&Vf$lddlmZddlmZddlmZddlmZddlmZddl m Z ddl m Z dZ d Zed d g ddZedddZedddZejdejeje_ejje_dS))backend)layers) keras_export)imagenet_utils) Functional)operation_utils) file_utilsznhttps://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels.h5zthttps://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5zkeras.applications.vgg19.VGG19zkeras.applications.VGG19TimagenetNsoftmaxc |dvs&tj|std||dkr|r|dkrtd|tj|ddt j||}|tj| }n-t j |stj|| }n|}tj d d ddd|}tj d d ddd|}tj ddd|}tj dd ddd|}tj dd ddd|}tj ddd|}tj dd ddd|}tj dd ddd|}tj dd ddd|}tj dd ddd|}tj ddd|}tj d d ddd!|}tj d d ddd"|}tj d d ddd#|}tj d d ddd$|}tj ddd%|}tj d d ddd&|}tj d d ddd'|}tj d d ddd(|}tj d d ddd)|}tj ddd*|}|rtj d+,|}tj d-dd./|}tj d-dd0/|}tj||tj ||d1/|}nE|d2krtj|}n"|d3krtj|}|t#j|} n|} t'| |d4,} |dkrS|rtjd5t*d6d78} ntjd9t,d6d:8} | | n|| || S);a Instantiates the VGG19 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 VGG19, call `keras.applications.vgg19.preprocess_input` on your inputs before passing them to the model. `vgg19.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 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 zcIf using `weights='imagenet'` with `include_top=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_conv4 block3_pooli block4_conv1 block4_conv2 block4_conv3 block4_conv4 block4_pool block5_conv1 block5_conv2 block5_conv3 block5_conv4 block5_poolflatten)rifc1)rrfc2 predictionsavgmaxvgg19z+vgg19_weights_tf_dim_ordering_tf_kernels.h5models cbe5617147190e668d6c5d5026f83318) cache_subdir file_hashz1vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5 253f8cb515780f3b799900260a226db6)r exists ValueErrorrobtain_input_shaperimage_data_formatrInputis_keras_tensorConv2D MaxPooling2DFlattenDensevalidate_activationGlobalAveragePooling2DGlobalMaxPooling2Drget_source_inputsrget_file WEIGHTS_PATHWEIGHTS_PATH_NO_TOP load_weights) include_topr input_tensor input_shapepoolingclassesclassifier_activation img_inputxinputsmodel weights_paths Y/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/applications/vgg19.pyVGG19rasR ) ) )Z->w-G-G ) !  ! !   *D * ' * *   !3-//# KL{333 &|44 % L LLLII$I   FvvN     A   FvvN     A HFFGGGJJA   V^     A   V^     A HFFGGGJJA   V^     A   V^     A   V^     A   V^     A HFFGGGJJA   V^     A   V^     A   V^     A   V^     A HFFGGGJJA   V^     A   V^     A   V^     A   V^     A HFFGGGJJA / *FN * * *1 - - =FL&u = = =a @ @ =FL&u = = =a @ @*+@'JJJ FL  5M      e  /-//22AA   +)++A..A 2<@@ vqw / / /E*  %.=%< LL&.C#%< L <((((   7### Lz)keras.applications.vgg19.preprocess_inputc0tj||dS)Ncaffe)rmode)rpreprocess_input)r\rs r`rfrfs#  * {   rbz+keras.applications.vgg19.decode_predictionsc.tj||S)N)top)rdecode_predictions)predsris r`rjrjs  ,U < < <r{s------111111''''''))))))&&&&&&8 8/1KLMM   #UUUNMUp9::;: ;<<====<=*>EE 5  3F ,>Frb