#第一部分,前向传播
import
tensorflow as tf #定义神经网络结构相关的参数 INPUT_NODE = 784 OUTPUT_NODE = 10 LAYER1_NODE = 500 #通过tf.get_Variable函数来获取变量。在训练神经网络时会创建这些变量;在测试时会通过 #保存的模型加载这些变量的取值。而且更加方便的是,因为可以在变量加载时将滑动平均变量 #重命名,所以可以直接通过同样的名字在训练时使用变量自身,而在测试时使用变量的滑动平均 #值。在这个函数中也会将变量的正则化损失加入损失集合。 def get_weight_variable(shape, regularizer): weights = tf.get_variable( "weights", shape, initializer=tf.truncated_normal_initializer(stddev=0.1)) if regularizer != None: tf.add_to_collection(\'losses\', regularizer(weights)) return weights #定义神经网络的前向传播过程。 def inference(input_tensor, regularizer): #第一层 with tf.variable_scope(\'layer1\'): #tf.get_variable/tf.Variable都可以,但是假如多次调用要留意reuse = True weights = get_weight_variable( [INPUT_NODE, LAYER1_NODE], regularizer) biases = tf.get_variable( "biases", [LAYER1_NODE], initializer = tf.constant_initializer(0.0)) layer1 = tf.nn.relu(tf.matmul(input_tensor, weights) + biases) #第二层 with tf.variable_scope(\'layer2\'): weights = get_weight_variable( [LAYER1_NODE, OUTPUT_NODE], regularizer) biases = tf.get_variable( "biases", [OUTPUT_NODE], initializer = tf.constant_initializer(0.0)) layer2 = tf.matmul(layer1, weights) + biases return layer2

 

#训练部分
import
os import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import mnist_inference BATCH_SIZE = 100 LEARNING_RATE_BASE = 0.8 LEARNING_RATE_DECAY = 0.99 REGULARAZTION_RATE = 0.0001 TRAINING_STEPS = 30000 MOVING_AVERAGE_DECAY = 0.99 # 模型保存的路径和文件名。 MODEL_SAVE_PATH = "saves_model_path" MODEL_NAME = "model.ckpt" def train(mnist): #定义输入输出placeholder。 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-input\') regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE) #inference函数 y = mnist_inference.inference(x, regularizer) 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()) 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\')) 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\') saver = tf.train.Saver() with tf.Session() as sess: tf.global_variables_initializer().run() 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}) if i % 1000 == 0: print("After %d training step(s), loss on training " "batch is %g." % (step, loss_value)) saver.save( sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step) def main(argv=None): mnist = input_data.read_data_sets("/tmp/data", one_hot=True) train(mnist) if __name__ == \'__main__\': tf.app.run()

 

#评价部分,保存模型
import
time import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import mnist_inference import mnist_train # 每10秒加载一次最新的模型,并在测试数据上测试最新模型的正确率 EVAL_INTERVAL_SECS = 10 def evaluate(mnist): with tf.Graph().as_default() as g: # 定义输入输出的格式 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-input\') validate_feed = {x: mnist.validation.images, y_:mnist.validation.labels} # 用封装好的函数来计算结果,不关注正则化损失的值 y = mnist_inference.inference(x, None) correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) variable_averages = tf.train.ExponentialMovingAverage( mnist_train.MOVING_AVERAGE_DECAY) variables_to_restore = variable_averages.variables_to_restore() saver = tf.train.Saver(variables_to_restore) while True: with tf.Session() as sess: ckpt = tf.train.get_checkpoint_state( mnist_train.MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) global_step = ckpt.model_checkpoint_path.split(\'/\')[-1]\
                      .split(\'-\')[-1]
accuracy_score
= sess.run(accuracy, feed_dict=validate_feed) print("After %s training step(s), validation " "accuracy = %g" % (global_step, accuracy_score)) else: print(\'No checkpoint file found\') return time.sleep(EVAL_INTERVAL_SECS) def main(argv=None): mnist = input_data.read_data_sets("/tmp/data", one_hot=True) evaluate(mnist) if __name__ == \'__main__\': tf.app.run()