R/layersconvolutional.R
layer_conv_2d.Rd
This layer creates a convolution kernel that is convolved with the layer
input to produce a tensor of outputs. If use_bias
is TRUE, a bias vector is
created and added to the outputs. Finally, if activation
is not NULL
, it
is applied to the outputs as well. When using this layer as the first layer
in a model, provide the keyword argument input_shape
(list of integers,
does not include the sample axis), e.g. input_shape=c(128, 128, 3)
for
128x128 RGB pictures in data_format="channels_last"
.
layer_conv_2d( object, filters, kernel_size, strides = c(1L, 1L), padding = "valid", data_format = NULL, dilation_rate = c(1L, 1L), 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, batch_input_shape = NULL, batch_size = NULL, dtype = NULL, name = NULL, trainable = NULL, weights = NULL )
object  Model or layer object 

filters  Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). 
kernel_size  An integer or 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 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. Specifying any stride
value != 1 is incompatible with specifying any 
padding  one of 
data_format  A string, one of 
dilation_rate  an integer or list of 2 integers, specifying the
dilation rate to use for dilated convolution. Can be a single integer to
specify the same value for all spatial dimensions. Currently, specifying
any 
activation  Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation: 
use_bias  Boolean, whether the layer uses a bias vector. 
kernel_initializer  Initializer for the 
bias_initializer  Initializer for the bias vector. 
kernel_regularizer  Regularizer function applied to the 
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  Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model. 
batch_input_shape  Shapes, including the batch size. For instance,

batch_size  Fixed batch size for layer 
dtype  The data type expected by the input, as a string ( 
name  An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided. 
trainable  Whether the layer weights will be updated during training. 
weights  Initial weights for layer. 
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'.
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.
Other convolutional layers:
layer_conv_1d_transpose()
,
layer_conv_1d()
,
layer_conv_2d_transpose()
,
layer_conv_3d_transpose()
,
layer_conv_3d()
,
layer_conv_lstm_2d()
,
layer_cropping_1d()
,
layer_cropping_2d()
,
layer_cropping_3d()
,
layer_depthwise_conv_2d()
,
layer_separable_conv_1d()
,
layer_separable_conv_2d()
,
layer_upsampling_1d()
,
layer_upsampling_2d()
,
layer_upsampling_3d()
,
layer_zero_padding_1d()
,
layer_zero_padding_2d()
,
layer_zero_padding_3d()