我一直在尝试使用tensorflow实现cnn。我已经制作了与Cifar10几乎相同的数据集格式，但总共有三个类。这是a link ，也是this page的帮助。我的代码向我显示此错误，我无法调试它。善意的帮助。 感谢。
tensorflow/core/framework/op_kernel.cc:993] Invalid argument: Received a label value of 253 which is outside the valid range of [0, 3). Label values: 11 121 3 59 194 190 239 11 207 33 138 60 186 63 156 250 187 61 223 60 180 40 186 187 251 200 66 154 253 60 245 47 189 168 86 93 61 62 61 62 52 150 94 172 143 23 60 142 59 28 60 149 15 100 248 149 196 189 159 212 178 152 65 189 9 241 189 62 189 21 60 244 47 48 196 47 66 56 101 22 190 190 60 91 204 21 147 61 75 223 27 168 223 149 61 82 246 186 190 211 190 186 125 103 162 134 61 202 239 189 32 188 90 187 189 172 75 200 76 122 11 46 72 252 190 63 118 189
Traceback (most recent call last):
File "cifar10_train.py", line 127, in <module>
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 44, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "cifar10_train.py", line 123, in main
train()
File "cifar10_train.py", line 115, in train
mon_sess.run(train_op)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 462, in run
run_metadata=run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 786, in run
run_metadata=run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 744, in run
return self._sess.run(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 891, in run
run_metadata=run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 744, in run
return self._sess.run(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 767, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 965, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1015, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1035, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Received a label value of 253 which is outside the valid range of [0, 3). Label values: 11 121 3 59 194 190 239 11 207 33 138 60 186 63 156 250 187 61 223 60 180 40 186 187 251 200 66 154 253 60 245 47 189 168 86 93 61 62 61 62 52 150 94 172 143 23 60 142 59 28 60 149 15 100 248 149 196 189 159 212 178 152 65 189 9 241 189 62 189 21 60 244 47 48 196 47 66 56 101 22 190 190 60 91 204 21 147 61 75 223 27 168 223 149 61 82 246 186 190 211 190 186 125 103 162 134 61 202 239 189 32 188 90 187 189 172 75 200 76 122 11 46 72 252 190 63 118 189
[[Node: cross_entropy_per_example/cross_entropy_per_example = SparseSoftmaxCrossEntropyWithLogits[T=DT_FLOAT, Tlabels=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](softmax_linear/softmax_linear, Cast_4)]]
Caused by op u'cross_entropy_per_example/cross_entropy_per_example', defined at:
File "cifar10_train.py", line 127, in <module>
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 44, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "cifar10_train.py", line 123, in main
train()
File "cifar10_train.py", line 75, in train
loss = cifar10.loss(logits, labels)
File "/home/nitakshi/Downloads/models-master/tutorials/image/cifar10/cifar10.py", line 309, in loss
labels=labels, logits=logits, name='cross_entropy_per_example')
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_ops.py", line 1713, in sparse_softmax_cross_entropy_with_logits
precise_logits, labels, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 2378, in _sparse_softmax_cross_entropy_with_logits
features=features, labels=labels, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2327, 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 1226, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Received a label value of 253 which is outside the valid range of [0, 3). Label values: 11 121 3 59 194 190 239 11 207 33 138 60 186 63 156 250 187 61 223 60 180 40 186 187 251 200 66 154 253 60 245 47 189 168 86 93 61 62 61 62 52 150 94 172 143 23 60 142 59 28 60 149 15 100 248 149 196 189 159 212 178 152 65 189 9 241 189 62 189 21 60 244 47 48 196 47 66 56 101 22 190 190 60 91 204 21 147 61 75 223 27 168 223 149 61 82 246 186 190 211 190 186 125 103 162 134 61 202 239 189 32 188 90 187 189 172 75 200 76 122 11 46 72 252 190 63 118 189
[[Node: cross_entropy_per_example/cross_entropy_per_example = SparseSoftmaxCrossEntropyWithLogits[T=DT_FLOAT, Tlabels=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](softmax_linear/softmax_linear, Cast_4)]]
Cifar输入代码：
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#s
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Routine for decoding the CIFAR-10 binary file format."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
# Process images of this size. Note that this differs from the original CIFAR
# image size of 32 x 32. If one alters this number, then the entire model
# architecture will change and any model would need to be retrained.
IMAGE_SIZE = 32
# Global constants describing the CIFAR-10 data set.
NUM_CLASSES = 3
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000
#NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 2000
#NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 2000
def read_cifar10(filename_queue):
"""Reads and parses examples from CIFAR10 data files.
Recommendation: if you want N-way read parallelism, call this function
N times. This will give you N independent Readers reading different
files & positions within those files, which will give better mixing of
examples.
Args:
filename_queue: A queue of strings with the filenames to read from.
Returns:
An object representing a single example, with the following fields:
height: number of rows in the result (32)
width: number of columns in the result (32)
depth: number of color channels in the result (3)
key: a scalar string Tensor describing the filename & record number
for this example.
label: an int32 Tensor with the label in the range 0..9.
uint8image: a [height, width, depth] uint8 Tensor with the image data
"""
class CIFAR10Record(object):
pass
result = CIFAR10Record()
print(result)
# Dimensions of the images in the CIFAR-10 dataset.
# See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the
# input format.
label_bytes = 1 # 2 for CIFAR-100
result.height = 32
result.width = 32
result.depth = 3
image_bytes = result.height * result.width * result.depth
print('img bytes@@@@@@')
print(image_bytes)
# Every record consists of a label followed by the image, with a
# fixed number of bytes for each.
record_bytes = label_bytes + image_bytes
# Read a record, getting filenames from the filename_queue. No
# header or footer in the CIFAR-10 format, so we leave header_bytes
# and footer_bytes at their default of 0.
reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
result.key, value = reader.read(filename_queue)
# Convert from a string to a vector of uint8 that is record_bytes long.
record_bytes = tf.decode_raw(value, tf.uint8)
# The first bytes represent the label, which we convert from uint8->int32.
#result.label = tf.cast(
# tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
result.label = tf.cast(
tf.slice(record_bytes, [0], [label_bytes]), tf.int32)
print('result label############:')
print(result.label)
# The remaining bytes after the label represent the image, which we reshape
# from [depth * height * width] to [depth, height, width].
depth_major = tf.reshape(
tf.strided_slice(record_bytes, [label_bytes],
[label_bytes + image_bytes]),
[result.depth, result.height, result.width])
# Convert from [depth, height, width] to [height, width, depth].
result.uint8image = tf.transpose(depth_major, [1, 2, 0])
return result
def _generate_image_and_label_batch(image, label, min_queue_examples,
batch_size, shuffle):
"""Construct a queued batch of images and labels.
Args:
image: 3-D Tensor of [height, width, 3] of type.float32.
label: 1-D Tensor of type.int32
min_queue_examples: int32, minimum number of samples to retain
in the queue that provides of batches of examples.
batch_size: Number of images per batch.
shuffle: boolean indicating whether to use a shuffling queue.
Returns:
images: Images. 4D tensor of [batch_size, height, width, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
"""
# Create a queue that shuffles the examples, and then
# read 'batch_size' images + labels from the example queue.
num_preprocess_threads = 16
if shuffle:
images, label_batch = tf.train.shuffle_batch(
[image, label],
batch_size=batch_size,
num_threads=num_preprocess_threads,
capacity=min_queue_examples + 3 * batch_size,
min_after_dequeue=min_queue_examples)
else:
images, label_batch = tf.train.batch(
[image, label],
batch_size=batch_size,
num_threads=num_preprocess_threads,
capacity=min_queue_examples + 3 * batch_size)
# Display the training images in the visualizer.
tf.summary.image('images', images)
print(images)
return images, tf.reshape(label_batch, [batch_size])
def distorted_inputs(data_dir, batch_size):
"""Construct distorted input for CIFAR training using the Reader ops.
Args:
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
"""
filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
#filenames = [os.path.join(data_dir, '28febtrain')
for i in xrange(1, 5)]
#filenames = [os.path.join(data_dir, '28febtrain')]
for f in filenames:
if not tf.gfile.Exists(f):
raise ValueError('Failed to find file: ' + f)
# Create a queue that produces the filenames to read.
filename_queue = tf.train.string_input_producer(filenames)
# Read examples from files in the filename queue.
read_input = read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
height = IMAGE_SIZE
width = IMAGE_SIZE
print('img height, width')
print(height)
print(width)
# Image processing for training the network. Note the many random
# distortions applied to the image.
# Randomly crop a [height, width] section of the image.
distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
# Randomly flip the image horizontally.
distorted_image = tf.image.random_flip_left_right(distorted_image)
# Because these operations are not commutative, consider randomizing
# the order their operation.
distorted_image = tf.image.random_brightness(distorted_image,
max_delta=63)
distorted_image = tf.image.random_contrast(distorted_image,
lower=0.2, upper=1.8)
# Subtract off the mean and divide by the variance of the pixels.
float_image = tf.image.per_image_standardization(distorted_image)
# Set the shapes of tensors.
float_image.set_shape([height, width, 3])
read_input.label.set_shape([1])
# Ensure that the random shuffling has good mixing properties.
min_fraction_of_examples_in_queue = 0.4
min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
min_fraction_of_examples_in_queue)
print ('Filling queue with %d CIFAR images before starting to train. '
'This will take a few minutes.' % min_queue_examples)
# Generate a batch of images and labels by building up a queue of examples.
return _generate_image_and_label_batch(float_image, read_input.label,
min_queue_examples, batch_size,
shuffle=True)
def inputs(eval_data, data_dir, batch_size):
"""Construct input for CIFAR evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
"""
if not eval_data:
filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
#filenames = [os.path.join(data_dir, '28febtrain')
for i in xrange(1, 6)]
num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
else:
filenames = [os.path.join(data_dir, '28febtrain')]
num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
for f in filenames:
if not tf.gfile.Exists(f):
raise ValueError('Failed to find file: ' + f)
# Create a queue that produces the filenames to read.
filename_queue = tf.train.string_input_producer(filenames)
# Read examples from files in the filename queue.
read_input = read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
height = IMAGE_SIZE
width = IMAGE_SIZE
# Image processing for evaluation.
# Crop the central [height, width] of the image.
resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,
height, width)
# Subtract off the mean and divide by the variance of the pixels.
float_image = tf.image.per_image_standardization(resized_image)
# Set the shapes of tensors.
float_image.set_shape([height, width, 3])
read_input.label.set_shape([1])
# Ensure that the random shuffling has good mixing properties.
min_fraction_of_examples_in_queue = 0.4
min_queue_examples = int(num_examples_per_epoch *
min_fraction_of_examples_in_queue)
# Generate a batch of images and labels by building up a queue of examples.
return _generate_image_and_label_batch(float_image, read_input.label,
min_queue_examples, batch_size,
shuffle=False)
Cifar Train代码与github相同。
代码结束 此文件调用的其余函数与上面提供的github链接中存在的函数相同。 groundtruth的类型是：我只给出了几行文件的值作为一个巨大的数据集。第一个col显示标签值，另一个是要处理的值 0 -0.3056471033 -0.0466023552 0.0033290606 0.0116261395 -0.0136461613 0.0064174382 0.0084394668 0.0064852377 -0.0003195472 0.0130523434 0.0081351981 -0.0041750822 -0.0044139047 0.0009210015 -0.0033359823 0.011423628 -0.0090784218 -0.0014336071 0.0029341407 -0.0083200129 -0.0014352675 -0.0060231589 0.002385679 0.001362363 0.0051867442 0.001592935 0.014525627 0.0014239945 -0.0030832436 0.0047563972 0.0008349333 -0.0040918221 -0.0061690423 0.009810869 -0.0006399579 -0.002112322 0.0028194289 -0.000801686 0.0012672692 -0.0028961465 0.0027815595 0.0007334416 0.0055782681 0.001759698 0.0097706833 -0.0137690884 0.0119607859 -0.0056124537 -0.0073978555 0.0128119595 0.0083815554 ------等与第一个是在标签
，例如3072倍的值请帮忙。 感谢。
答案 0 :(得分：0)
回溯并没有大的帮助，但看起来您还没有正确编辑代码以将其更改为3个类。例如，您将num_classes设置为10.
如果你有新数据，那么你可能需要检查你的地面实况数据，以确保它被正确标记为3类。