Functions

def DenseNet(blocks, include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, residuals=False, **kwargs)

Instantiates the DenseNet architecture. Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json.

Arguments

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.

Returns

A Keras model instance.

Raises

ValueError: in case of invalid argument for `weights`,
    or invalid input shape.

Expand source code

def DenseNet(
    blocks,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    residuals=False,
    **kwargs
):
    """Instantiates the DenseNet architecture.
    Optionally loads weights pre-trained on ImageNet.
    Note that the data format convention used by the model is
    the one specified in your Keras config at `~/.keras/keras.json`.
    # Arguments
        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.
    # Returns
        A Keras model instance.
    # Raises
        ValueError: in case of invalid argument for `weights`,
            or invalid input shape.
    """
    if not (weights in {"imagenet", None} or os.path.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 = _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

    res_outputs = []
    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")
    if residuals == 2:
        res_outputs = x
    x = transition_block(x, 0.5, name="pool2")
    x = dense_block(x, blocks[1], name="conv3")
    if residuals == 3:
        res_outputs = x
    x = transition_block(x, 0.5, name="pool3")
    x = dense_block(x, blocks[2], name="conv4")
    x = transition_block(x, 0.5, name="pool4", pool=False)
    x = dense_block(x, blocks[3], name="conv5", dilation=2)

    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)
        x = layers.Dense(classes, activation="softmax", name="fc1000")(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 = keras_utils.get_source_inputs(input_tensor)
    else:
        inputs = img_input

    # Create model.
    if blocks == [6, 12, 24, 16]:
        model = models.Model(inputs, x, name="densenet121")
    elif blocks == [6, 12, 32, 32]:
        model = models.Model(inputs, x, name="densenet169")
    elif blocks == [6, 12, 48, 32]:
        model = models.Model(inputs, x, name="densenet201")
    else:
        model = models.Model(inputs, x, name="densenet")

    # Load weights.
    if weights == "imagenet":
        if include_top:
            if blocks == [6, 12, 24, 16]:
                weights_path = keras_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 = keras_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 = keras_utils.get_file(
                    "densenet201_weights_tf_dim_ordering_tf_kernels.h5",
                    DENSENET201_WEIGHT_PATH,
                    cache_subdir="models",
                    file_hash="1ceb130c1ea1b78c3bf6114dbdfd8807",
                )
        else:
            if blocks == [6, 12, 24, 16]:
                weights_path = keras_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 = keras_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 = keras_utils.get_file(
                    "densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5",
                    DENSENET201_WEIGHT_PATH_NO_TOP,
                    cache_subdir="models",
                    file_hash="c13680b51ded0fb44dff2d8f86ac8bb1",
                )
        model.load_weights(weights_path)
    elif weights is not None:
        model.load_weights(weights)
    if residuals:
        model = models.Model(inputs, res_outputs, name="densenet121")
    return model

def DenseNet121(include_top=False, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, residuals=False, **kwargs)

Expand source code

def DenseNet121(
    include_top=False,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    residuals=False,
    **kwargs
):
    return DenseNet(
        [6, 12, 24, 16],
        include_top,
        weights,
        input_tensor,
        input_shape,
        pooling,
        classes,
        residuals,
        **kwargs
    )

def DenseNet169(include_top=False, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs)

Expand source code

def DenseNet169(
    include_top=False,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    **kwargs
):
    return DenseNet(
        [6, 12, 32, 32],
        include_top,
        weights,
        input_tensor,
        input_shape,
        pooling,
        classes,
        **kwargs
    )

def DenseNet201(include_top=False, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs)

Expand source code

def DenseNet201(
    include_top=False,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    **kwargs
):
    return DenseNet(
        [6, 12, 48, 32],
        include_top,
        weights,
        input_tensor,
        input_shape,
        pooling,
        classes,
        **kwargs
    )

def conv_block(x, growth_rate, name, dilation=1)

A building block for a dense block.

Arguments

x: input tensor.
growth_rate: float, growth rate at dense layers.
name: string, block label.

Returns

Output tensor for the block.

Expand source code

def conv_block(x, growth_rate, name, dilation=1):
    """A building block for a dense block.
    # Arguments
        x: input tensor.
        growth_rate: float, growth rate at dense layers.
        name: string, block label.
    # Returns
        Output tensor for the block.
    """
    bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
    x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + "_0_bn")(
        x
    )
    x1 = layers.Activation("relu", name=name + "_0_relu")(x1)
    x1 = layers.Conv2D(4 * growth_rate, 1, use_bias=False, name=name + "_1_conv")(x1)
    x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + "_1_bn")(
        x1
    )
    x1 = layers.Activation("relu", name=name + "_1_relu")(x1)
    x1 = layers.Conv2D(
        growth_rate,
        3,
        padding="same",
        use_bias=False,
        dilation_rate=dilation,
        name=name + "_2_conv",
    )(x1)
    x = layers.Concatenate(axis=bn_axis, name=name + "_concat")([x, x1])
    return x

def dense_block(x, blocks, name, dilation=1)

A dense block.

Arguments

x: input tensor.
blocks: integer, the number of building blocks.
name: string, block label.

Returns

output tensor for the block.

Expand source code

def dense_block(x, blocks, name, dilation=1):
    """A dense block.
    # Arguments
        x: input tensor.
        blocks: integer, the number of building blocks.
        name: string, block label.
    # Returns
        output tensor for the block.
    """
    for i in range(blocks):
        x = conv_block(x, 32, name=name + "_block" + str(i + 1), dilation=dilation)
    return x

def transition_block(x, reduction, name, pool=True)

A transition block.

Arguments

x: input tensor.
reduction: float, compression rate at transition layers.
name: string, block label.

Returns

output tensor for the block.

Expand source code

def transition_block(x, reduction, name, pool=True):
    """A transition block.
    # Arguments
        x: input tensor.
        reduction: float, compression rate at transition layers.
        name: string, block label.
    # Returns
        output tensor for the block.
    """
    bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + "_bn")(x)
    x = layers.Activation("relu", name=name + "_relu")(x)
    x = layers.Conv2D(
        int(backend.int_shape(x)[bn_axis] * reduction),
        1,
        use_bias=False,
        name=name + "_conv",
    )(x)
    if pool:
        x = layers.AveragePooling2D(2, strides=2, name=name + "_pool")(x)
    return x