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),
groups = 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
)What to compose the new Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()).
The return value depends on object. If object is:
missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.
Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
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.
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.
one of "valid" or "same" (case-insensitive). Note that "same"
is slightly inconsistent across backends with strides != 1, as described
here
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".
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.
A positive integer specifying the number of groups in which the
input is split along the channel axis. Each group is convolved separately
with filters / groups filters. The output is the concatenation of all the
groups results along the channel axis. Input channels and filters must both
be divisible by groups.
Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation: a(x) = x).
Boolean, whether the layer uses a bias vector.
Initializer for the kernel weights matrix.
Initializer for the bias vector.
Regularizer function applied to the kernel
weights matrix.
Regularizer function applied to the bias vector.
Regularizer function applied to the output of the layer (its "activation")..
Constraint function applied to the kernel matrix.
Constraint function applied to the bias vector.
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.
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.
Fixed batch size for layer
The data type expected by the input, as a string (float32,
float64, int32...)
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.
Whether the layer weights will be updated during training.
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_1d(),
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()