# layer_conv_2d

##### 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"`

.

##### Usage

```
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`

*Documentation reproduced from package keras, version 2.2.4, License: MIT + file LICENSE*