tf.keras.layers.Conv2D

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.

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.

Input shape:

  • 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)

A 4D tensor representing activation(conv2d(inputs, kernel) + bias).

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)

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
)