def DenseNet(
    blocks,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    name="densenet",
):
    """Instantiates the DenseNet architecture.

    Reference:
    - [Densely Connected Convolutional Networks](
        https://arxiv.org/abs/1608.06993) (CVPR 2017)

    This function returns a Keras image classification model,
    optionally loaded with weights pre-trained on ImageNet.

    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/).

    Note: each Keras Application expects a specific kind of input preprocessing.
    For DenseNet, call `keras.applications.densenet.preprocess_input`
    on your inputs before passing them to the model.
    `densenet.preprocess_input` will scale pixels between 0 and 1 and then
    will normalize each channel with respect to the ImageNet
    dataset statistics.

    Args:
        blocks: numbers of building blocks for the four dense layers.
        include_top: whether to include the fully-connected
            layer 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 inputs 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. Defaults to `1000`.
        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.
    """
    if backend.image_data_format() == "channels_first":
        raise ValueError(
            "DenseNet does not support the `channels_first` image data "
            "format. Switch to `channels_last` by editing your local "
            "config file at ~/.keras/keras.json"
        )
    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."
        )

    if weights == "imagenet" and include_top and classes != 1000:
        raise ValueError(
            'If using `weights` as `"imagenet"` with `include_top`'
            " as true, `classes` should be 1000"
        )

    # Determine proper input shape
    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

    bn_axis = 3 if backend.image_data_format() == "channels_last" else 1

    x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
    x = layers.Conv2D(64, 7, strides=2, use_bias=False, name="conv1_conv")(x)
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name="conv1_bn"
    )(x)
    x = layers.Activation("relu", name="conv1_relu")(x)
    x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
    x = layers.MaxPooling2D(3, strides=2, name="pool1")(x)

    x = dense_block(x, blocks[0], name="conv2")
    x = transition_block(x, 0.5, name="pool2")
    x = dense_block(x, blocks[1], name="conv3")
    x = transition_block(x, 0.5, name="pool3")
    x = dense_block(x, blocks[2], name="conv4")
    x = transition_block(x, 0.5, name="pool4")
    x = dense_block(x, blocks[3], name="conv5")

    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name="bn")(x)
    x = layers.Activation("relu", name="relu")(x)

    if include_top:
        x = layers.GlobalAveragePooling2D(name="avg_pool")(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(name="avg_pool")(x)
        elif pooling == "max":
            x = layers.GlobalMaxPooling2D(name="max_pool")(x)

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = operation_utils.get_source_inputs(input_tensor)
    else:
        inputs = img_input

    # Create model.
    model = Functional(inputs, x, name=name)

    # Load weights.
    if weights == "imagenet":
        if include_top:
            if blocks == [6, 12, 24, 16]:
                weights_path = file_utils.get_file(
                    "densenet121_weights_tf_dim_ordering_tf_kernels.h5",
                    DENSENET121_WEIGHT_PATH,
                    cache_subdir="models",
                    file_hash="9d60b8095a5708f2dcce2bca79d332c7",
                )
            elif blocks == [6, 12, 32, 32]:
                weights_path = file_utils.get_file(
                    "densenet169_weights_tf_dim_ordering_tf_kernels.h5",
                    DENSENET169_WEIGHT_PATH,
                    cache_subdir="models",
                    file_hash="d699b8f76981ab1b30698df4c175e90b",
                )
            elif blocks == [6, 12, 48, 32]:
                weights_path = file_utils.get_file(
                    "densenet201_weights_tf_dim_ordering_tf_kernels.h5",
                    DENSENET201_WEIGHT_PATH,
                    cache_subdir="models",
                    file_hash="1ceb130c1ea1b78c3bf6114dbdfd8807",
                )
            else:
                raise ValueError("weights_path undefined")
        else:
            if blocks == [6, 12, 24, 16]:
                weights_path = file_utils.get_file(
                    "densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5",
                    DENSENET121_WEIGHT_PATH_NO_TOP,
                    cache_subdir="models",
                    file_hash="30ee3e1110167f948a6b9946edeeb738",
                )
            elif blocks == [6, 12, 32, 32]:
                weights_path = file_utils.get_file(
                    "densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5",
                    DENSENET169_WEIGHT_PATH_NO_TOP,
                    cache_subdir="models",
                    file_hash="b8c4d4c20dd625c148057b9ff1c1176b",
                )
            elif blocks == [6, 12, 48, 32]:
                weights_path = file_utils.get_file(
                    "densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5",
                    DENSENET201_WEIGHT_PATH_NO_TOP,
                    cache_subdir="models",
                    file_hash="c13680b51ded0fb44dff2d8f86ac8bb1",
                )
            else:
                raise ValueError("weights_path undefined")
        model.load_weights(weights_path)
    elif weights is not None:
        model.load_weights(weights)

    return model
Instantiates the DenseNet architecture.

    Reference:
    - [Densely Connected Convolutional Networks](
        https://arxiv.org/abs/1608.06993) (CVPR 2017)

    This function returns a Keras image classification model,
    optionally loaded with weights pre-trained on ImageNet.

    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/).

    Note: each Keras Application expects a specific kind of input preprocessing.
    For DenseNet, call `keras.applications.densenet.preprocess_input`
    on your inputs before passing them to the model.
    `densenet.preprocess_input` will scale pixels between 0 and 1 and then
    will normalize each channel with respect to the ImageNet
    dataset statistics.

    Args:
        blocks: numbers of building blocks for the four dense layers.
        include_top: whether to include the fully-connected
            layer 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 inputs 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. Defaults to `1000`.
        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.
    
keras/src/applications/densenet.py