LocallyConnected: Locally-connected layer

In kerasR: R Interface to the Keras Deep Learning Library

Locally-connected layer

Description

The LocallyConnected layers works similarly to the Conv layers, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

Usage

LocallyConnected1D(filters, kernel_size, strides = 1, padding = "valid",
  activation = NULL, use_bias = TRUE,
  kernel_initializer = "glorot_uniform", bias_initializer = "zeros",
  kernel_regularizer = NULL, bias_regularizer = NULL,
  activity_regularizer = NULL, kernel_constraint = NULL,
  bias_constraint = NULL, input_shape = NULL)

LocallyConnected2D(filters, kernel_size, strides = c(1, 1),
  padding = "valid", data_format = NULL, activation = NULL,
  use_bias = TRUE, kernel_initializer = "glorot_uniform",
  bias_initializer = "zeros", kernel_regularizer = NULL,
  bias_regularizer = NULL, activity_regularizer = NULL,
  kernel_constraint = NULL, bias_constraint = NULL, input_shape = NULL)

Arguments

filters	Integer, the dimensionality of the output space (i.e. the number output of filters in the convolution).
kernel_size	A pair of integers specifying the dimensions of the 2D convolution window.
strides	A pair of integers specifying the stride length of the convolution.
padding	One of "valid", "causal" or "same" (case-insensitive).
activation	Activation function to use
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
input_shape	only need when first layer of a model; sets the input shape of the data
data_format	A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs.

Author(s)

Taylor B. Arnold, [e-mail]

References

Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.

See Also

Other layers: Activation, ActivityRegularization, AdvancedActivation, BatchNormalization, Conv, Dense, Dropout, Embedding, Flatten, GaussianNoise, LayerWrapper, Masking, MaxPooling, Permute, RNN, RepeatVector, Reshape, Sequential

Examples

if(keras_available()) {
  X_train <- array(rnorm(100 * 28 * 28), dim = c(100, 28, 28, 1))
  Y_train <- to_categorical(matrix(sample(0:2, 100, TRUE), ncol = 1), 3)
  
  mod <- Sequential()
  mod$add(Conv2D(filters = 2, kernel_size = c(2, 2),
                 input_shape = c(28, 28, 1)))
  mod$add(Activation("relu"))
  mod$add(MaxPooling2D(pool_size=c(2, 2)))
  mod$add(LocallyConnected2D(filters = 2, kernel_size = c(2, 2)))
  mod$add(Activation("relu"))
  mod$add(MaxPooling2D(pool_size=c(2, 2)))
  mod$add(Dropout(0.25))
  
  mod$add(Flatten())
  mod$add(Dropout(0.5))
  mod$add(Dense(3, activation='softmax'))
  
  keras_compile(mod, loss='categorical_crossentropy', optimizer=RMSprop())
  keras_fit(mod, X_train, Y_train, verbose = 0)
}

kerasR documentation built on Aug. 17, 2022, 5:06 p.m.