def EfficientNet(
    width_coefficient,
    depth_coefficient,
    default_size,
    dropout_rate=0.2,
    drop_connect_rate=0.2,
    depth_divisor=8,
    activation="swish",
    blocks_args="default",
    name="efficientnet",
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    weights_name=None,
):
    """Instantiates the EfficientNet architecture.

    Args:
      width_coefficient: float, scaling coefficient for network width.
      depth_coefficient: float, scaling coefficient for network depth.
      default_size: integer, default input image size.
      dropout_rate: float, dropout rate before final classifier layer.
      drop_connect_rate: float, dropout rate at skip connections.
      depth_divisor: integer, a unit of network width.
      activation: activation function.
      blocks_args: list of dicts, parameters to construct block modules.
      name: string, model name.
      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.
          It should have exactly 3 inputs channels.
      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 layer.
          - `avg` means that global average pooling
              will be applied to the output of the
              last convolutional layer, 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.

    Returns:
        A model instance.
    """
    if blocks_args == "default":
        blocks_args = DEFAULT_BLOCKS_ARGS

    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="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=default_size,
        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

    def round_filters(filters, divisor=depth_divisor):
        """Round number of filters based on depth multiplier."""
        filters *= width_coefficient
        new_filters = max(
            divisor, int(filters + divisor / 2) // divisor * divisor
        )
        # Make sure that round down does not go down by more than 10%.
        if new_filters < 0.9 * filters:
            new_filters += divisor
        return int(new_filters)

    def round_repeats(repeats):
        """Round number of repeats based on depth multiplier."""
        return int(math.ceil(depth_coefficient * repeats))

    # Build stem
    x = img_input
    x = layers.Rescaling(1.0 / 255.0)(x)
    x = layers.Normalization(axis=bn_axis)(x)

    if weights == "imagenet":
        # Note that the normalization layer uses square value of STDDEV as the
        # variance for the layer: result = (input - mean) / sqrt(var)
        # However, the original implementation uses (input - mean) / var to
        # normalize the input, we need to divide another sqrt(var) to match the
        # original implementation.
        # See https://github.com/tensorflow/tensorflow/issues/49930 for more
        # details
        x = layers.Rescaling(
            [1.0 / math.sqrt(stddev) for stddev in IMAGENET_STDDEV_RGB]
        )(x)

    x = layers.ZeroPadding2D(
        padding=imagenet_utils.correct_pad(x, 3), name="stem_conv_pad"
    )(x)
    x = layers.Conv2D(
        round_filters(32),
        3,
        strides=2,
        padding="valid",
        use_bias=False,
        kernel_initializer=CONV_KERNEL_INITIALIZER,
        name="stem_conv",
    )(x)
    x = layers.BatchNormalization(axis=bn_axis, name="stem_bn")(x)
    x = layers.Activation(activation, name="stem_activation")(x)

    # Build blocks
    blocks_args = copy.deepcopy(blocks_args)

    b = 0
    blocks = float(sum(round_repeats(args["repeats"]) for args in blocks_args))
    for i, args in enumerate(blocks_args):
        assert args["repeats"] > 0
        # Update block input and output filters based on depth multiplier.
        args["filters_in"] = round_filters(args["filters_in"])
        args["filters_out"] = round_filters(args["filters_out"])

        for j in range(round_repeats(args.pop("repeats"))):
            # The first block needs to take care of stride and filter size
            # increase.
            if j > 0:
                args["strides"] = 1
                args["filters_in"] = args["filters_out"]
            x = block(
                x,
                activation,
                drop_connect_rate * b / blocks,
                name=f"block{i + 1}{chr(j + 97)}_",
                **args,
            )
            b += 1

    # Build top
    x = layers.Conv2D(
        round_filters(1280),
        1,
        padding="same",
        use_bias=False,
        kernel_initializer=CONV_KERNEL_INITIALIZER,
        name="top_conv",
    )(x)
    x = layers.BatchNormalization(axis=bn_axis, name="top_bn")(x)
    x = layers.Activation(activation, name="top_activation")(x)
    if include_top:
        x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
        if dropout_rate > 0:
            x = layers.Dropout(dropout_rate, name="top_dropout")(x)
        imagenet_utils.validate_activation(classifier_activation, weights)
        x = layers.Dense(
            classes,
            activation=classifier_activation,
            kernel_initializer=DENSE_KERNEL_INITIALIZER,
            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:
            file_suffix = ".h5"
            file_hash = WEIGHTS_HASHES[weights_name][0]
        else:
            file_suffix = "_notop.h5"
            file_hash = WEIGHTS_HASHES[weights_name][1]
        file_name = name + file_suffix
        weights_path = file_utils.get_file(
            file_name,
            BASE_WEIGHTS_PATH + file_name,
            cache_subdir="models",
            file_hash=file_hash,
        )
        model.load_weights(weights_path)
    elif weights is not None:
        model.load_weights(weights)
    return model
Instantiates the EfficientNet architecture.

    Args:
      width_coefficient: float, scaling coefficient for network width.
      depth_coefficient: float, scaling coefficient for network depth.
      default_size: integer, default input image size.
      dropout_rate: float, dropout rate before final classifier layer.
      drop_connect_rate: float, dropout rate at skip connections.
      depth_divisor: integer, a unit of network width.
      activation: activation function.
      blocks_args: list of dicts, parameters to construct block modules.
      name: string, model name.
      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.
          It should have exactly 3 inputs channels.
      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 layer.
          - `avg` means that global average pooling
              will be applied to the output of the
              last convolutional layer, 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.

    Returns:
        A model instance.
    
keras/src/applications/efficientnet.py