contrib.keras.layers.Cropping2D

tf.contrib.keras.layers.Cropping2D

class tf.contrib.keras.layers.Cropping2D

Defined in tensorflow/contrib/keras/python/keras/layers/convolutional.py.

Cropping layer for 2D input (e.g. picture).

It crops along spatial dimensions, i.e. width and height.

Arguments:

cropping: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
    - If int: the same symmetric cropping
        is applied to width and height.
    - If tuple of 2 ints:
        interpreted as two different
        symmetric cropping values for height and width:
        `(symmetric_height_crop, symmetric_width_crop)`.
    - If tuple of 2 tuples of 2 ints:
        interpreted as
        `((top_crop, bottom_crop), (left_crop, right_crop))`
data_format: A string,
    one of `channels_last` (default) or `channels_first`.
    The ordering of the dimensions in the inputs.
    `channels_last` corresponds to inputs with shape
    `(batch, height, width, channels)` while `channels_first`
    corresponds to inputs with shape
    `(batch, channels, height, width)`.
    It defaults to the `image_data_format` value found in your
    Keras config file at `~/.keras/keras.json`.
    If you never set it, then it will be "channels_last".

Input shape: 4D tensor with shape: - If data_format is "channels_last": (batch, rows, cols, channels) - If data_format is "channels_first": (batch, channels, rows, cols)

Output shape: 4D tensor with shape: - If data_format is "channels_last": (batch, cropped_rows, cropped_cols, channels) - If data_format is "channels_first": (batch, channels, cropped_rows, cropped_cols)

Examples:

# Crop the input 2D images or feature maps
model = Sequential()
model.add(Cropping2D(cropping=((2, 2), (4, 4)),
                     input_shape=(28, 28, 3)))
# now model.output_shape == (None, 24, 20, 3)
model.add(Conv2D(64, (3, 3), padding='same))
model.add(Cropping2D(cropping=((2, 2), (2, 2))))
# now model.output_shape == (None, 20, 16. 64)

Properties

constraints

graph

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Input tensor or list of input tensors.

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Input mask tensor (potentially None) or list of input
mask tensors.

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Input shape, as `TensorShape`
(or list of `TensorShape`, one tuple per input tensor).

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

losses

non_trainable_variables

non_trainable_weights

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Output tensor or list of output tensors.

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Output mask tensor (potentially None) or list of output
mask tensors.

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one inbound node, or if all inbound nodes have the same output shape.

Returns:

Output shape, as `TensorShape`
(or list of `TensorShape`, one tuple per output tensor).

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

scope_name

trainable_variables

trainable_weights

updates

variables

Returns the list of all layer variables/weights.

Returns:

A list of variables.

weights

Returns the list of all layer variables/weights.

Returns:

A list of variables.

Methods

__init__

__init__(
    cropping=((0, 0), (0, 0)),
    data_format=None,
    **kwargs
)

__call__

__call__(
    inputs,
    **kwargs
)

Wrapper around self.call(), for handling internal references.

If a Keras tensor is passed: - We call self._add_inbound_node(). - If necessary, we build the layer to match the shape of the input(s). - We update the _keras_history of the output tensor(s) with the current layer. This is done as part of _add_inbound_node().

Arguments:

inputs: Can be a tensor or list/tuple of tensors.
**kwargs: Additional keyword arguments to be passed to `call()`.

Returns:

Output of the layer's `call` method.

Raises:

ValueError: in case the layer is missing shape information
    for its `build` call.

__deepcopy__

__deepcopy__(memo)

add_loss

add_loss(
    losses,
    inputs=None
)

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing a same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Arguments:

  • losses: Loss tensor, or list/tuple of tensors.
  • inputs: Optional input tensor(s) that the loss(es) depend on. Must match the inputs argument passed to the __call__ method at the time the losses are created. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).

add_update

add_update(
    updates,
    inputs=None
)

Add update op(s), potentially dependent on layer inputs.

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing a same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

Arguments:

  • updates: Update op, or list/tuple of update ops.
  • inputs: Optional input tensor(s) that the update(s) depend on. Must match the inputs argument passed to the __call__ method at the time the updates are created. If None is passed, the updates are assumed to be unconditional, and will apply across all dataflows of the layer.

add_variable

add_variable(
    name,
    shape,
    dtype=None,
    initializer=None,
    regularizer=None,
    trainable=True
)

Adds a new variable to the layer, or gets an existing one; returns it.

Arguments:

  • name: variable name.
  • shape: variable shape.
  • dtype: The type of the variable. Defaults to self.dtype.
  • initializer: initializer instance (callable).
  • regularizer: regularizer instance (callable).
  • trainable: whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean, stddev).

Returns:

The created variable.

add_weight

add_weight(
    name,
    shape,
    dtype=None,
    initializer=None,
    regularizer=None,
    trainable=True,
    constraint=None
)

Adds a weight variable to the layer.

Arguments:

name: String, the name for the weight variable.
shape: The shape tuple of the weight.
dtype: The dtype of the weight.
initializer: An Initializer instance (callable).
regularizer: An optional Regularizer instance.
trainable: A boolean, whether the weight should
    be trained via backprop or not (assuming
    that the layer itself is also trainable).
constraint: An optional Constraint instance.

Returns:

The created weight variable.

apply

apply(
    inputs,
    *args,
    **kwargs
)

Apply the layer on a input.

This simply wraps self.__call__.

Arguments:

  • inputs: Input tensor(s). args: additional positional arguments to be passed to self.call.
    *kwargs: additional keyword arguments to be passed to self.call.

Returns:

Output tensor(s).

build

build(input_shape)

Creates the layer weights.

Must be implemented on all layers that have weights.

Arguments:

input_shape: Keras tensor (future input to layer)
    or list/tuple of Keras tensors to reference
    for weight shape computations.

call

call(inputs)

compute_mask

compute_mask(
    inputs,
    mask=None
)

Computes an output mask tensor.

Arguments:

inputs: Tensor or list of tensors.
mask: Tensor or list of tensors.

Returns:

None or a tensor (or list of tensors,
    one per output tensor of the layer).

count_params

count_params()

Count the total number of scalars composing the weights.

Returns:

An integer count.

Raises:

RuntimeError: if the layer isn't yet built
    (in which case its weights aren't yet defined).

from_config

from_config(
    cls,
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Container), nor weights (handled by set_weights).

Arguments:

config: A Python dictionary, typically the
    output of get_config.

Returns:

A layer instance.

get_config

get_config()

get_input_at

get_input_at(node_index)

Retrieves the input tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple inputs).

get_input_mask_at

get_input_mask_at(node_index)

Retrieves the input mask tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A mask tensor
(or list of tensors if the layer has multiple inputs).

get_input_shape_at

get_input_shape_at(node_index)

Retrieves the input shape(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A shape tuple
(or list of shape tuples if the layer has multiple inputs).

get_losses_for

get_losses_for(inputs)

Retrieves losses relevant to a specific set of inputs.

Arguments:

  • inputs: Input tensor or list/tuple of input tensors. Must match the inputs argument passed to the __call__ method at the time the losses were created. If you pass inputs=None, unconditional losses are returned, such as weight regularization losses.

Returns:

List of loss tensors of the layer that depend on inputs.

get_output_at

get_output_at(node_index)

Retrieves the output tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple outputs).

get_output_mask_at

get_output_mask_at(node_index)

Retrieves the output mask tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A mask tensor
(or list of tensors if the layer has multiple outputs).

get_output_shape_at

get_output_shape_at(node_index)

Retrieves the output shape(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A shape tuple
(or list of shape tuples if the layer has multiple outputs).

get_updates_for

get_updates_for(inputs)

Retrieves updates relevant to a specific set of inputs.

Arguments:

  • inputs: Input tensor or list/tuple of input tensors. Must match the inputs argument passed to the __call__ method at the time the updates were created. If you pass inputs=None, unconditional updates are returned.

Returns:

List of update ops of the layer that depend on inputs.

get_weights

get_weights()

Returns the current weights of the layer.

Returns:

Weights values as a list of numpy arrays.

set_weights

set_weights(weights)

Sets the weights of the layer, from Numpy arrays.

Arguments:

weights: a list of Numpy arrays. The number
    of arrays and their shape must match
    number of the dimensions of the weights
    of the layer (i.e. it should match the
    output of `get_weights`).

Raises:

ValueError: If the provided weights list does not match the
    layer's specifications.

© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/keras/layers/Cropping2D

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