2Vh#rddlmZddlmZddlmZddlmZddlmZddl m Z ddl m Z dZ d Zed d g dd ZedddZedddZej$j'dej(ej*e_ej"j,e_y ))backend)layers) keras_export)imagenet_utils) Functional)operation_utils) file_utilsznhttps://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5zthttps://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5zkeras.applications.vgg16.VGG16zkeras.applications.VGG16Nc|dvs#tj|std||dk(r|r|dk7rtd|tj|ddt j ||}|tj| }n/t j|stj|| }n|}tjd d d dd|} tjd d d dd| } tjddd| } tjdd d dd| } tjdd d dd| } tjddd| } tjdd d dd| } tjdd d dd| } tjdd d dd| } tjddd| } tjdd d dd| } tjdd d dd | } tjdd d dd!| } tjddd"| } tjdd d dd#| } tjdd d dd$| } tjdd d dd%| } tjddd&| } |rtjd'(| } tjd)d d*+| } tjd)d d,+| } tj||tj||d-+| } n?|d.k(rtj| } n|d/k(rtj | } |t#j$|} n|} t'| | |(} |dk(rP|rtj(d0t*d1d23} ntj(d4t,d1d53} | j/| | S|| j/|| S)6a Instantiates the VGG16 model. Reference: - [Very Deep Convolutional Networks for Large-Scale Image Recognition]( https://arxiv.org/abs/1409.1556) (ICLR 2015) 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/). The default input size for this model is 224x224. Note: each Keras Application expects a specific kind of input preprocessing. For VGG16, call `keras.applications.vgg16.preprocess_input` on your inputs before passing them to the model. `vgg16.preprocess_input` will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling. Args: include_top: whether to include the 3 fully-connected layers 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 input 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. >NimagenetzThe `weights` argument should be either `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. Received: weights=r zcIf using `weights='imagenet'` with `include_top=True`, `classes` should be 1000. 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