问题

在运行过程中, 抛出异常:

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input/y-input' with dtype float and shape [?,10]

原始代码如下

import tensorflow as tf
from tensorflow.contrib.layers import l2_regularizer
from tensorflow.examples.tutorials.mnist import input_data

REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99
BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
INPUT_NODE = 28 * 28
OUTPUT_NODE = 10
LAYER1_NODE = 500

def get_weight_variable(shape, regularizer):
    weights = tf.get_variable('weights', shape=shape, initializer=tf.truncated_normal_initializer(stddev=0.1))

    if regularizer is not 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=None):
    # 声明第一层神经网络并前向传播.
    with tf.variable_scope('layer1'):
        weights = get_weight_variable(shape=[INPUT_NODE, LAYER1_NODE], regularizer=regularizer)
        biases = tf.get_variable(name='biases', shape=[LAYER1_NODE], initializer=tf.constant_initializer(value=0.0))
        layer1 = tf.nn.relu(tf.matmul(a=input_tensor, b=weights) + biases)

    # 声明第二层神经网络并前向传播.
    with tf.variable_scope('layer2'):
        weights = get_weight_variable(shape=[LAYER1_NODE, OUTPUT_NODE], regularizer=regularizer)
        biases = tf.get_variable(name='biases', shape=[OUTPUT_NODE], initializer=tf.constant_initializer(value=0.0))
        layer2 = tf.nn.relu(tf.matmul(a=layer1, b=weights) + biases)

    # 返回前向传播的结果.
    return layer2

def train(mnist):
    # 将处理输入数据的计算放在命名空间'input'内.
    with tf.name_scope('input'):
        x = tf.placeholder(dtype=tf.float32, shape=(None,INPUT_NODE), name='x-input')
        y_ = tf.placeholder(dtype=tf.float32, shape=(None,OUTPUT_NODE), name='y-input')

    regularizer = l2_regularizer(REGULARIZATION_RATE)
    y = inference(x, regularizer)
    global_step = tf.Variable(initial_value=0, trainable=False)

    # 将处理滑动平均相关的计算都放在命名空间'moving_average'内.
    with tf.name_scope('moving_average'):
        variable_averages = tf.train.ExponentialMovingAverage(decay=MOVING_AVERAGE_DECAY, num_updates=global_step)
        variables_average_op = variable_averages.apply(tf.trainable_variables())

    # 将计算损失函数的计算都放在命名空间'loss_function'内.
    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(inputs=tf.get_collection('losses'))

    # 将定义学习率, 优化器以及每一轮训练需要执行的操作都放在命名空间'train_step'内.
    with tf.name_scope('train_step'):
        learning_rate = tf.train.exponential_decay(learning_rate=LEARNING_RATE_BASE,
                                                   global_step=global_step,
                                                   decay_steps=mnist.train.num_examples / BATCH_SIZE,
                                                   decay_rate=LEARNING_RATE_DECAY,
                                                   staircase=True)
        train_step = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss=loss,
                                                                                             global_step=global_step)
        with tf.control_dependencies(control_inputs=[train_step, variables_average_op]):
            train_op = tf.no_op(name='train')

    saver = tf.train.Saver()

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        for i in range(TRAINING_STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)

            _, loss_value, step = sess.run(fetches=[train_op, loss, global_step],
                                           feed_dict={x: xs, y: ys})

            if i % 1000 == 0:
                print('After %d training steps, loss on training batch is %d.' % (step, loss_value))

    # 将当前计算图输出到TensorBoard日志文件.
    writer = tf.summary.FileWriter(logdir='./log/', graph=tf.get_default_graph())
    writer.close()


def main(argv=None):
    mnist = input_data.read_data_sets('./mnist_data/', one_hot=True)
    train(mnist)


if __name__ == '__main__':
    tf.app.run()

解决

经过排查, 发现错误行:

_, loss_value, step = sess.run(fetches=[train_op, loss, global_step], feed_dict={x: xs, y: ys})

其中, feed_dict中应该是将ys传给y_占位符, 这里误写成了y, 导致报出占位符无输入的错误. 修改之后正常运行.