a SicM'@sdZddlmmZddlmZddlmZddl m Z ddl m Z ddl mZddl mZdd lmZd Zd Ze Zed d dddZeddddZeddddZejjdejejde_ejje_dS) zVGG16 model for Keras. Reference: - [Very Deep Convolutional Networks for Large-Scale Image Recognition] (https://arxiv.org/abs/1409.1556) (ICLR 2015) N)backend)imagenet_utils)training)VersionAwareLayers) data_utils) layer_utils) keras_exportznhttps://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.VGG16Timagenetsoftmaxc CsD|dvs$tjj|s$td||dkrH|rH|dkrHtd|dtj|ddt||d }|d urxt j |d }nt |st j ||d }n|}t j d ddddd|}t j d ddddd|}t j dddd|}t j dddddd|}t j dddddd|}t j dddd|}t j dddddd|}t j dddddd|}t j dddddd|}t j dddd|}t j d dddd!d|}t j d dddd"d|}t j d dddd#d|}t j ddd$d|}t j d dddd%d|}t j d dddd&d|}t j d dddd'd|}t j ddd(d|}|rt jd)d*|}t jd+dd,d-|}t jd+dd.d-|}t||t j||d/d-|}n.|d0krt |}n|d1krt |}|d urt|} n|} tj| |d2d*} |dkr,|rtjd3td4d5d6} ntjd7td4d8d6} | | n|d ur@| || S)9a 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 `tf.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"`. Returns: A `keras.Model` instance. >Nr zThe `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 r zlIf using `weights` as `"imagenet"` with `include_top` as true, `classes` should be 1000. 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