Conv: Convolution layers
In YTLogos/kerasR: R Interface to the Keras Deep Learning Library

Description Usage Arguments Author(s) References See Also Examples

Convolution layers

Conv1D(filters, kernel_size, strides = 1, padding = "valid",
  dilation_rate = 1, 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)

Conv2D(filters, kernel_size, strides = c(1, 1), padding = "valid",
  data_format = NULL, dilation_rate = c(1, 1), 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)

SeparableConv2D(filters, kernel_size, strides = c(1, 1), padding = "valid",
  data_format = NULL, depth_multiplier = 1, dilation_rate = c(1, 1),
  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)

Conv2DTranspose(filters, kernel_size, strides = c(1, 1), padding = "valid",
  data_format = NULL, dilation_rate = c(1, 1), 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)

Conv3D(filters, kernel_size, strides = c(1, 1, 1), padding = "valid",
  data_format = NULL, dilation_rate = c(1, 1, 1), 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)

`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).
`dilation_rate`	A pair of integers specifying the dilation rate to use for dilated convolution
`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.
`depth_multiplier`	The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to filterss_in * depth_multiplier.

Taylor B. Arnold, taylor.arnold@acm.org

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

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

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)
}