2D convolution layer (e.g. spatial convolution over images).

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)

Arguments

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 dilation_rate value != 1.
padding

one of "valid" or "same" (case-insensitive). Note that "same" is slightly inconsistent across backends with strides != 1, as described here
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".
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 dilation_rate value != 1 is incompatible with specifying any stride value != 1.
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.
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_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.
batch_size

Fixed batch size for layer
dtype

The data type expected by the input, as a string (float32, float64, int32...)
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.

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.

See also

Other convolutional layers: 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