折腾了很久，终于运行成功。
才云科技的书不错，就是需要微调一二。
心得：1，记得activate tensorflow,然后再python
2，Python的代码格式很重要，不要错误。
3，还不清楚如何不跳出去就能用tensorflow的方法。
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from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data INPUT_NODE = 784 OUTPUT_NODE = 10 LAYER1_NODE = 500 BATCH_SIZE = 100 LEARNING_RATE_BASE = 0.8 LEARNING_RATE_DECAY = 0.99 REGULARIZATION_RATE = 0.001 TRAINING_STEPS = 30000 MOVING_AVERAGE_DECAY = 0.99 FLAGS=None
def inference(input_tensor,avg_class,weights1,biases1,weights2,biases2): if avg_class == None: layer1=tf.nn.relu(tf.matmul(input_tensor,weights1)+biases1) return tf.matmul(layer1,weights2)+biases2 else: layer1 = tf.nn.relu( tf.matmul(input_tensor,avg_class.average(weights1))+avg_class.average(biases1)) return tf.matmul(layer1,avg_class.average(weights2))+avg_class.average(biases2)
def main(_): mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) x = tf.placeholder(tf.float32,[None,INPUT_NODE],name='x-input') y_= tf.placeholder(tf.float32,[None,OUTPUT_NODE],name='y-input') weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE,LAYER1_NODE],stddev=0.1)) biases1 = tf.Variable(tf.constant(0.1,shape=[LAYER1_NODE])) weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE,OUTPUT_NODE],stddev=0.1)) biases2 = tf.Variable(tf.constant(0.1,shape=[OUTPUT_NODE])) y=inference(x,None,weights1,biases1,weights2,biases2) global_step =tf.Variable(0,trainable=False) variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step) variables_averages_op = variable_averages.apply(tf.trainable_variables()) average_y = inference(x,variable_averages,weights1,biases1,weights2,biases2) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_,1)) cross_entropy_mean = tf.reduce_mean(cross_entropy) regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE) regularization=regularizer(weights1)+regularizer(weights2) loss = cross_entropy_mean + regularization learning_rate = tf.train.exponential_decay( LEARNING_RATE_BASE,global_step,mnist.train.num_examples/BATCH_SIZE,LEARNING_RATE_DECAY) train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step) with tf.control_dependencies([train_step,variables_averages_op]): train_op=tf.no_op(name='train') correct_prediction = tf.equal(tf.argmax(average_y,1),tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) with tf.Session() as sess: tf.global_variables_initializer().run() validate_feed = {x:mnist.validation.images,y_:mnist.validation.labels} test_feed = {x:mnist.test.images,y_:mnist.test.labels} for i in range(TRAINING_STEPS): if i % 1000 == 0: validate_acc = sess.run(accuracy,feed_dict = validate_feed) print("After %d training steps,validation accuracy " "using average model is %g" % (i, validate_acc)) xs,ys = mnist.train.next_batch(BATCH_SIZE) sess.run(train_op,feed_dict = {x:xs,y_:ys}) test_acc = sess.run(accuracy,feed_dict=test_feed) print("After %d training steps,test accuracy using average model is %g" % (TRAINING_STEPS,test_acc))
if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data', help='Directory for storing input data') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)