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tf.keras.layers.LocallyConnected2D

TensorFlow 2.0 version View source on GitHub

Class LocallyConnected2D

Locally-connected layer for 2D inputs.

Inherits From: Layer

Aliases:

  • Class tf.compat.v1.keras.layers.LocallyConnected2D
  • Class tf.compat.v2.keras.layers.LocallyConnected2D

The LocallyConnected2D layer works similarly to the Conv2D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

Examples:

    # apply a 3x3 unshared weights convolution with 64 output filters on a
    32x32 image
    # with `data_format="channels_last"`:
    model = Sequential()
    model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3)))
    # now model.output_shape == (None, 30, 30, 64)
    # notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64
    parameters

    # add a 3x3 unshared weights convolution on top, with 32 output filters:
    model.add(LocallyConnected2D(32, (3, 3)))
    # now model.output_shape == (None, 28, 28, 32)

Arguments:

  • filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
  • kernel_size: An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
  • strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions.
  • padding: Currently only support "valid" (case-insensitive). "same" will be supported in future.
  • 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".
  • activation: Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).
  • use_bias: Boolean, whether the layer uses a bias vector.
  • kernel_initializer: Initializer for the kernel weights matrix.
  • bias_initializer: Initializer for the bias vector.
  • kernel_regularizer: Regularizer function applied to the kernel weights matrix.
  • bias_regularizer: Regularizer function applied to the bias vector.
  • activity_regularizer: Regularizer function applied to the output of the layer (its "activation").
  • kernel_constraint: Constraint function applied to the kernel matrix.
  • bias_constraint: Constraint function applied to the bias vector.
  • implementation: implementation mode, either 1 or 2. 1 loops over input spatial locations to perform the forward pass. It is memory-efficient but performs a lot of (small) ops.

    2 stores layer weights in a dense but sparsely-populated 2D matrix and implements the forward pass as a single matrix-multiply. It uses a lot of RAM but performs few (large) ops.

    Depending on the inputs, layer parameters, hardware, and tf.executing_eagerly() one implementation can be dramatically faster (e.g. 50X) than another.

    It is recommended to benchmark both in the setting of interest to pick the most efficient one (in terms of speed and memory usage).

    Following scenarios could benefit from setting implementation=2: - eager execution; - inference; - running on CPU; - large amount of RAM available; - small models (few filters, small kernel); - using padding=same (only possible with implementation=2).

Input shape:

4D tensor with shape: (samples, channels, rows, cols) if data_format='channels_first' or 4D tensor with shape: (samples, rows, cols, channels) if data_format='channels_last'.

Output shape:

4D tensor with shape: (samples, filters, new_rows, new_cols) if data_format='channels_first' or 4D tensor with shape: (samples, new_rows, new_cols, filters) if data_format='channels_last'. rows and cols values might have changed due to padding.

__init__

View source

__init__(
    filters,
    kernel_size,
    strides=(1, 1),
    padding='valid',
    data_format=None,
    activation=None,
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='zeros',
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    implementation=1,
    **kwargs
)