~WhM'dZddlmcmZddlmZddlmZddl m Z ddl m Z ddl mZddl mZdd lmZd Zd Ze Zed d  ddZedddZedddZejdejeje_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.VGG16TimagenetsoftmaxcN |dvs6tjj|st d||dkr|r|dkrt d|dt j|ddtj|| }|t | }n3tj |st || }n|}t d dddd|}t d dddd|}t ddd|}t ddddd|}t ddddd|}t ddd|}t ddddd|}t ddddd|}t ddddd|}t ddd|}t d dddd!|}t d dddd"|}t d dddd#|}t ddd$|}t d dddd%|}t d dddd&|}t d dddd'|}t ddd(|}|rtd)*|}td+dd,-|}td+dd.-|}t j||t||d/-|}nO|d0kr"t|}n'|d1kr!t|}|t'j|} n|} t+j| |d2*} |dkrS|rt/jd3t2d4d56} nt/jd7t4d4d86} | | n|| || 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. Received `classes=` ) default_sizemin_size data_formatrequire_flattenweightsN)shape)tensorr@)rrelusame block1_conv1) activationpaddingname block1_conv2)r! block1_pool)stridesr block2_conv1 block2_conv2 block2_pool block3_conv1 block3_conv2 block3_conv3 block3_pooli block4_conv1 block4_conv2 block4_conv3 block4_pool block5_conv1 block5_conv2 block5_conv3 block5_poolflatten)rifc1)rrfc2 predictionsavgmaxvgg16z+vgg16_weights_tf_dim_ordering_tf_kernels.h5models 64373286793e3c8b2b4e3219cbf3544b) cache_subdir file_hashz1vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5 6d6bbae143d832006294945121d1f1fc)tfiogfileexists ValueErrorrobtain_input_shaperimage_data_formatlayersInputis_keras_tensorConv2D MaxPooling2DFlattenDensevalidate_activationGlobalAveragePooling2DGlobalMaxPooling2Drget_source_inputsrModelrget_file WEIGHTS_PATHWEIGHTS_PATH_NO_TOP load_weights) include_topr input_tensor input_shapepoolingclassesclassifier_activation img_inputxinputsmodel weights_paths ^/var/www/html/movieo_spanner_bot/venv/lib/python3.11/site-packages/keras/applications/vgg16.pyVGG16rd0s R ) ) )RU[-?-?-H-H ) !  ! !   *D ,!( , , ,   !3-//# KLL{L33 &|44 % L LLII$I  FvvN     A   FvvN     A FFGGJJA   V^     A   V^     A FFGGJJA   V^     A   V^     A   V^     A FFGGJJA   V^     A   V^     A   V^     A FFGGJJA   V^     A   V^     A   V^     A FFGGJJA/ NN N * *1 - - LL&uL = =a @ @ LL&uL = =a @ @*+@'JJJ LL  5M      e  --//22AA   ))++A..A.|<< N617 3 3 3E*  %.=%< LL&.C#%< L <((((   7### Lz)keras.applications.vgg16.preprocess_inputc0tj||dS)Ncaffe)rmode)rpreprocess_input)r_rs rcriris#  * {   rez+keras.applications.vgg16.decode_predictionsc.tj||S)N)top)rdecode_predictions)predsrls rcrmrms  ,U < < <rs "!!!!!!!!------!!!!!!++++++""""""######:999998 8    .0JKK   #KKKLKK\9::;: ;<<====<=*>EE 5  3F ,>Fre