我几个月前安装了tensorflow 2.0。我成功地运行了细胞神经网络、线性回归和其他keras模型。我最近从tensorflow 2.0 RNN学习了RNN和keras教程。我运行了教程中的以下代码：
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)
~AppDataRoamingPythonPython35site-packagestensorflow_corepythonkerasenginetraining.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,
~AppDataRoamingPythonPython35site-packagestensorflow_corepythonkerasenginetraining_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
~AppDataRoamingPythonPython35site-packagestensorflow_corepythonkerasenginetraining_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?
~AppDataRoamingPythonPython35site-packagestensorflow_corepythonkerasenginetraining_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
~AppDataRoamingPythonPython35site-packagestensorflow_corepythoneagerdef_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()
~AppDataRoamingPythonPython35site-packagestensorflow_corepythoneagerdef_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 =
~AppDataRoamingPythonPython35site-packagestensorflow_corepythoneagerfunction.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
~AppDataRoamingPythonPython35site-packagestensorflow_corepythoneagerfunction.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):
~AppDataRoamingPythonPython35site-packagestensorflow_corepythoneagerfunction.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()
~AppDataRoamingPythonPython35site-packagestensorflow_corepythoneagerfunction.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(
~AppDataRoamingPythonPython35site-packagestensorflow_corepythoneagerexecute.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:usersgokul adethyaappdatalocalprogramspythonpython35libsite-packagessix.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
我研究了这个错误，发现了这个。我试着将growth设置为true，但我实际上并不了解它是如何工作的，但我仍然通过插入https://www.tensorflow.org/guide/gpu，这仍然导致了相同的错误。
配置：
tensorflow gpu版本--2.0.0CUDA版本-v10.0
我终于发现了问题。问题是我的Cudnn低于tensorflow建议的版本，该版本>=7.4.1。当我将ot升级到最新发布的版本时，它被修复了