完全错误：
未知错误：无法获取卷积算法。这可能是 因为cuDNN初始化失败，所以请尝试查看是否有警告 上面打印了日志消息。[OP：Conv2D]
软件包安装命令：
conda install -c anaconda keras-gpu
它已安装：
我已尝试从NVIDIA网站安装CUDA-TOOLKIT，但没有解决问题，因此建议与CONDA命令相关。
一些博客建议安装Visual Studio，但是如果我有Spyder IDE，有什么需要吗？
编码：
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，我收到错误：
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 25,
validation_data = test_set,
validation_steps = 2000)
编辑1：回溯
Traceback (most recent call last):
File "D:Machine LearningMachine Learning A-Z Template FolderPart 8 - Deep LearningSection 40 - Convolutional Neural Networks (CNN)cnn.py", line 70, in <module>
validation_steps = 2000)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasengine raining.py", line 1297, in fit_generator
steps_name='steps_per_epoch')
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasengine raining_generator.py", line 265, in model_iteration
batch_outs = batch_function(*batch_data)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasengine raining.py", line 973, in train_on_batch
class_weight=class_weight, reset_metrics=reset_metrics)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasengine raining_v2_utils.py", line 264, in train_on_batch
output_loss_metrics=model._output_loss_metrics)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasengine raining_eager.py", line 311, in train_on_batch
output_loss_metrics=output_loss_metrics))
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasengine raining_eager.py", line 252, in _process_single_batch
training=training))
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasengine raining_eager.py", line 127, in _model_loss
outs = model(inputs, **kwargs)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasenginease_layer.py", line 891, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasenginesequential.py", line 256, in call
return super(Sequential, self).call(inputs, training=training, mask=mask)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasengine
etwork.py", line 708, in call
convert_kwargs_to_constants=base_layer_utils.call_context().saving)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasengine
etwork.py", line 860, in _run_internal_graph
output_tensors = layer(computed_tensors, **kwargs)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkerasenginease_layer.py", line 891, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonkeraslayersconvolutional.py", line 197, in call
outputs = self._convolution_op(inputs, self.kernel)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonops
n_ops.py", line 1134, in __call__
return self.conv_op(inp, filter)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonops
n_ops.py", line 639, in __call__
return self.call(inp, filter)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonops
n_ops.py", line 238, in __call__
name=self.name)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonops
n_ops.py", line 2010, in conv2d
name=name)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonopsgen_nn_ops.py", line 1031, in conv2d
data_format=data_format, dilations=dilations, name=name, ctx=_ctx)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythonopsgen_nn_ops.py", line 1130, in conv2d_eager_fallback
ctx=_ctx, name=name)
File "C:AnacondaenvsMLlibsite-packages ensorflow_corepythoneagerexecute.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]
以下代码解决了该问题：
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)
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