改为ResNet

好的，以下是使用ResNet-50的代码示例：

from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import Dense, Flatten, Dropout
from tensorflow.keras.models import Model

def ResNet50_model(dim, rate, optimizer):
    base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
    x = base_model.output
    x = Flatten()(x)
    x = Dense(units=dim/2, kernel_initializer='uniform', activation='relu')(x)
    x = Dropout(rate/2)(x)
    x = Dense(units=dim, kernel_initializer='uniform', activation='relu')(x)
    x = Dropout(rate/2)(x)
    x = Dense(units=dim, kernel_initializer='uniform', activation='relu')(x)
    predictions = Dense(units=7, kernel_initializer='uniform', activation='softmax')(x)
    model = Model(inputs=base_model.input, outputs=predictions)
    for layer in base_model.layers:
        layer.trainable = False
    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
    return model

这个模型使用了Keras中的ResNet50预训练模型作为基础模型，然后在其顶部添加了全连接层。同时，为了避免过拟合，我们也加入了Dropout层。在这个模型中，我们使用了ResNet50的预训练权重，并且将其冻结了，不进行微调，只训练新增的全连接层的权重。