as_list() 未在未知 TensorShape 上定义
as_list() is not defined on an unknown TensorShape
**更新
实施@jdehesa 回答后：我的代码如下所示：
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
import PIL as pil
from tensorflow import feature_column
from tensorflow_core.python.platform.flags import FLAGS
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def print_type(name , x):
print(" {} type = {}".format(name, type(x)))
def _bytes_feature(value):
"""Returns a bytes_list from a string / byte."""
if isinstance(value, type(tf.constant(0))):
value = value.numpy() # BytesList won't unpack a string from an EagerTensor.
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _float_feature(value):
if not isinstance(value, np.ndarray):
value = [value]
"""Returns a float_list from a float / double."""
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def _int64_feature(value):
"""Returns an int64_list from a bool / enum / int / uint."""
if not isinstance(value, np.ndarray):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def encode_example(arr, label):
shape = arr.shape
feature = {
'height': _int64_feature(shape[0]),
'width': _int64_feature(shape[1]),
'label': _int64_feature(label),
'image_raw': _bytes_feature(arr.flatten().tobytes()),
}
# print("Encoding type {}".format(type(feature['image_raw'])))
return tf.train.Example(features=tf.train.Features(feature=feature))
def decode_example(serialized_example):
# Create a dictionary describing the features.
image_feature_description = {
'height': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'label': tf.io.FixedLenFeature([], tf.int64),
'image_raw': tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(serialized_example, image_feature_description)
return example
def map_example(height, width, image_raw, label):
# Assumes little endian decoding, pass little_endian=False for big endian
image_data = tf.io.decode_raw(image_raw, tf.uint8)
image_data = tf.reshape(image_data, [height, width])
return image_data, label
def make_dataset(partition):
files = tf.data.Dataset.list_files("images_" + partition + "*.tfrecord")
dataset = tf.data.TFRecordDataset(files)
# dataset = dataset.shuffle(buffer_size=FLAGS.shuffle_buffer_size)
dataset = dataset.map(decode_example)
dataset = dataset.map(
lambda x: map_example(x['height'], x['width'], x['image_raw'], x['label']))
# dataset = dataset.batch(batch_size=FLAGS.batch_size)
return dataset
def write_examples_to_record_file(file_name, x , y):
with tf.io.TFRecordWriter(file_name) as writer:
for i in range(len(x)):
tf_example = encode_example(x[i], y[i])
writer.write(tf_example.SerializeToString())
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, y_train, x_test, y_test = x_train[:100], y_train[:100], x_test[:100], y_test[:100]
# x_train, x_test = x_train.astype(np.int8), x_test.astype(np.int8)
write_examples_to_record_file('images_train.tfrecord', x_train, y_train)
# write_examples_to_record_file('images_test.tfrecord', x_test, y_test)
train_dataset = make_dataset("train")
# test_dataset = make_dataset("test")
it = iter(train_dataset)
r = next(it)
print_type("r",r)
(x,y) = r
print_type("X", x)
print_type("Y", y)
print("x is" , x)
print("y is" , y)
print("x shape is" , x.shape())
print("y shape is" , y.shape())
# print(next(it))
# for r,label in next(it):
# print(repr(r))
# print("feature shape = {}".format(r.shape.as_list()))
# print("label shape = {}".format(label.shape.as_list()))
# feature_column = [tf.feature_column.numeric_column(key='image', shape=(28,28))]
# feature_layer = tf.keras.layers.DenseFeatures(feature_column)
#
# it = iter(train_dataset)
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=[28,28]),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_dataset, epochs=5)
# model.evaluate(test_dataset, verbose=2)
但现在错误不同了，我认为这是原始问题错误的原因，即获取 next() 元组的 x,y 分量的形状时出现问题：
2019-11-19 11:11:54.540221: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_100.dll'; dlerror: cudart64_100.dll not found
2019-11-19 11:11:56.763955: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2019-11-19 11:11:56.764410: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)
2019-11-19 11:11:56.767167: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: szclu-dvcasa027
2019-11-19 11:11:56.767572: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: szclu-dvcasa027
2019-11-19 11:11:56.768026: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
WARNING:tensorflow:Entity <function decode_example at 0x000002A67F5EE438> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: No module named 'tensorflow_core.estimator'
WARNING:tensorflow:Entity <function make_dataset.<locals>.<lambda> at 0x000002A67F61C0D8> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: No module named 'tensorflow_core.estimator'
r type = <class 'tuple'>
X type = <class 'tensorflow.python.framework.ops.EagerTensor'>
Y type = <class 'tensorflow.python.framework.ops.EagerTensor'>
x is tf.Tensor(
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 3 18 18 18 126 136
175 26 166 255 247 127 0 0 0 0]
[ 0 0 0 0 0 0 0 0 30 36 94 154 170 253 253 253 253 253
225 172 253 242 195 64 0 0 0 0]
[ 0 0 0 0 0 0 0 49 238 253 253 253 253 253 253 253 253 251
93 82 82 56 39 0 0 0 0 0]
[ 0 0 0 0 0 0 0 18 219 253 253 253 253 253 198 182 247 241
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 80 156 107 253 253 205 11 0 43 154
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 14 1 154 253 90 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 139 253 190 2 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 11 190 253 70 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 35 241 225 160 108 1
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 81 240 253 253 119
25 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45 186 253 253
150 27 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 93 252
253 187 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249
253 249 64 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46 130 183 253
253 207 2 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 39 148 229 253 253 253
250 182 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 24 114 221 253 253 253 253 201
78 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 23 66 213 253 253 253 253 198 81 2
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 18 171 219 253 253 253 253 195 80 9 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 55 172 226 253 253 253 253 244 133 11 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 136 253 253 253 212 135 132 16 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]], shape=(28, 28), dtype=uint8)
y is tf.Tensor(5, shape=(), dtype=int64)
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "C:\Users\me\.IntelliJIdea2019.3\config\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Users\me\.IntelliJIdea2019.3\config\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/data/projects/cludeeplearning/train_model_so.py", line 108, in <module>
print("x shape is" , x.shape())
TypeError: 'TensorShape' object is not callable
============================================= ================================
我正在尝试使用 tfrecords 进行试验，因为最终我们会将大量数据存储在多个文件中，而这些文件可能不适合内存。
此 POC 的基本思想是从 mnist 数据集加载数据，将其保存在 tfrecrod 文件中作为 tf.example，使用 tfrecorddataset 从 tfrecord 加载数据并训练模型。
这是我的代码示例：
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
import PIL as pil
from tensorflow import feature_column
from tensorflow_core.python.platform.flags import FLAGS
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def _bytes_feature(value):
"""Returns a bytes_list from a string / byte."""
if isinstance(value, type(tf.constant(0))):
value = value.numpy() # BytesList won't unpack a string from an EagerTensor.
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _float_feature(value):
if not isinstance(value, np.ndarray):
value = [value]
"""Returns a float_list from a float / double."""
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def _int64_feature(value):
"""Returns an int64_list from a bool / enum / int / uint."""
if not isinstance(value, np.ndarray):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def encode_example(arr, label):
shape = arr.shape
feature = {
'height': _int64_feature(shape[0]),
'width': _int64_feature(shape[1]),
'label': _int64_feature(label),
'image_raw': _bytes_feature(arr.flatten().tobytes()),
}
# print("Encoding type {}".format(type(feature['image_raw'])))
return tf.train.Example(features=tf.train.Features(feature=feature))
def decode_example(serialized_example):
# Create a dictionary describing the features.
image_feature_description = {
'height': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'label': tf.io.FixedLenFeature([], tf.int64),
'image_raw': tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(serialized_example, image_feature_description)
return example
def map_example(height, width, image_raw, label):
image_data = np.frombuffer(image_raw.numpy(), dtype=np.dtype('int64'))
image_data = tf.reshape(image_data, [height.numpy(), width.numpy()])
# image_data.set_shape([28,28])
label = tf.constant([label.numpy()], tf.int64)
return image_data, label
def make_dataset(partition):
files = tf.data.Dataset.list_files("images_" + partition + "*.tfrecord")
dataset = tf.data.TFRecordDataset(files)
# dataset = dataset.shuffle(buffer_size=FLAGS.shuffle_buffer_size)
dataset = dataset.map(decode_example)
dataset = dataset.map(lambda x: tf.py_function(func=map_example, inp=[x['height'], x['width'], x['image_raw'], x['label']], Tout=(tf.int64, tf.int64)))
# dataset = dataset.batch(batch_size=FLAGS.batch_size)
return dataset
def write_examples_to_record_file(file_name, x , y):
with tf.io.TFRecordWriter(file_name) as writer:
for i in range(len(x)):
tf_example = encode_example(x[i], y[i])
writer.write(tf_example.SerializeToString())
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, y_train, x_test, y_test = x_train[:100], y_train[:100], x_test[:100], y_test[:100]
x_train, x_test = x_train / 255.0, x_test / 255.0
write_examples_to_record_file('images_train.tfrecord', x_train, y_train)
# write_examples_to_record_file('images_test.tfrecord', x_test, y_test)
train_dataset = make_dataset("train")
# test_dataset = make_dataset("test")
for r in train_dataset.take(1):
print(r[0].shape.as_list())
print(r[1])
# feature_column = [tf.feature_column.numeric_column(key='image', shape=(28,28))]
# feature_layer = tf.keras.layers.DenseFeatures(feature_column)
#
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=[28,28,1]),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_dataset, epochs=5)
# model.evaluate(test_dataset, verbose=2)
我在开始训练之前收到以下错误，知道为什么它不起作用吗？
2019-11-19 06:31:21.067987: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_100.dll'; dlerror: cudart64_100.dll not found
2019-11-19 06:31:23.315270: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2019-11-19 06:31:23.315617: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)
2019-11-19 06:31:23.320751: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: szclu-dvcasa027
2019-11-19 06:31:23.321132: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: szclu-dvcasa027
2019-11-19 06:31:23.321927: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
WARNING:tensorflow:Entity <function decode_example at 0x00000250EE94A1F8> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: No module named 'tensorflow_core.estimator'
WARNING:tensorflow:Entity <function make_dataset.<locals>.<lambda> at 0x00000250EE94A438> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: No module named 'tensorflow_core.estimator'
feature shape = [28, 28]
label shape = [1]
Epoch 1/5
Traceback (most recent call last):
File "C:/data/projects/cludeeplearning/train_model.py", line 110, in <module>
model.fit(train_dataset, epochs=5)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 728, in fit
1/Unknown - 0s 13ms/step
1/Unknown - 0s 13ms/step use_multiprocessing=use_multiprocessing)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 324, in fit
total_epochs=epochs)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 123, in run_one_epoch
batch_outs = execution_function(iterator)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 86, in execution_function
distributed_function(input_fn))
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 457, in __call__
result = self._call(*args, **kwds)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 503, in _call
self._initialize(args, kwds, add_initializers_to=initializer_map)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 408, in _initialize
*args, **kwds))
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\function.py", line 1848, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\function.py", line 2150, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\function.py", line 2041, in _create_graph_function
capture_by_value=self._capture_by_value),
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\framework\func_graph.py", line 915, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 358, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 66, in distributed_function
model, input_iterator, mode)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 112, in _prepare_feed_values
inputs, targets, sample_weights = _get_input_from_iterator(inputs)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 149, in _get_input_from_iterator
distribution_strategy_context.get_strategy(), x, y, sample_weights)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\distribute\distributed_training_utils.py", line 308, in validate_distributed_dataset_inputs
x_values_list = validate_per_replica_inputs(distribution_strategy, x)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\distribute\distributed_training_utils.py", line 356, in validate_per_replica_inputs
validate_all_tensor_shapes(x, x_values)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\distribute\distributed_training_utils.py", line 373, in validate_all_tensor_shapes
x_shape = x_values[0].shape.as_list()
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\framework\tensor_shape.py", line 1171, in as_list
raise ValueError("as_list() is not defined on an unknown TensorShape.")
ValueError: as_list() is not defined on an unknown TensorShape.
Process finished with exit code 1
感谢任何帮助。谢谢
如果没有独立的示例进行测试，很难确定，但我认为问题很可能来自使用 tf.py_func，它可能缺少张量的形状信息。不过你在这里不需要它，我认为你可以像这样定义 map_example 和 make_dataset ：
def map_example(height, width, image_raw, label):
image_data = tf.io.decode_raw(image_raw, tf.uint8)
image_data = tf.reshape(image_data, [1, height, width])
return image_data, label
def make_dataset(partition):
files = tf.data.Dataset.list_files("images_" + partition + "*.tfrecord")
dataset = tf.data.TFRecordDataset(files)
# dataset = dataset.shuffle(buffer_size=FLAGS.shuffle_buffer_size)
dataset = dataset.map(decode_example)
dataset = dataset.map(
lambda x: map_example(x['height'], x['width'], x['image_raw'], x['label']))
# dataset = dataset.batch(batch_size=FLAGS.batch_size)
return dataset
**更新 实施@jdehesa 回答后：我的代码如下所示：
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
import PIL as pil
from tensorflow import feature_column
from tensorflow_core.python.platform.flags import FLAGS
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def print_type(name , x):
print(" {} type = {}".format(name, type(x)))
def _bytes_feature(value):
"""Returns a bytes_list from a string / byte."""
if isinstance(value, type(tf.constant(0))):
value = value.numpy() # BytesList won't unpack a string from an EagerTensor.
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _float_feature(value):
if not isinstance(value, np.ndarray):
value = [value]
"""Returns a float_list from a float / double."""
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def _int64_feature(value):
"""Returns an int64_list from a bool / enum / int / uint."""
if not isinstance(value, np.ndarray):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def encode_example(arr, label):
shape = arr.shape
feature = {
'height': _int64_feature(shape[0]),
'width': _int64_feature(shape[1]),
'label': _int64_feature(label),
'image_raw': _bytes_feature(arr.flatten().tobytes()),
}
# print("Encoding type {}".format(type(feature['image_raw'])))
return tf.train.Example(features=tf.train.Features(feature=feature))
def decode_example(serialized_example):
# Create a dictionary describing the features.
image_feature_description = {
'height': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'label': tf.io.FixedLenFeature([], tf.int64),
'image_raw': tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(serialized_example, image_feature_description)
return example
def map_example(height, width, image_raw, label):
# Assumes little endian decoding, pass little_endian=False for big endian
image_data = tf.io.decode_raw(image_raw, tf.uint8)
image_data = tf.reshape(image_data, [height, width])
return image_data, label
def make_dataset(partition):
files = tf.data.Dataset.list_files("images_" + partition + "*.tfrecord")
dataset = tf.data.TFRecordDataset(files)
# dataset = dataset.shuffle(buffer_size=FLAGS.shuffle_buffer_size)
dataset = dataset.map(decode_example)
dataset = dataset.map(
lambda x: map_example(x['height'], x['width'], x['image_raw'], x['label']))
# dataset = dataset.batch(batch_size=FLAGS.batch_size)
return dataset
def write_examples_to_record_file(file_name, x , y):
with tf.io.TFRecordWriter(file_name) as writer:
for i in range(len(x)):
tf_example = encode_example(x[i], y[i])
writer.write(tf_example.SerializeToString())
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, y_train, x_test, y_test = x_train[:100], y_train[:100], x_test[:100], y_test[:100]
# x_train, x_test = x_train.astype(np.int8), x_test.astype(np.int8)
write_examples_to_record_file('images_train.tfrecord', x_train, y_train)
# write_examples_to_record_file('images_test.tfrecord', x_test, y_test)
train_dataset = make_dataset("train")
# test_dataset = make_dataset("test")
it = iter(train_dataset)
r = next(it)
print_type("r",r)
(x,y) = r
print_type("X", x)
print_type("Y", y)
print("x is" , x)
print("y is" , y)
print("x shape is" , x.shape())
print("y shape is" , y.shape())
# print(next(it))
# for r,label in next(it):
# print(repr(r))
# print("feature shape = {}".format(r.shape.as_list()))
# print("label shape = {}".format(label.shape.as_list()))
# feature_column = [tf.feature_column.numeric_column(key='image', shape=(28,28))]
# feature_layer = tf.keras.layers.DenseFeatures(feature_column)
#
# it = iter(train_dataset)
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=[28,28]),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_dataset, epochs=5)
# model.evaluate(test_dataset, verbose=2)
但现在错误不同了，我认为这是原始问题错误的原因，即获取 next() 元组的 x,y 分量的形状时出现问题：
2019-11-19 11:11:54.540221: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_100.dll'; dlerror: cudart64_100.dll not found
2019-11-19 11:11:56.763955: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2019-11-19 11:11:56.764410: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)
2019-11-19 11:11:56.767167: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: szclu-dvcasa027
2019-11-19 11:11:56.767572: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: szclu-dvcasa027
2019-11-19 11:11:56.768026: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
WARNING:tensorflow:Entity <function decode_example at 0x000002A67F5EE438> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: No module named 'tensorflow_core.estimator'
WARNING:tensorflow:Entity <function make_dataset.<locals>.<lambda> at 0x000002A67F61C0D8> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: No module named 'tensorflow_core.estimator'
r type = <class 'tuple'>
X type = <class 'tensorflow.python.framework.ops.EagerTensor'>
Y type = <class 'tensorflow.python.framework.ops.EagerTensor'>
x is tf.Tensor(
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 3 18 18 18 126 136
175 26 166 255 247 127 0 0 0 0]
[ 0 0 0 0 0 0 0 0 30 36 94 154 170 253 253 253 253 253
225 172 253 242 195 64 0 0 0 0]
[ 0 0 0 0 0 0 0 49 238 253 253 253 253 253 253 253 253 251
93 82 82 56 39 0 0 0 0 0]
[ 0 0 0 0 0 0 0 18 219 253 253 253 253 253 198 182 247 241
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 80 156 107 253 253 205 11 0 43 154
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 14 1 154 253 90 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 139 253 190 2 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 11 190 253 70 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 35 241 225 160 108 1
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 81 240 253 253 119
25 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45 186 253 253
150 27 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 93 252
253 187 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249
253 249 64 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46 130 183 253
253 207 2 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 39 148 229 253 253 253
250 182 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 24 114 221 253 253 253 253 201
78 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 23 66 213 253 253 253 253 198 81 2
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 18 171 219 253 253 253 253 195 80 9 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 55 172 226 253 253 253 253 244 133 11 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 136 253 253 253 212 135 132 16 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0]], shape=(28, 28), dtype=uint8)
y is tf.Tensor(5, shape=(), dtype=int64)
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "C:\Users\me\.IntelliJIdea2019.3\config\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Users\me\.IntelliJIdea2019.3\config\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/data/projects/cludeeplearning/train_model_so.py", line 108, in <module>
print("x shape is" , x.shape())
TypeError: 'TensorShape' object is not callable
============================================= ================================ 我正在尝试使用 tfrecords 进行试验，因为最终我们会将大量数据存储在多个文件中，而这些文件可能不适合内存。
此 POC 的基本思想是从 mnist 数据集加载数据，将其保存在 tfrecrod 文件中作为 tf.example，使用 tfrecorddataset 从 tfrecord 加载数据并训练模型。
这是我的代码示例：
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
import PIL as pil
from tensorflow import feature_column
from tensorflow_core.python.platform.flags import FLAGS
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def _bytes_feature(value):
"""Returns a bytes_list from a string / byte."""
if isinstance(value, type(tf.constant(0))):
value = value.numpy() # BytesList won't unpack a string from an EagerTensor.
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _float_feature(value):
if not isinstance(value, np.ndarray):
value = [value]
"""Returns a float_list from a float / double."""
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def _int64_feature(value):
"""Returns an int64_list from a bool / enum / int / uint."""
if not isinstance(value, np.ndarray):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def encode_example(arr, label):
shape = arr.shape
feature = {
'height': _int64_feature(shape[0]),
'width': _int64_feature(shape[1]),
'label': _int64_feature(label),
'image_raw': _bytes_feature(arr.flatten().tobytes()),
}
# print("Encoding type {}".format(type(feature['image_raw'])))
return tf.train.Example(features=tf.train.Features(feature=feature))
def decode_example(serialized_example):
# Create a dictionary describing the features.
image_feature_description = {
'height': tf.io.FixedLenFeature([], tf.int64),
'width': tf.io.FixedLenFeature([], tf.int64),
'label': tf.io.FixedLenFeature([], tf.int64),
'image_raw': tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(serialized_example, image_feature_description)
return example
def map_example(height, width, image_raw, label):
image_data = np.frombuffer(image_raw.numpy(), dtype=np.dtype('int64'))
image_data = tf.reshape(image_data, [height.numpy(), width.numpy()])
# image_data.set_shape([28,28])
label = tf.constant([label.numpy()], tf.int64)
return image_data, label
def make_dataset(partition):
files = tf.data.Dataset.list_files("images_" + partition + "*.tfrecord")
dataset = tf.data.TFRecordDataset(files)
# dataset = dataset.shuffle(buffer_size=FLAGS.shuffle_buffer_size)
dataset = dataset.map(decode_example)
dataset = dataset.map(lambda x: tf.py_function(func=map_example, inp=[x['height'], x['width'], x['image_raw'], x['label']], Tout=(tf.int64, tf.int64)))
# dataset = dataset.batch(batch_size=FLAGS.batch_size)
return dataset
def write_examples_to_record_file(file_name, x , y):
with tf.io.TFRecordWriter(file_name) as writer:
for i in range(len(x)):
tf_example = encode_example(x[i], y[i])
writer.write(tf_example.SerializeToString())
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, y_train, x_test, y_test = x_train[:100], y_train[:100], x_test[:100], y_test[:100]
x_train, x_test = x_train / 255.0, x_test / 255.0
write_examples_to_record_file('images_train.tfrecord', x_train, y_train)
# write_examples_to_record_file('images_test.tfrecord', x_test, y_test)
train_dataset = make_dataset("train")
# test_dataset = make_dataset("test")
for r in train_dataset.take(1):
print(r[0].shape.as_list())
print(r[1])
# feature_column = [tf.feature_column.numeric_column(key='image', shape=(28,28))]
# feature_layer = tf.keras.layers.DenseFeatures(feature_column)
#
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=[28,28,1]),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_dataset, epochs=5)
# model.evaluate(test_dataset, verbose=2)
我在开始训练之前收到以下错误，知道为什么它不起作用吗？
2019-11-19 06:31:21.067987: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_100.dll'; dlerror: cudart64_100.dll not found
2019-11-19 06:31:23.315270: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2019-11-19 06:31:23.315617: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)
2019-11-19 06:31:23.320751: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: szclu-dvcasa027
2019-11-19 06:31:23.321132: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: szclu-dvcasa027
2019-11-19 06:31:23.321927: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
WARNING:tensorflow:Entity <function decode_example at 0x00000250EE94A1F8> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: No module named 'tensorflow_core.estimator'
WARNING:tensorflow:Entity <function make_dataset.<locals>.<lambda> at 0x00000250EE94A438> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: No module named 'tensorflow_core.estimator'
feature shape = [28, 28]
label shape = [1]
Epoch 1/5
Traceback (most recent call last):
File "C:/data/projects/cludeeplearning/train_model.py", line 110, in <module>
model.fit(train_dataset, epochs=5)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 728, in fit
1/Unknown - 0s 13ms/step
1/Unknown - 0s 13ms/step use_multiprocessing=use_multiprocessing)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 324, in fit
total_epochs=epochs)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 123, in run_one_epoch
batch_outs = execution_function(iterator)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 86, in execution_function
distributed_function(input_fn))
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 457, in __call__
result = self._call(*args, **kwds)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 503, in _call
self._initialize(args, kwds, add_initializers_to=initializer_map)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 408, in _initialize
*args, **kwds))
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\function.py", line 1848, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\function.py", line 2150, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\function.py", line 2041, in _create_graph_function
capture_by_value=self._capture_by_value),
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\framework\func_graph.py", line 915, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 358, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 66, in distributed_function
model, input_iterator, mode)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 112, in _prepare_feed_values
inputs, targets, sample_weights = _get_input_from_iterator(inputs)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 149, in _get_input_from_iterator
distribution_strategy_context.get_strategy(), x, y, sample_weights)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\distribute\distributed_training_utils.py", line 308, in validate_distributed_dataset_inputs
x_values_list = validate_per_replica_inputs(distribution_strategy, x)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\distribute\distributed_training_utils.py", line 356, in validate_per_replica_inputs
validate_all_tensor_shapes(x, x_values)
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\distribute\distributed_training_utils.py", line 373, in validate_all_tensor_shapes
x_shape = x_values[0].shape.as_list()
File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\framework\tensor_shape.py", line 1171, in as_list
raise ValueError("as_list() is not defined on an unknown TensorShape.")
ValueError: as_list() is not defined on an unknown TensorShape.
Process finished with exit code 1
感谢任何帮助。谢谢
如果没有独立的示例进行测试，很难确定，但我认为问题很可能来自使用 tf.py_func，它可能缺少张量的形状信息。不过你在这里不需要它，我认为你可以像这样定义 map_example 和 make_dataset ：
def map_example(height, width, image_raw, label):
image_data = tf.io.decode_raw(image_raw, tf.uint8)
image_data = tf.reshape(image_data, [1, height, width])
return image_data, label
def make_dataset(partition):
files = tf.data.Dataset.list_files("images_" + partition + "*.tfrecord")
dataset = tf.data.TFRecordDataset(files)
# dataset = dataset.shuffle(buffer_size=FLAGS.shuffle_buffer_size)
dataset = dataset.map(decode_example)
dataset = dataset.map(
lambda x: map_example(x['height'], x['width'], x['image_raw'], x['label']))
# dataset = dataset.batch(batch_size=FLAGS.batch_size)
return dataset