o 2hc@sddlmZddlmZddlmZeddg      d#d d Zeddg      d$ddZeddg      d%ddZedd&ddZedd'ddZ ej j dej ej d e_ej je _d!Zeed"ejeeed"ejeeed"ejedS)() keras_export)imagenet_utils)resnetzkeras.applications.ResNet50V2z'keras.applications.resnet_v2.ResNet50V2TimagenetNsoftmax resnet50v2c C*dd}tj|dd|d|||||||d S)z)Instantiates the ResNet50V2 architecture.cSJtj|dddd}tj|dddd}tj|dd d d}tj|d dd d dS)N@conv2nameconv3conv4conv5stride1rrstack_residual_blocks_v2xr[/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/applications/resnet_v2.pystack_fn  zResNet50V2..stack_fnTr r weights_name include_topweights input_tensor input_shapepoolingclassesclassifier_activationrResNet r%r&r'r(r)r*r+rr!rrr ResNet50V2r/zkeras.applications.ResNet101V2z(keras.applications.resnet_v2.ResNet101V2 resnet101v2c Cr )z*Instantiates the ResNet101V2 architecture.cSr )Nr r r rrrrrrrrrrrrrrr r!Br"zResNet101V2..stack_fnTr1r#r,r.rrr ResNet101V20r0r3zkeras.applications.ResNet152V2z(keras.applications.resnet_v2.ResNet152V2 resnet152v2c Cr )z*Instantiates the ResNet152V2 architecture.cSr )Nr r r rrrr$rrrrrrrrrr r!lr"zResNet152V2..stack_fnTr4r#r,r.rrr ResNet152V2Zr0r7z-keras.applications.resnet_v2.preprocess_inputcCstj||ddS)Ntf) data_formatmode)rpreprocess_input)rr9rrr r;sr;z/keras.applications.resnet_v2.decode_predictionscCstj||dS)N)top)rdecode_predictions)predsr=rrr r>sr>)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 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. __doc__)TrNNNrrr)TrNNNrrr1)TrNNNrrr4)N)r<)keras.src.api_exportrkeras.src.applicationsrrr/r3r7r;r>PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOCrCDOCsetattrrrrr sv   $ $ $   6