[TensorFlow实战] 简单CNN
2017-09-12 17:41
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代码
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data dataset_default_path = r'C:\Users\Administrator\.keras\datasets' mnist = input_data.read_data_sets(dataset_default_path,one_hot=True) sess = tf.InteractiveSession() def weight_variable(shape): initial = tf.truncated_normal(shape,stddev=0.1) 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') x = tf.placeholder(tf.float32,[None,784]) y_ = tf.placeholder(tf.float32,[None,10]) x_image = tf.reshape(x,[-1,28,28,1]) 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) 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(tf.float32) 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_mean( -tf.reduce_sum(y_*tf.log(y_conv),reduction_indices=[1])) 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,tf.float32)) tf.global_variables_initializer().run() for i in range(20000): batch = mnist.train.next_batch(50) if i%100 == 0 : train_acc = accuracy.eval({x:batch[0],y_:batch[1],keep_prob:1.0}) print("iter %d, acc:%g" %(i,train_acc)) train_step.run(feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5}) print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))
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