我正在尝试在 Conda 环境中的 Jupyter Notebooks 上运行 ResNet152。这是我的代码：从 tensorflow.keras.applications 导入 ResNet50、ResNet101、ResNet152、InceptionResNetV2、DenseNet12...

我正在尝试在 Conda 环境中的 Jupyter Notebook 上运行 ResNet152。这是我的代码：

from tensorflow.keras.applications import ResNet50, ResNet101, ResNet152, InceptionResNetV2, DenseNet121
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Activation, BatchNormalization
from tensorflow.keras import mixed_precision, layers, models
import tensorflow as tf
from tensorflow.keras.optimizers.schedules import ExponentialDecay
from tensorflow.keras.optimizers import Adam, RMSprop

mixed_precision.set_global_policy('mixed_float16')

base_model = ResNet152(weights='imagenet', include_top=False, input_shape=(256, 256, 3))


for layer in base_model.layers:
    layer.trainable = True 

pooling_layer = layers.GlobalAveragePooling2D()(base_model.output)  # GlobalAveragePooling2D layer
output_layer = layers.Dense(1, activation='sigmoid', name='output_layer')(pooling_layer)

model = Model(inputs=base_model.input, outputs=output_layer)

initial_learning_rate = 0.001
lr_schedule = ExponentialDecay(
    initial_learning_rate,
    decay_steps=50,  # Adjust the decay steps as needed
    decay_rate=0.9,     # Adjust the decay rate as needed
    staircase=False)

optimizer = Adam(learning_rate=lr_schedule)

model.compile(
    optimizer=optimizer,
    loss='binary_crossentropy',
    metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), tf.keras.metrics.AUC(name='auc')]
)

model.summary()

os.environ['CUDA_VISIBLE_DEVICES'] = '0'
history = model.fit(dataset_batched_withoutnames, epochs=5)

我收到以下错误：

DNN library is not found.
     [[{{node model/conv1_conv/Conv2D}}]] [Op:__inference_train_function_55822]

（有一个长框，但没有将其全部复制粘贴）

我回去降级了我的 Tensorflow、CUDA 和 cuDNN。我现在使用的是 CUDA 11.2、cuDNN 8.1.0 和 Tensorflow 2.10.0

我回到我的路径并清理了所有内容，并将以下所有内容添加到路径中

这为什么不起作用？