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68 lines
2.1 KiB
68 lines
2.1 KiB
import tensorflow as tf
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from tensorflow.examples.tutorials.mnist import input_data
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def weight_variable(shape):
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initial = tf.truncated_normal(shape,stddev=0.1)
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return tf.Variable(initial)
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def bias_variable(shape):
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initial = tf.constant(0,1,shape=shape)
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return tf.Variable(initial)
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def conv2d(x,W):
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return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
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def max_pool_2x2(x):
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return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
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# 预处理
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mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
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xs = tf.placeholder(tf.float32,[None,784])
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ys = tf.placeholder(tf.float32,[None,10])
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keep_prob = tf.placeholder(tf.float32)
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# 第一层卷积
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W_conv1 = weight_variable([5,5,1,32])
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b_conv1 = bias_variable([32])
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x_image = tf.reshape(xs,[-1,28,28,1])
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h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
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h_pool1 = max_pool_2x2(h_conv1)
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# 第二层卷积
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W_conv2 = weight_variable([5,5,32,64])
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b_conv2 = bias_variable([64])
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h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)
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h_pool2 = max_pool_2x2(h_conv2)
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# 第一层全连接层
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W_fc1 = weight_variable([7*7*64,1024])
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b_fc1 = bias_variable([1024])
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h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])
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h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
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h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)
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# 第二层全连接层
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W_fc2 = weight_variable([1024,10])
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b_fc2 = bias_variable([10])
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y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
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# 训练模型
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cross_entropy = -tf.reduce_sum(ys*tf.log(y_conv))
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train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
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# 概率计算
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correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(ys,1))
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accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
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init = tf.initialize_all_variables()
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sess = tf.Session()
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sess.run(init)
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for i in range(2001):
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batch = mnist.train.next_batch(50)
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sess.run(train_step,feed_dict={xs:batch[0],ys:batch[1],keep_prob:0.5})
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if i%100==0:
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tests = mnist.test.next_batch(200)
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print(sess.run(accuracy,feed_dict={xs:tests[0],ys:tests[1],keep_prob:1.0})) |