您好我正在尝试运行转发器。神经网络从tensORflow中的MINST2教程中添加。我有以下错误，但我不确定发生了什么：
W tensorflow/core/framework/op_kernel.cc:909] Invalid argument: Shape mismatch in tuple component 0. Expected [784], got [6272]
W tensorflow/core/framework/op_kernel.cc:909] Invalid argument: Shape mismatch in tuple component 0. Expected [784], got [6272]
Traceback (most recent call last):
File "4_Treino_Rede_Neural.py", line 161, in <module>
train_accuracy = accuracy.eval(feed_dict={keep_prob: 1.0})
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 555, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 3498, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 372, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 636, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 708, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 728, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.OutOfRangeError: RandomShuffleQueue '_0_input/shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 100, current size 0)
[[Node: input/shuffle_batch = QueueDequeueMany[_class=["loc:@input/shuffle_batch/random_shuffle_queue"], component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](input/shuffle_batch/random_shuffle_queue, input/shuffle_batch/n)]]
Caused by op u'input/shuffle_batch', defined at:
File "4_Treino_Rede_Neural.py", line 113, in <module>
x, y_ = inputs(train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs)
File "4_Treino_Rede_Neural.py", line 93, in inputs
min_after_dequeue=1000)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py", line 779, in shuffle_batch
dequeued = queue.dequeue_many(batch_size, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/data_flow_ops.py", line 400, in dequeue_many
self._queue_ref, n=n, component_types=self._dtypes, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 465, in _queue_dequeue_many
timeout_ms=timeout_ms, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 704, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2260, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1230, in __init__
self._traceback = _extract_stack()
我的节目是：
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import time
import numpy as np
import tensorflow as tf
# Basic model parameters as external flags.
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_integer('num_epochs', 2, 'Number of epochs to run trainer.')
flags.DEFINE_integer('batch_size', 100, 'Batch size.')
flags.DEFINE_string('train_dir', '/root/data', 'Directory with the training data.')
#flags.DEFINE_string('train_dir', '/root/data2', 'Directory with the training data.')
# Constants used for dealing with the files, matches convert_to_records.
TRAIN_FILE = 'train.tfrecords'
VALIDATION_FILE = 'validation.tfrecords'
# Set-up dos pacotes
sess = tf.InteractiveSession()
def read_and_decode(filename_queue):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64),
})
# Convert from a scalar string tensor (whose single string has
# length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
# [mnist.IMAGE_PIXELS].
image = tf.decode_raw(features['image_raw'], tf.uint8)
image.set_shape([784])
# OPTIONAL: Could reshape into a 28x28 image and apply distortions
# here. Since we are not applying any distortions in this
# example, and the next step expects the image to be flattened
# into a vector, we don't bother.
# Convert from [0, 255] -> [-0.5, 0.5] floats.
image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
# Convert label from a scalar uint8 tensor to an int32 scalar.
label = tf.cast(features['label'], tf.int32)
return image, label
def inputs(train, batch_size, num_epochs):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and validation (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, 30,26,1]
in the range [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
a number in the range [0, char letras).
Note that an tf.train.QueueRunner is added to the graph, which
must be run using e.g. tf.train.start_queue_runners().
"""
if not num_epochs: num_epochs = None
filename = os.path.join(FLAGS.train_dir,
TRAIN_FILE if train else VALIDATION_FILE)
with tf.name_scope('input'):
filename_queue = tf.train.string_input_producer(
[filename], num_epochs=num_epochs)
# Even when reading in multiple threads, share the filename
# queue.
image, label = read_and_decode(filename_queue)
# Shuffle the examples and collect them into batch_size batches.
# (Internally uses a RandomShuffleQueue.)
# We run this in two threads to avoid being a bottleneck.
images, sparse_labels = tf.train.shuffle_batch(
[image, label], batch_size=batch_size, num_threads=2,
capacity=1000 + 3 * batch_size,
# Ensures a minimum amount of shuffling of examples.
min_after_dequeue=1000)
return images, sparse_labels
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
#Variaveis
x, y_ = inputs(train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs)
#onehot_y_ = tf.one_hot(y_, 36, dtype=tf.float32)
#y_ = tf.string_to_number(y_, out_type=tf.int32)
#Layer 1
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
#Layer 2
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
#Densely Connected Layer
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
#Dropout - reduz overfitting
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
#Readout layer
W_fc2 = weight_variable([1024, 36])
b_fc2 = bias_variable([36])
#y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
#Train and evaluate
#cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
#cross_entropy = tf.reduce_mean(-tf.reduce_sum(onehot_y_ * tf.log(y_conv), reduction_indices=[1]))
cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(y_conv, y_))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(20000):
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={keep_prob: 0.5})
x, y_ = inputs(train=True, batch_size=2000)
#y_ = tf.string_to_number(y_, out_type=tf.int32)
print("test accuracy %g"%accuracy.eval(feed_dict={keep_prob: 1.0}))
coord.join(threads)
sess.close()
我尝试将num_epochs更改为10000和None，但会显示相同的错误消息。我想知道是否有人知道如何解决这个问题。
由于 马塞洛
答案 0 :(得分：0)
这看起来像是image.set_shape（[784]）的问题。错误是说它期待大小[784]，但它得到了[6272]。我对本教程半熟悉，图像应该是28x28，这样可以给你784的大小，但也许有6272个图像，你的尺寸很混乱，因为第一个尺寸应该是观察量而不是尺寸一次观察？对不起，这不是一个具体的答案，但我会从那里开始。