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)
Args
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. "valid" means no padding.
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, 2, or 3. 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. 3 stores layer weights in a sparse tensor
and implements the forward pass as a single sparse matrix-multiply.
How to choose:
1: large, dense models,
2: small models,
3: large, sparse models, where "large" stands for large
input/output activations (i.e. many filters, input_filters,
large np.prod(input_size), np.prod(output_size)), and "sparse"
stands for few connections between inputs and outputs, i.e. small
ratio filters * input_filters * np.prod(kernel_size) /
(np.prod(input_size) * np.prod(strides)), where inputs to and
outputs of the layer are assumed to have shapes input_size +
(input_filters,), output_size + (filters,) respectively. It is
recommended to benchmark each in the setting of interest to pick
the most efficient one (in terms of speed and memory usage).
Correct choice of implementation can lead to dramatic speed
improvements (e.g. 50X), potentially at the expense of RAM. Also,
only padding="valid" is supported by implementation=1.
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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[],[]]