o ={c@sdZddlmZddlmZddlmZedd   d!d d Zedd   d!ddZedd   d!ddZedd"ddZ edd#ddZ ej j dej ejde _ej je _dZeed ejeeed ejeeed ejed S)$zResNet v2 models for Keras. Reference: - [Identity Mappings in Deep Residual Networks]( https://arxiv.org/abs/1603.05027) (CVPR 2016) )imagenet_utils)resnet) keras_exportz'keras.applications.resnet_v2.ResNet50V2zkeras.applications.ResNet50V2TimagenetNsoftmaxc C(dd}tj|ddd|||||||d S)z)Instantiates the ResNet50V2 architecture.cSJtj|dddd}tj|dddd}tj|dd d d}tj|d dd d dS)N@conv2nameconv3conv4conv5Zstride1rrZstack2xr.stack_fnTZ resnet50v2classifier_activationrZResNetZ include_topZweightsZ input_tensorZ input_shapeZpoolingZclassesr!rrrr ResNet50V2r$z(keras.applications.resnet_v2.ResNet101V2zkeras.applications.ResNet101V2c Cr)z*Instantiates the ResNet101V2 architecture.cSr )Nr r r r rrrrrrrrrrrrrrrOrzResNet101V2..stack_fnTZ resnet101v2r r"r#rrr ResNet101V2Ar%r'z(keras.applications.resnet_v2.ResNet152V2zkeras.applications.ResNet152V2c Cr)z*Instantiates the ResNet152V2 architecture.cSr )Nr r r r rrr$rrrrrrrrrrrrrzResNet152V2..stack_fnTZ resnet152v2r r"r#rrr ResNet152V2dr%r*z-keras.applications.resnet_v2.preprocess_inputcCstj||ddS)NZtf) data_formatmode)rpreprocess_input)rr+rrrr-sr-z/keras.applications.resnet_v2.decode_predictionscCstj||dS)N)top)rdecode_predictions)Zpredsr/rrrr0sr0)r,reterrora 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 ResNetV2, call `tf.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"`. Returns: A `keras.Model` instance. __doc__)TrNNNrr)N)r.)r4Zkeras.applicationsrrZ tensorflow.python.util.tf_exportrr$r'r*r-r0ZPREPROCESS_INPUT_DOCformatZPREPROCESS_INPUT_RET_DOC_TFZPREPROCESS_INPUT_ERROR_DOCZDOCsetattrrrrrsf           :