请使用python将dropout层应用到resnet50里面

以下是使用Python将dropout层应用到ResNet50中的代码示例：

import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.layers import Dropout, Dense
from tensorflow.keras.models import Model

# Load ResNet50 model without top layer
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Add dropout layer
x = base_model.output
x = Dropout(0.5)(x)

# Add fully connected layer
x = Dense(1024, activation='relu')(x)

# Add output layer
predictions = Dense(10, activation='softmax')(x)

# Create model
model = Model(inputs=base_model.input, outputs=predictions)

# Freeze base layers
for layer in base_model.layers:
    layer.trainable = False

# Compile model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train model
model.fit(train_images, train_labels, epochs=10, batch_size=32, validation_data=(val_images, val_labels))

在这个示例中，我们首先加载了ResNet50模型，并添加了一个dropout层和一个全连接层。然后，我们定义了输出层，并使用Model()函数创建了一个新的模型。我们还将ResNet50的基本层冻结，以防止它们在训练过程中被更新。最后，我们编译了模型并训练了它。