6bi'SrSSKJs Jr SSKJr SSKJr SSK J r SSK J r SSK Jr SSK Jr SS KJr S rS r\ "5r\"S S 5SSj5r\"S5SSj5r\"S5SSj5r\R0R3S\R4\R6S9\l\R.R\lg)zVGG16 model for TF-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.VGG16c ~US;d<[RRRU5(d[ SU35eUS:XaU(aUS:wa[ SUS35e[ R "USS[R"5UUS 9nUc[RUS 9nO1[R"U5(d[RX#S 9nOUn[RS S SSSS9"U5n[RS S SSSS9"U5n[RSSSS9"U5n[RSS SSSS9"U5n[RSS SSSS9"U5n[RSSSS9"U5n[RSS SSSS9"U5n[RSS SSSS9"U5n[RSS SSSS9"U5n[RSSSS9"U5n[RSS SSS S9"U5n[RSS SSS!S9"U5n[RSS SSS"S9"U5n[RSSS#S9"U5n[RSS SSS$S9"U5n[RSS SSS%S9"U5n[RSS SSS&S9"U5n[RSSS'S9"U5nU(a[RS(S)9"U5n[RS*SS+S,9"U5n[RS*SS-S,9"U5n[ R "Xa5 [RXVS.S,9"U5nOAUS/:Xa[R#5"U5nO US0:Xa[R%5"U5nUb[&R("U5n OUn [*R,"XS1S)9n US:XaQU(a[.R0"S2[2S3S4S59n O[.R0"S6[4S3S7S59n U R7U 5 U $UbU R7U5 U $)8a 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 TF-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 TF-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. >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 zlIf using `weights` as `"imagenet"` with `include_top` as true, `classes` should be 1000. Received `classes=` ) default_sizemin_size data_formatrequire_flattenweights)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 Y/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/applications/vgg16.pyVGG16rc0sR ) )RUU[[-?-?-H-H i  !  *D !!(  ,  !33--/# KLL{L3 &&|44 L LI$I  FvvN   A   FvvN   A FFGJA   V^   A   V^   A FFGJA   V^   A   V^   A   V^   A FFGJA   V^   A   V^   A   V^   A FFGJA   V^   A   V^   A   V^   A FFGJA NN N *1 - LL&uL =a @ LL&uL =a @**+@J LL M    e --/2A  ))+A.A..|< NN67 3E* %..=%< L&..C#%< L <( L   7# Lz)keras.applications.vgg16.preprocess_inputc,[R"XSS9$)Ncaffe)rmode)rpreprocess_input)r^rs rbrhrhs  * *  rdz+keras.applications.vgg16.decode_predictionsc*[R"XS9$)N)top)rdecode_predictions)predsrjs rbrkrks  , ,U <rs "! 4(2)*:8 8  .0JK   #KLK\9:; ;<===*>>EE 55  3 3F ,>>FFrd