Conv2D
classtf.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
)
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 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)
Arguments
dilation_rate
value != 1."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.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
.dilation_rate
value != 1 is incompatible with specifying any stride
value != 1.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
.keras.activations
).kernel
weights matrix (see
keras.initializers
). Defaults to 'glorot_uniform'.keras.initializers
). Defaults to 'zeros'.kernel
weights
matrix (see keras.regularizers
).keras.regularizers
).keras.regularizers
).keras.constraints
).keras.constraints
).Input shape
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
padding
is "causal"
.strides > 1
and dilation_rate > 1
.