from keras.src.api_export import keras_export from keras.src.applications import imagenet_utils from keras.src.applications import resnet @keras_export( [ "keras.applications.ResNet50V2", "keras.applications.resnet_v2.ResNet50V2", ] ) def ResNet50V2( include_top=True, weights="imagenet", input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation="softmax", name="resnet50v2", ): """Instantiates the ResNet50V2 architecture.""" def stack_fn(x): x = resnet.stack_residual_blocks_v2(x, 64, 3, name="conv2") x = resnet.stack_residual_blocks_v2(x, 128, 4, name="conv3") x = resnet.stack_residual_blocks_v2(x, 256, 6, name="conv4") return resnet.stack_residual_blocks_v2( x, 512, 3, stride1=1, name="conv5" ) return resnet.ResNet( stack_fn, True, True, name=name, weights_name="resnet50v2", include_top=include_top, weights=weights, input_tensor=input_tensor, input_shape=input_shape, pooling=pooling, classes=classes, classifier_activation=classifier_activation, ) @keras_export( [ "keras.applications.ResNet101V2", "keras.applications.resnet_v2.ResNet101V2", ] ) def ResNet101V2( include_top=True, weights="imagenet", input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation="softmax", name="resnet101v2", ): """Instantiates the ResNet101V2 architecture.""" def stack_fn(x): x = resnet.stack_residual_blocks_v2(x, 64, 3, name="conv2") x = resnet.stack_residual_blocks_v2(x, 128, 4, name="conv3") x = resnet.stack_residual_blocks_v2(x, 256, 23, name="conv4") return resnet.stack_residual_blocks_v2( x, 512, 3, stride1=1, name="conv5" ) return resnet.ResNet( stack_fn, True, True, name=name, weights_name="resnet101v2", include_top=include_top, weights=weights, input_tensor=input_tensor, input_shape=input_shape, pooling=pooling, classes=classes, classifier_activation=classifier_activation, ) @keras_export( [ "keras.applications.ResNet152V2", "keras.applications.resnet_v2.ResNet152V2", ] ) def ResNet152V2( include_top=True, weights="imagenet", input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation="softmax", name="resnet152v2", ): """Instantiates the ResNet152V2 architecture.""" def stack_fn(x): x = resnet.stack_residual_blocks_v2(x, 64, 3, name="conv2") x = resnet.stack_residual_blocks_v2(x, 128, 8, name="conv3") x = resnet.stack_residual_blocks_v2(x, 256, 36, name="conv4") return resnet.stack_residual_blocks_v2( x, 512, 3, stride1=1, name="conv5" ) return resnet.ResNet( stack_fn, True, True, name=name, weights_name="resnet152v2", include_top=include_top, weights=weights, input_tensor=input_tensor, input_shape=input_shape, pooling=pooling, classes=classes, classifier_activation=classifier_activation, ) @keras_export("keras.applications.resnet_v2.preprocess_input") def preprocess_input(x, data_format=None): return imagenet_utils.preprocess_input( x, data_format=data_format, mode="tf" ) @keras_export("keras.applications.resnet_v2.decode_predictions") def decode_predictions(preds, top=5): return imagenet_utils.decode_predictions(preds, top=top) preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format( mode="", ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF, error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC, ) decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__ DOC = """ Reference: - [Identity Mappings in Deep Residual Networks]( https://arxiv.org/abs/1603.05027) (CVPR 2016) For image classification use cases, see [this page for detailed examples]( https://keras.io/api/applications/#usage-examples-for-image-classification-models). For transfer learning use cases, make sure to read the [guide to transfer learning & fine-tuning]( https://keras.io/guides/transfer_learning/). Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call `keras.applications.resnet_v2.preprocess_input` on your inputs before passing them to the model. `resnet_v2.preprocess_input` will scale input pixels between -1 and 1. Args: include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), `"imagenet"` (pre-training on ImageNet), or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is `False` (otherwise the input shape has to be `(224, 224, 3)` (with `"channels_last"` data format) or `(3, 224, 224)` (with `"channels_first"` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(200, 200, 3)` would be one valid value. pooling: Optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional block. - `avg` means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if `include_top` is `True`, and if no `weights` argument is specified. classifier_activation: A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`. name: The name of the model (string). Returns: A Model instance. """ setattr(ResNet50V2, "__doc__", ResNet50V2.__doc__ + DOC) setattr(ResNet101V2, "__doc__", ResNet101V2.__doc__ + DOC) setattr(ResNet152V2, "__doc__", ResNet152V2.__doc__ + DOC)