The ResNet50 transfer learning network

The ResNet50 model for transfer learning can be defined similarly to the VGG16 and InceptionV3 networks, as follows:

def resnet_pseudo(dim=224,freeze_layers=10,full_freeze='N'):
   # model_save_dest = {}
    model = ResNet50(weights='imagenet',include_top=False)
    x = model.output
    x = GlobalAveragePooling2D()(x)
    x = Dense(512, activation='relu')(x)
    x = Dropout(0.5)(x)
    x = Dense(512, activation='relu')(x)
    x = Dropout(0.5)(x)
    out = Dense(5,activation='softmax')(x)
    model_final = Model(input = model.input,outputs=out)
    if full_freeze != 'N':
        for layer in model.layers[0:freeze_layers]:
       layer.trainable = False
    return model_final