我正在尝试制作一个卷积神经网络来分析微软的猫和狗数据集。我使用的是tensorflow-gpu 1.12.0，jupyter笔记本和Windows 10上的anaconda。我的GPU是GTX 1080。我安装了CUDA和cuDNN，并且我相当确定我已经正确设置了它们。我已经检查了版本。以下是我的代码（我在jupyter中将其放在不同的单元格中）。
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
import pickle
import sys
print(sys.executable)
print(tf.__version__)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = 0.4)
session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
print('GPU Settings set')
X = pickle.load(open('X.pickle','rb')) # Brings in the "pictures" of the training set
y = pickle.load(open('y.pickle','rb')) # Brings in the answers
X = X/255.0 # Normalizes the model so each number is between 0 and 1
print('Data Loaded')
model = Sequential()
model.add(Conv2D(64, (3,3), input_shape = X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss="binary_crossentropy", optimizer='adam', metrics = ['accuracy'])
model.fit(X, y, batch_size=25, epochs=3, validation_split=0.1)
我得到了这个错误：
Train on 22451 samples, validate on 2495 samples
Epoch 1/3
---------------------------------------------------------------------------
UnknownError Traceback (most recent call last)
<ipython-input-6-9cef6147c3c5> in <module>
17 model.compile(loss="binary_crossentropy", optimizer='adam', metrics = ['accuracy'])
18
---> 19 model.fit(X, y, batch_size=25, epochs=3, validation_split=0.1)
~\Anaconda3\envs\learning\lib\site-packages\tensorflow\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, max_queue_size, workers, use_multiprocessing, **kwargs)
1637 initial_epoch=initial_epoch,
1638 steps_per_epoch=steps_per_epoch,
-> 1639 validation_steps=validation_steps)
1640
1641 def evaluate(self,
~\Anaconda3\envs\learning\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py in fit_loop(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps)
213 ins_batch[i] = ins_batch[i].toarray()
214
--> 215 outs = f(ins_batch)
216 if not isinstance(outs, list):
217 outs = [outs]
~\Anaconda3\envs\learning\lib\site-packages\tensorflow\python\keras\backend.py in __call__(self, inputs)
2984
2985 fetched = self._callable_fn(*array_vals,
-> 2986 run_metadata=self.run_metadata)
2987 self._call_fetch_callbacks(fetched[-len(self._fetches):])
2988 return fetched[:len(self.outputs)]
~\Anaconda3\envs\learning\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args, **kwargs)
1437 ret = tf_session.TF_SessionRunCallable(
1438 self._session._session, self._handle, args, status,
-> 1439 run_metadata_ptr)
1440 if run_metadata:
1441 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~\Anaconda3\envs\learning\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
526 None, None,
527 compat.as_text(c_api.TF_Message(self.status.status)),
--> 528 c_api.TF_GetCode(self.status.status))
529 # Delete the underlying status object from memory otherwise it stays alive
530 # as there is a reference to status from this from the traceback due to
UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node conv2d_3/Conv2D}} = Conv2D[T=DT_FLOAT, _class=["loc:@training_2/Adam/gradients/conv2d_3/Conv2D_grad/Conv2DBackpropFilter"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](training_2/Adam/gradients/conv2d_3/Conv2D_grad/Conv2DBackpropFilter-0-TransposeNHWCToNCHW-LayoutOptimizer, conv2d_3/Conv2D/ReadVariableOp)]]
[[{{node loss_2/activation_7_loss/broadcast_weights/assert_broadcastable/AssertGuard/Assert/Switch/_329}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_321_l...ert/Switch", tensor_type=DT_BOOL, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
from tensorflow.keras.layers import LSTM，但您是否尝试在不使用per_process_gpu_memory_fraction的情况下运行它？注释掉第8-10行代码并测试它是否能正常运行，虽然速度可能会慢些。 - Suleiman