 下载并加载mnist数据
+x = tf.placeholder(tf.float32, [None, 784])  # 输入的数据占位符
+y_actual = tf.placeholder(tf.float32, shape=[None, 10])  # 输入的标签占位符
+
+
+# 定义一个函数，用于初始化所有的权值 W
+def weight_variable(shape):
+    initial = tf.truncated_normal(shape, stddev=0.1)
+    return tf.Variable(initial)
+
+
+# 定义一个函数，用于初始化所有的偏置项 b
+def bias_variable(shape):
+    initial = tf.constant(0.1, shape=shape)
+    return tf.Variable(initial)
+
+
+# 定义一个函数，用于构建卷积层
+def conv2d(x, W):
+    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
+
+
+# 定义一个函数，用于构建池化层
+def max_pool(x):
+    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+
+
+# 构建网络
+x_image = tf.reshape(x, [-1, 28, 28, 1])  # 转换输入数据shape,以便于用于网络中
+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)  # 第一个卷积层
+h_pool1 = max_pool(h_conv1)  # 第一个池化层
+
+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)  # 第二个卷积层
+h_pool2 = max_pool(h_conv2)  # 第二个池化层
+
+W_fc1 = weight_variable([7 * 7 * 64, 1024])
+b_fc1 = bias_variable([1024])
+h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])  # reshape成向量
+h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)  # 第一个全连接层
+
+keep_prob = tf.placeholder("float")
+h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)  # dropout层
+
+W_fc2 = weight_variable([1024, 10])
+b_fc2 = bias_variable([10])
+y_predict = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)  # softmax层
+
+cross_entropy = -tf.reduce_sum(y_actual * tf.log(y_predict))  # 交叉熵
+train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy)  # 梯度下降法
+correct_prediction = tf.equal(tf.argmax(y_predict, 1), tf.argmax(y_actual, 1))
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))  # 精确度计算
+sess = tf.InteractiveSession()
+sess.run(tf.initialize_all_variables())
+for i in range(20000):
+    batch = mnist.train.next_batch(50)
+    if i % 100 == 0:  # 训练100次，验证一次
+        train_acc = accuracy.eval(feed_dict={x: batch[0], y_actual: batch[1], keep_prob: 1.0})
+        print('step', i, 'training accuracy', train_acc)
+        train_step.run(feed_dict={x: batch[0], y_actual: batch[1], keep_prob: 0.5})
+
+test_acc = accuracy.eval(feed_dict={x: mnist.test.images, y_actual: mnist.test.labels, keep_prob: 1.0})
+print("test accuracy", test_acc)