问题
The relatively new keras-tuner module for tensorflow-2 is causing the error 'Failed to create a NewWriteableFile'. The tuner.search function is working, it is only after the trial completes that the error is thrown. This is a tutorial from the sentdex Youtube channel.
Here is the code:
from tensorflow import keras
from tensorflow.keras.datasets import fashion_mnist
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Activation, Flatten
from kerastuner.tuners import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters
import matplotlib.pyplot as plt
import time
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
x_train = x_train[:1000].reshape(-1, 28, 28, 1)
x_test = x_test[:100].reshape(-1, 28, 28, 1)
y_train = y_train[:1000]
y_test = y_test[:100]
# x_train = x_train.reshape(-1, 28, 28, 1)
# x_test = x_test.reshape(-1, 28, 28, 1)
LOG_DIR = f"{int(time.time())}"
def build_model(hp):
model = keras.models.Sequential()
model.add(Conv2D(hp.Int("layer1_channels", min_value=32,
max_value=256, step=32), (3,3), input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
for i in range(hp.Int("n_layers", 1, 4)):
model.add(Conv2D(hp.Int(f"conv_{i}_channels", min_value=32,
max_value=256, step=32), (3,3)))
model.add(Flatten())
model.add(Dense(10))
model.add(Activation('softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
return model
tuner = RandomSearch(build_model,
objective = "val_accuracy",
max_trials = 1,
executions_per_trial = 1,
directory = LOG_DIR,
project_name = 'junk')
tuner.search(x_train,
y_train,
epochs=1,
batch_size=64,
validation_data=(x_test, y_test))
This is the traceback printout:
(tf_2.0) C:\Users\redex\OneDrive\Documents\Education\Sentdex Tutorials\Keras-Tuner>C:/Users/redex/Anaconda3/envs/tf_2.0/python.exe "c:/Users/redex/OneDrive/Documents/Education/Sentdex Tutorials/Keras-Tuner/keras-tuner.py"
2019-12-21 10:07:47.556531: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: AVX AVX2
To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.
2019-12-21 10:07:47.574699: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 8. Tune using inter_op_parallelism_threads for best performance.
Train on 1000 samples, validate on 100 samples
960/1000 [===========================>..] - ETA: 0s - loss: 64.0616 - accuracy: 0.2844
2019-12-21 10:07:55.080024: W tensorflow/core/framework/op_kernel.cc:1622] OP_REQUIRES failed at save_restore_v2_ops.cc:109 : Not found: Failed to create a NewWriteableFile: 1576951667\junk\trial_c5a5436b1d28a85446ce55c8d13f9657\checkpoints\epoch_0\checkpoint_temp_8a230a5ae2d046098456d1fdfc696690/part-00000-of-00001.data-00000-of-00001.tempstate15377864750281844169 : The system cannot find the path specified.
; No such process
Traceback (most recent call last):
File "c:/Users/redex/OneDrive/Documents/Education/Sentdex Tutorials/Keras-Tuner/keras-tuner.py", line 65, in <module>
validation_data=(x_test, y_test))
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\kerastuner\engine\base_tuner.py", line 122, in search
self.run_trial(trial, *fit_args, **fit_kwargs)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\kerastuner\engine\multi_execution_tuner.py", line 95, in run_trial
history = model.fit(*fit_args, **fit_kwargs, callbacks=callbacks)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 728, in fit
use_multiprocessing=use_multiprocessing)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 372, in fit
prefix='val_')
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\contextlib.py", line 119, in __exit__
next(self.gen)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 685, in on_epoch
self.callbacks.on_epoch_end(epoch, epoch_logs)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\keras\callbacks.py", line 298, in on_epoch_end
callback.on_epoch_end(epoch, logs)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\keras\callbacks.py", line 965, in on_epoch_end
self._save_model(epoch=epoch, logs=logs)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\keras\callbacks.py", line 999, in _save_model
self.model.save_weights(filepath, overwrite=True)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 1090, in save_weights
self._trackable_saver.save(filepath, session=session)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\training\tracking\util.py", line 1155, in save
file_prefix=file_prefix_tensor, object_graph_tensor=object_graph_tensor)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\training\tracking\util.py", line 1103, in _save_cached_when_graph_building
save_op = saver.save(file_prefix)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\training\saving\functional_saver.py", line 230, in save
sharded_saves.append(saver.save(shard_prefix))
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\training\saving\functional_saver.py", line 72, in save
return io_ops.save_v2(file_prefix, tensor_names, tensor_slices, tensors)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\ops\gen_io_ops.py", line 1932, in save_v2
ctx=_ctx)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\ops\gen_io_ops.py", line 1969, in save_v2_eager_fallback
ctx=_ctx, name=name)
File "C:\Users\redex\Anaconda3\envs\tf_2.0\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.NotFoundError: Failed to create a NewWriteableFile: 1576951667\junk\trial_c5a5436b1d28a85446ce55c8d13f9657\checkpoints\epoch_0\checkpoint_temp_8a230a5ae2d046098456d1fdfc696690/part-00000-of-00001.data-00000-of-00001.tempstate15377864750281844169 : The system cannot find the path specified.
; No such process [Op:SaveV2]
My machine is Windows 10 The keras-tuner documentation specifies Tensorflow 2.0 and Python 3.6 but I'm using 3.7.4. I presume more recent is OK. I'm no software expert so this is about all I know, any help is appreciated.
回答1:
In my case, the path exceeds the maximum length of path in windows because the length of generated path by Keras Turner is about 170. After I make my folder shorter, it works normally.
回答2:
I had the similas problem while using kerastuner in Windows and I've solved it:
The first issue is that the path to the log directory may be too long. I had to reduced it.
The second problem is that python (or tf) doens't work in Windows with mixed slashes. But kerastuner forms the path with backslashes. So I should provide the path with backslashes. I've done this with os.path.normpath() method:
tuner=RandomSearch(build_model,objective='val_accuracy',max_trials=10,directory=os.path.normpath('C:/'))
tuner.search(x_train,y_train,batch_size=256,epochs=30,validation_split=0.2,verbose=1)
Now I don't receive this error.
回答3:
The problem it would appear is a Windows issue. Running the same code in a Linux environment had no issue in this regard.
来源：https://stackoverflow.com/questions/59439124/keras-tuner-search-function-throws-failed-to-create-a-newwriteablefile-error