2D convolution layer (e.g. spatial convolution over images).
Inherits From: Layer
, Module
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
)
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 None
, 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
(tuple of integers or None
, does not include the sample axis),
e.g. input_shape=(128, 128, 3)
for 128x128 RGB pictures
in data_format="channels_last"
. You can use None
when
a dimension has variable size.
Examples:
# The inputs are 28x28 RGB images with `channels_last` and the batch
# size is 4.
input_shape = (4, 28, 28, 3)
x = tf.random.normal(input_shape)
y = tf.keras.layers.Conv2D(
2, 3, activation='relu', input_shape=input_shape[1:])(x)
print(y.shape)
(4, 26, 26, 2)
# With `dilation_rate` as 2.
input_shape = (4, 28, 28, 3)
x = tf.random.normal(input_shape)
y = tf.keras.layers.Conv2D(
2, 3,
activation='relu',
dilation_rate=2,
input_shape=input_shape[1:])(x)
print(y.shape)
(4, 24, 24, 2)
# With `padding` as "same".
input_shape = (4, 28, 28, 3)
x = tf.random.normal(input_shape)
y = tf.keras.layers.Conv2D(
2, 3, activation='relu', padding="same", input_shape=input_shape[1:])(x)
print(y.shape)
(4, 28, 28, 2)
# With extended batch shape [4, 7]:
input_shape = (4, 7, 28, 28, 3)
x = tf.random.normal(input_shape)
y = tf.keras.layers.Conv2D(
2, 3, activation='relu', input_shape=input_shape[2:])(x)
print(y.shape)
(4, 7, 26, 26, 2)
Args |
filters
|
Integer, the dimensionality of the output space (i.e. the number
of output filters in the convolution).
|
kernel_size
|
An integer or tuple/list of 2 integers, specifying the height
and width of the 2D convolution window. Can be a single integer to
specify the same value for all spatial dimensions.
|
strides
|
An integer or tuple/list of 2 integers, specifying the strides of
the convolution along the height and width. 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).
"valid" means no padding. "same" results in padding with zeros
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
|
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_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 . Note that the channels_first format is currently not
supported by TensorFlow on CPU.
|
dilation_rate
|
an integer or tuple/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 (see keras.activations ).
|
use_bias
|
Boolean, whether the layer uses a bias vector.
|
kernel_initializer
|
Initializer for the kernel weights matrix (see
keras.initializers ). Defaults to 'glorot_uniform'.
|
bias_initializer
|
Initializer for the bias vector (see
keras.initializers ). Defaults to 'zeros'.
|
kernel_regularizer
|
Regularizer function applied to the kernel weights
matrix (see keras.regularizers ).
|
bias_regularizer
|
Regularizer function applied to the bias vector (see
keras.regularizers ).
|
activity_regularizer
|
Regularizer function applied to the output of the
layer (its "activation") (see keras.regularizers ).
|
kernel_constraint
|
Constraint function applied to the kernel matrix (see
keras.constraints ).
|
bias_constraint
|
Constraint function applied to the bias vector (see
keras.constraints ).
|
|
4+D tensor with shape: batch_shape + (channels, rows, cols) if
data_format='channels_first'
or 4+D tensor with shape: batch_shape + (rows, cols, channels) if
data_format='channels_last' .
|
Output shape |
4+D tensor with shape: batch_shape + (filters, new_rows, new_cols) if
data_format='channels_first' or 4+D tensor with shape: batch_shape +
(new_rows, new_cols, filters) if data_format='channels_last' . rows
and cols values might have changed due to padding.
|
Returns |
A tensor of rank 4+ representing
activation(conv2d(inputs, kernel) + bias) .
|
Raises |
ValueError
|
if padding is "causal" .
|
ValueError
|
when both strides > 1 and dilation_rate > 1 .
|
Methods
convolution_op
View source
convolution_op(
inputs, kernel
)