运行 使用 tensorflow 2.0 的 RNN LSTM 模型时出现错误
I'm getting an error while running the RNN LSTM model with tensorflow 2.0
几个月前我安装了tensorflow 2.0。我成功地 运行 CNN、线性回归和其他 keras 模型。我最近在 tensorflow 2.0 RNN with keras tutorials 学习 RNN。我 运行 本教程中的以下代码：
import collections
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
batch_size = 64
input_dim = 28
units = 64
output_size = 10
def build_model(allow_cudnn_kernel=True):
if allow_cudnn_kernel:
lstm_layer = tf.keras.layers.LSTM(units, input_shape=(None, input_dim))
else:
lstm_layer = tf.keras.layers.RNN(
tf.keras.layers.LSTMCell(units),
input_shape=(None, input_dim))
model = tf.keras.models.Sequential([
lstm_layer,
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Dense(output_size, activation='softmax')]
)
return model
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
sample, sample_label = x_train[0], y_train[0]
model = build_model(allow_cudnn_kernel=True)
model.compile(loss='sparse_categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
model.fit(x_train, y_train,
validation_data=(x_test, y_test),
batch_size=batch_size,
epochs=5)
这是我得到的输出：
Train on 60000 samples, validate on 10000 samples
Epoch 1/5
64/60000 [..............................] - ETA: 1:17:13
---------------------------------------------------------------------------
UnknownError Traceback (most recent call last)
<ipython-input-8-6a1ac7233ae1> in <module>()
31 validation_data=(x_test, y_test),
32 batch_size=batch_size,
---> 33 epochs=5)
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
726 max_queue_size=max_queue_size,
727 workers=workers,
--> 728 use_multiprocessing=use_multiprocessing)
729
730 def evaluate(self,
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
322 mode=ModeKeys.TRAIN,
323 training_context=training_context,
--> 324 total_epochs=epochs)
325 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
326
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
121 step=step, mode=mode, size=current_batch_size) as batch_logs:
122 try:
--> 123 batch_outs = execution_function(iterator)
124 except (StopIteration, errors.OutOfRangeError):
125 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
84 # `numpy` translates Tensors to values in Eager mode.
85 return nest.map_structure(_non_none_constant_value,
---> 86 distributed_function(input_fn))
87
88 return execution_function
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
518 # Lifting succeeded, so variables are initialized and we can run the
519 # stateless function.
--> 520 return self._stateless_fn(*args, **kwds)
521 else:
522 canon_args, canon_kwds = \
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\function.py in __call__(self, *args, **kwargs)
1821 """Calls a graph function specialized to the inputs."""
1822 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 1823 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
1824
1825 @property
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
1139 if isinstance(t, (ops.Tensor,
1140 resource_variable_ops.BaseResourceVariable))),
-> 1141 self.captured_inputs)
1142
1143 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1222 if executing_eagerly:
1223 flat_outputs = forward_function.call(
-> 1224 ctx, args, cancellation_manager=cancellation_manager)
1225 else:
1226 gradient_name = self._delayed_rewrite_functions.register()
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
509 inputs=args,
510 attrs=("executor_type", executor_type, "config_proto", config),
--> 511 ctx=ctx)
512 else:
513 outputs = execute.execute_with_cancellation(
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
c:\users\gokul adethya\appdata\local\programs\python\python35\lib\site-packages\six.py in raise_from(value, from_value)
UnknownError: [_Derived_] Fail to find the dnn implementation.
[[{{node CudnnRNN}}]]
[[sequential_1/lstm_1/StatefulPartitionedCall]] [Op:__inference_distributed_function_6815]
Function call stack:
distributed_function -> distributed_function -> distributed_function
我研究了错误并发现 this. I tried setting growth to true, which I didn't actually understand how it works, but I still tried it by inserting code from https://www.tensorflow.org/guide/gpu，它仍然导致相同的错误。
配置：
tensorflow-gpu 版本 -- 2.0.0
CUDA 版本 - v10.0
我终于找到了 problem.The 问题是我的 Cudnn 低于 tensorflow 推荐的版本 >=7.4.1 。当我将 ot 升级到最新发布的版本时，它已修复
几个月前我安装了tensorflow 2.0。我成功地 运行 CNN、线性回归和其他 keras 模型。我最近在 tensorflow 2.0 RNN with keras tutorials 学习 RNN。我 运行 本教程中的以下代码：
import collections
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
batch_size = 64
input_dim = 28
units = 64
output_size = 10
def build_model(allow_cudnn_kernel=True):
if allow_cudnn_kernel:
lstm_layer = tf.keras.layers.LSTM(units, input_shape=(None, input_dim))
else:
lstm_layer = tf.keras.layers.RNN(
tf.keras.layers.LSTMCell(units),
input_shape=(None, input_dim))
model = tf.keras.models.Sequential([
lstm_layer,
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Dense(output_size, activation='softmax')]
)
return model
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
sample, sample_label = x_train[0], y_train[0]
model = build_model(allow_cudnn_kernel=True)
model.compile(loss='sparse_categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
model.fit(x_train, y_train,
validation_data=(x_test, y_test),
batch_size=batch_size,
epochs=5)
这是我得到的输出：
Train on 60000 samples, validate on 10000 samples
Epoch 1/5
64/60000 [..............................] - ETA: 1:17:13
---------------------------------------------------------------------------
UnknownError Traceback (most recent call last)
<ipython-input-8-6a1ac7233ae1> in <module>()
31 validation_data=(x_test, y_test),
32 batch_size=batch_size,
---> 33 epochs=5)
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
726 max_queue_size=max_queue_size,
727 workers=workers,
--> 728 use_multiprocessing=use_multiprocessing)
729
730 def evaluate(self,
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
322 mode=ModeKeys.TRAIN,
323 training_context=training_context,
--> 324 total_epochs=epochs)
325 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
326
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
121 step=step, mode=mode, size=current_batch_size) as batch_logs:
122 try:
--> 123 batch_outs = execution_function(iterator)
124 except (StopIteration, errors.OutOfRangeError):
125 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
84 # `numpy` translates Tensors to values in Eager mode.
85 return nest.map_structure(_non_none_constant_value,
---> 86 distributed_function(input_fn))
87
88 return execution_function
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
518 # Lifting succeeded, so variables are initialized and we can run the
519 # stateless function.
--> 520 return self._stateless_fn(*args, **kwds)
521 else:
522 canon_args, canon_kwds = \
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\function.py in __call__(self, *args, **kwargs)
1821 """Calls a graph function specialized to the inputs."""
1822 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 1823 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
1824
1825 @property
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
1139 if isinstance(t, (ops.Tensor,
1140 resource_variable_ops.BaseResourceVariable))),
-> 1141 self.captured_inputs)
1142
1143 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1222 if executing_eagerly:
1223 flat_outputs = forward_function.call(
-> 1224 ctx, args, cancellation_manager=cancellation_manager)
1225 else:
1226 gradient_name = self._delayed_rewrite_functions.register()
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
509 inputs=args,
510 attrs=("executor_type", executor_type, "config_proto", config),
--> 511 ctx=ctx)
512 else:
513 outputs = execute.execute_with_cancellation(
~\AppData\Roaming\Python\Python35\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
c:\users\gokul adethya\appdata\local\programs\python\python35\lib\site-packages\six.py in raise_from(value, from_value)
UnknownError: [_Derived_] Fail to find the dnn implementation.
[[{{node CudnnRNN}}]]
[[sequential_1/lstm_1/StatefulPartitionedCall]] [Op:__inference_distributed_function_6815]
Function call stack:
distributed_function -> distributed_function -> distributed_function
我研究了错误并发现 this. I tried setting growth to true, which I didn't actually understand how it works, but I still tried it by inserting code from https://www.tensorflow.org/guide/gpu，它仍然导致相同的错误。
配置：
tensorflow-gpu 版本 -- 2.0.0 CUDA 版本 - v10.0
我终于找到了 problem.The 问题是我的 Cudnn 低于 tensorflow 推荐的版本 >=7.4.1 。当我将 ot 升级到最新发布的版本时，它已修复