''' Created on 2017年5月23日 @author: weizhen ''' import os import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # minist_inference中定义的常量和前向传播的函数不需要改变， # 因为前向传播已经通过tf.variable_scope实现了计算节点按照网络结构的划分 import mnist_inference from mnist_train import MOVING_AVERAGE_DECAY, REGULARAZTION_RATE, LEARNING_RATE_BASE, BATCH_SIZE, LEARNING_RATE_DECAY, TRAINING_STEPS, MODEL_SAVE_PATH, MODEL_NAME INPUT_NODE = 784 OUTPUT_NODE = 10 LAYER1_NODE = 500 def train(mnist): # 将处理输入数据集的计算都放在名子为"input"的命名空间下 with tf.name_scope("input"): x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input') y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-cinput') regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE) y = mnist_inference.inference(x, regularizer) global_step = tf.Variable(0, trainable=False) # 将滑动平均相关的计算都放在名为moving_average的命名空间下 with tf.name_scope("moving_average"): variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step) variable_averages_op = variable_averages.apply(tf.trainable_variables()) # 将计算损失函数相关的计算都放在名为loss_function的命名空间下 with tf.name_scope("loss_function"): 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) loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses')) # 将定义学习率、优化方法以及每一轮训练需要执行的操作都放在名子为"train_step"的命名空间下 with tf.name_scope("train_step"): learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step, mnist.train._num_examples / BATCH_SIZE, LEARNING_RATE_DECAY, staircase=True) train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step) with tf.control_dependencies([train_step, variable_averages_op]): train_op = tf.no_op(name='train') # 训练模型。 with tf.Session() as sess: tf.global_variables_initializer().run() for i in range(TRAINING_STEPS): xs, ys = mnist.train.next_batch(BATCH_SIZE) if i % 1000 == 0: # 配置运行时需要记录的信息。 run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) # 运行时记录运行信息的proto。 run_metadata = tf.RunMetadata() _, loss_value, step = sess.run( [train_op, loss, global_step], feed_dict={x: xs, y_: ys}, options=run_options, run_metadata=run_metadata) print("After %d training step(s), loss on training batch is %g." % (step, loss_value)) writer = tf.summary.FileWriter("/log/modified_mnist_train.log", tf.get_default_graph()) writer.add_run_metadata(run_metadata, "stop%03d" % i) writer.close() print("After %d training steps(s),loss on training batch is %g."%(step,loss_value)) else: _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys}) # 初始化Tensorflow持久化类 # saver = tf.train.Saver() # with tf.Session() as sess: # tf.global_variables_initializer().run() # # 在训练过程中不再测试模型在验证数据上的表现，验证和测试的过程将会有一个独立的程序来完成 # for i in range(TRAINING_STEPS): # xs, ys = mnist.train.next_batch(BATCH_SIZE) # _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x:xs, y_:ys}) # 每1000轮保存一次模型 # if i % 1000 == 0: # 输出当前训练情况。这里只输出了模型在当前训练batch上的损失函数大小 # 通过损失函数的大小可以大概了解训练的情况。在验证数据集上的正确率信息 # 会有一个单独的程序来生成 # print("After %d training step(s),loss on training batch is %g" % (step, loss_value)) # 保存当前的模型。注意这里给出了global_step参数，这样可以让每个被保存模型的文件末尾加上训练的轮数 # 比如"model.ckpt-1000"表示训练1000轮之后得到的模型 # saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step) # 将当前的计算图输出到TensorBoard日志文件 # writer=tf.summary.FileWriter("/path/to/log",tf.get_default_graph()) # writer.close() def main(argv=None): mnist = input_data.read_data_sets("/tmp/data", one_hot=True) train(mnist) if __name__ == '__main__': tf.app.run()
