2D convolution layer.
Inherits From: Layer, Operation
tf.keras.layers.Conv2D(
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
groups=1,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs
)
Used in the notebooks
| Used in the guide |
Used in the tutorials |
|
|
|
This layer creates a convolution kernel that is convolved with the layer
input over a single spatial (or temporal) dimension 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 None, it is applied to the
outputs as well.
Args |
|---|
filters
|
int, the dimension of the output space (the number of filters
in the convolution).
|
kernel_size
|
int or tuple/list of 2 integer, specifying the size of the
convolution window.
|
strides
|
int or tuple/list of 2 integer, specifying the stride length
of the convolution. strides > 1 is incompatible with
dilation_rate > 1.
|
padding
|
string, either "valid" or "same" (case-insensitive).
"valid" means no padding. "same" results in padding evenly to
the left/right or up/down of the input. When padding="same" and
strides=1, the output has the same size as the input.
|
data_format
|
string, either "channels_last" or "channels_first".
The ordering of the dimensions in the inputs. "channels_last"
corresponds to inputs with shape
(batch_size, height, width, channels)
while "channels_first" corresponds to inputs with shape
(batch_size, 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
|
int or tuple/list of 2 integers, specifying the dilation
rate to use for dilated convolution.
|
groups
|
A positive int 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. If None, no activation is applied.
|
use_bias
|
bool, if True, bias will be added to the output.
|
kernel_initializer
|
Initializer for the convolution kernel. If None,
the default initializer ("glorot_uniform") will be used.
|
bias_initializer
|
Initializer for the bias vector. If None, the
default initializer ("zeros") will be used.
|
kernel_regularizer
|
Optional regularizer for the convolution kernel.
|
bias_regularizer
|
Optional regularizer for the bias vector.
|
activity_regularizer
|
Optional regularizer function for the output.
|
kernel_constraint
|
Optional projection function to be applied to the
kernel after being updated by an Optimizer (e.g. used to implement
norm constraints or value constraints for layer weights). The
function must take as input the unprojected variable and must return
the projected variable (which must have the same shape). Constraints
are not safe to use when doing asynchronous distributed training.
|
bias_constraint
|
Optional projection function to be applied to the
bias after being updated by an Optimizer.
|
- If
data_format="channels_last":
A 4D tensor with shape: (batch_size, height, width, channels)
- If
data_format="channels_first":
A 4D tensor with shape: (batch_size, channels, height, width)
Output shape:
- If
data_format="channels_last":
A 4D tensor with shape: (batch_size, new_height, new_width, filters)
- If
data_format="channels_first":
A 4D tensor with shape: (batch_size, filters, new_height, new_width)
Returns |
|---|
A 4D tensor representing activation(conv2d(inputs, kernel) + bias).
|
Raises |
|---|
ValueError
|
when both strides > 1 and dilation_rate > 1.
|
Example:
x = np.random.rand(4, 10, 10, 128)
y = keras.layers.Conv2D(32, 3, activation='relu')(x)
print(y.shape)
(4, 8, 8, 32)
Attributes |
|---|
input
|
Retrieves the input tensor(s) of a symbolic operation.
Only returns the tensor(s) corresponding to the first time
the operation was called.
|
kernel
|
|
output
|
Retrieves the output tensor(s) of a layer.
Only returns the tensor(s) corresponding to the first time
the operation was called.
|
Methods
convolution_op
View source
convolution_op(
inputs, kernel
)
enable_lora
View source
enable_lora(
rank, a_initializer='he_uniform', b_initializer='zeros'
)
from_config
View source
@classmethod
from_config(
config
)
Creates a layer from its config.
This method is the reverse of get_config,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights).
| Args |
|---|
config
|
A Python dictionary, typically the
output of get_config.
|
| Returns |
|---|
|
A layer instance.
|
symbolic_call
View source
symbolic_call(
*args, **kwargs
)
View source on GitHub