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
)
```

- object
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.

- 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.- groups
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
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.

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()`