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.