instantiates the vgg19 model reference very deep convolutional networks for largescale image recognition https arxiv orgabs1409 1556 iclr 2015 for image classification use cases see this page for detailed examples https keras ioapiapplicationsusageexamplesforimageclassificationmodels for transfer learning use cases make sure to read the guide to transfer learning finetuning https keras ioguidestransferlearning 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 preprocessinput on your inputs before passing them to the model vgg19 preprocessinput will convert the input images from rgb to bgr then will zerocenter each color channel with respect to the imagenet dataset without scaling args includetop whether to include the 3 fullyconnected layers at the top of the network weights one of none random initialization imagenet pretraining on imagenet or the path to the weights file to be loaded inputtensor optional keras tensor i e output of layers input to use as image input for the model inputshape optional shape tuple only to be specified if includetop is false otherwise the input shape has to be 224 224 3 with channelslast data format or 3 224 224 with channelsfirst 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 includetop 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 includetop is true and if no weights argument is specified classifieractivation a str or callable the activation function to use on the top layer ignored unless includetoptrue set classifieractivationnone to return the logits of the top layer when loading pretrained weights classifieractivation can only be none or softmax returns a model instance determine proper input shape block 1 block 2 block 3 block 4 block 5 classification block ensure that the model takes into account any potential predecessors of inputtensor create model load weights 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 determine proper input shape block 1 block 2 block 3 block 4 block 5 classification block ensure that the model takes into account any potential predecessors of input_tensor create model load weights
from keras import backend
from keras import layers
from keras.api_export import keras_export
from keras.applications import imagenet_utils
from keras.models import Functional
from keras.ops import operation_utils
from keras.utils import file_utils

WEIGHTS_PATH = (
    "https://storage.googleapis.com/tensorflow/keras-applications/"
    "vgg19/vgg19_weights_tf_dim_ordering_tf_kernels.h5"
)
WEIGHTS_PATH_NO_TOP = (
    "https://storage.googleapis.com/tensorflow/"
    "keras-applications/vgg19/"
    "vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5"
)


@keras_export(["keras.applications.vgg19.VGG19", "keras.applications.VGG19"])
def VGG19(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
):
    
    if not (weights in {"imagenet", None} or file_utils.exists(weights)):
        raise ValueError(
            "The `weights` argument should be either "
            "`None` (random initialization), 'imagenet' "
            "(pre-training on ImageNet), "
            "or the path to the weights file to be loaded.  Received: "
            f"weights={weights}"
        )

    if weights == "imagenet" and include_top and classes != 1000:
        raise ValueError(
            "If using `weights='imagenet'` with `include_top=True`, "
            "`classes` should be 1000.  "
            f"Received classes={classes}"
        )

    
    input_shape = imagenet_utils.obtain_input_shape(
        input_shape,
        default_size=224,
        min_size=32,
        data_format=backend.image_data_format(),
        require_flatten=include_top,
        weights=weights,
    )

    if input_tensor is None:
        img_input = layers.Input(shape=input_shape)
    else:
        if not backend.is_keras_tensor(input_tensor):
            img_input = layers.Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor
    
    x = layers.Conv2D(
        64, (3, 3), activation="relu", padding="same", name="block1_conv1"
    )(img_input)
    x = layers.Conv2D(
        64, (3, 3), activation="relu", padding="same", name="block1_conv2"
    )(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block1_pool")(x)

    
    x = layers.Conv2D(
        128, (3, 3), activation="relu", padding="same", name="block2_conv1"
    )(x)
    x = layers.Conv2D(
        128, (3, 3), activation="relu", padding="same", name="block2_conv2"
    )(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block2_pool")(x)

    
    x = layers.Conv2D(
        256, (3, 3), activation="relu", padding="same", name="block3_conv1"
    )(x)
    x = layers.Conv2D(
        256, (3, 3), activation="relu", padding="same", name="block3_conv2"
    )(x)
    x = layers.Conv2D(
        256, (3, 3), activation="relu", padding="same", name="block3_conv3"
    )(x)
    x = layers.Conv2D(
        256, (3, 3), activation="relu", padding="same", name="block3_conv4"
    )(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block3_pool")(x)

    
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block4_conv1"
    )(x)
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block4_conv2"
    )(x)
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block4_conv3"
    )(x)
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block4_conv4"
    )(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block4_pool")(x)

    
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block5_conv1"
    )(x)
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block5_conv2"
    )(x)
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block5_conv3"
    )(x)
    x = layers.Conv2D(
        512, (3, 3), activation="relu", padding="same", name="block5_conv4"
    )(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name="block5_pool")(x)

    if include_top:
        
        x = layers.Flatten(name="flatten")(x)
        x = layers.Dense(4096, activation="relu", name="fc1")(x)
        x = layers.Dense(4096, activation="relu", name="fc2")(x)
        imagenet_utils.validate_activation(classifier_activation, weights)
        x = layers.Dense(
            classes, activation=classifier_activation, name="predictions"
        )(x)
    else:
        if pooling == "avg":
            x = layers.GlobalAveragePooling2D()(x)
        elif pooling == "max":
            x = layers.GlobalMaxPooling2D()(x)

    
    
    if input_tensor is not None:
        inputs = operation_utils.get_source_inputs(input_tensor)
    else:
        inputs = img_input

    
    model = Functional(inputs, x, name="vgg19")

    
    if weights == "imagenet":
        if include_top:
            weights_path = file_utils.get_file(
                "vgg19_weights_tf_dim_ordering_tf_kernels.h5",
                WEIGHTS_PATH,
                cache_subdir="models",
                file_hash="cbe5617147190e668d6c5d5026f83318",
            )
        else:
            weights_path = file_utils.get_file(
                "vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5",
                WEIGHTS_PATH_NO_TOP,
                cache_subdir="models",
                file_hash="253f8cb515780f3b799900260a226db6",
            )
        model.load_weights(weights_path)
    elif weights is not None:
        model.load_weights(weights)

    return model


@keras_export("keras.applications.vgg19.preprocess_input")
def preprocess_input(x, data_format=None):
    return imagenet_utils.preprocess_input(
        x, data_format=data_format, mode="caffe"
    )


@keras_export("keras.applications.vgg19.decode_predictions")
def decode_predictions(preds, top=5):
    return imagenet_utils.decode_predictions(preds, top=top)


preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
    mode="",
    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_CAFFE,
    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__