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mnist_cnn.py
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mnist_cnn.py
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#coding:utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev = 0)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape = shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding = 'SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding = 'SAME')
def train():
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder("float", [None,10])
#卷积与池化
W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
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)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024,10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch_xs, batch_ys = mnist.train.next_batch(100)
if i%1 == 0:
train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 1.0})
print "step %d, training accuracy %g" % (i, train_accuracy)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})
#print "test accuracy %g" % accuracy.eval(feed_dict = {x: mnist.test.image, y_:mnist.test.labels, keep_prob: 1.0})
def main(_):
train()
if __name__ == '__main__':
tf.app.run()