o 2h%@sddlmZddlmZddlmZddlmZddlmZddl m Z ddl m Z dZ d Zed d g      dddZeddddZeddddZejjdejejde_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.VGG19TimagenetNsoftmaxvgg19c Cs|dvst|std||dkr!|r!|dkr!td|tj|ddt||d}|d ur9tj|d }nt |sFtj||d }n|}tj d d dddd|} tj d d dddd| } tj dddd| } tj dd dddd| } tj dd dddd| } tj dddd| } tj dd dddd| } tj dd dddd| } tj dd dddd| } tj dd dddd| } tj dddd| } tj d d ddd!d| } tj d d ddd"d| } tj d d ddd#d| } tj d d ddd$d| } tj ddd%d| } tj d d ddd&d| } tj d d ddd'd| } tj d d ddd(d| } tj d d ddd)d| } tj ddd*d| } |rjtj d+d,| } tj d-dd.d/| } tj d-dd0d/| } t||tj ||d1d/| } n|d2krvt| } n |d3krt| } |d urt|} n|} t| | |d,} |dkr|rtjd4td5d6d7} n tjd8td5d9d7} | | | S|d ur| || 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"`. name: The name of the model (string). 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 predictionsavgmaxz+vgg19_weights_tf_dim_ordering_tf_kernels.h5models cbe5617147190e668d6c5d5026f83318) cache_subdir file_hashz1vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5 253f8cb515780f3b799900260a226db6)rexists 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_activationr img_inputxinputsmodel weights_pathr^W/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/applications/vgg19.pyVGG19s&K                           r`z)keras.applications.vgg19.preprocess_inputcCstj||ddS)Ncaffe)rmode)rpreprocess_input)rZrr^r^r_rcsrcz+keras.applications.vgg19.decode_predictionscCstj||dS)N)top)rdecode_predictions)predsrer^r^r_rfsrf)rbreterror)Tr NNNr r r )N)rd) keras.srcrrkeras.src.api_exportrkeras.src.applicationsrkeras.src.modelsr keras.src.opsrkeras.src.utilsrrPrQr`rcrfPREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_CAFFEPREPROCESS_INPUT_ERROR_DOC__doc__r^r^r^r_s@        [