完全错误：
UnknownError：无法获得卷积算法。这可能是由于cuDNN无法初始化，因此请尝试查看是否有警告日志消息已打印在上方。 [Op：Conv2D]
用于软件包安装的命令：
conda install -c anaconda keras-gpu
已安装：
我曾尝试从nvidia网站安装cuda-toolkit，但它没有解决问题，所以建议与conda命令相关。
一些博客建议安装Visual Studio，但是如果我有spyder IDE，那有什么需要？
代码：
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Convolution2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
classifier = Sequential()
classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('dataset/training_set',
target_size = (64, 64),
batch_size = 4,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('dataset/test_set',
target_size = (64, 64),
batch_size = 4,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 25,
validation_data = test_set,
validation_steps = 2000)
执行下面的代码后，我得到了错误：
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 25,
validation_data = test_set,
validation_steps = 2000)
edit 1：追溯
Traceback (most recent call last):
File "D:\Machine Learning\Machine Learning A-Z Template Folder\Part 8 - Deep Learning\Section 40 - Convolutional Neural Networks (CNN)\cnn.py", line 70, in <module>
validation_steps = 2000)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 1297, in fit_generator
steps_name='steps_per_epoch')
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training_generator.py", line 265, in model_iteration
batch_outs = batch_function(*batch_data)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 973, in train_on_batch
class_weight=class_weight, reset_metrics=reset_metrics)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 264, in train_on_batch
output_loss_metrics=model._output_loss_metrics)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 311, in train_on_batch
output_loss_metrics=output_loss_metrics))
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 252, in _process_single_batch
training=training))
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 127, in _model_loss
outs = model(inputs, **kwargs)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 891, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py", line 256, in call
return super(Sequential, self).call(inputs, training=training, mask=mask)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 708, in call
convert_kwargs_to_constants=base_layer_utils.call_context().saving)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 860, in _run_internal_graph
output_tensors = layer(computed_tensors, **kwargs)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 891, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\layers\convolutional.py", line 197, in call
outputs = self._convolution_op(inputs, self.kernel)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 1134, in __call__
return self.conv_op(inp, filter)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 639, in __call__
return self.call(inp, filter)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 238, in __call__
name=self.name)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 2010, in conv2d
name=name)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py", line 1031, in conv2d
data_format=data_format, dilations=dilations, name=name, ctx=_ctx)
File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py", line 1130, in conv2d_eager_fallback
ctx=_ctx, name=name)
File "C:\Anaconda\envs\ML\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
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. [Op:Conv2D]
错误是由于以下事实之间的不兼容：
在下面的答案中，我提供了张量流，cuda和cudnn的有效组合。请查看与您类似的问题：Tensorflow 2.0 can't use GPU, something wrong in cuDNN? :Failed to get convolution algorithm. This is probably because cuDNN failed to initialize
例如Cuda 10.0 + CuDNN 7.6.3 + / TensorFlow 1.13 / 1.14 / TensorFlow 2.0。
Eg2 Cuda 9 + CuDNN 7.0.5 + TensorFlow 1.10作品
我最近在jupyter中遇到的相同错误：该错误是由于Cuda软件和tensorflow-gpu正确安装或安装造成的]
我做什么？
请在youtube how we install tensorflow-gpu on window上观看此视频
下面的代码解决了这个问题：
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
except RuntimeError as e:
print(e)