tf.keras.layers.Conv2D

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Class Conv2D

2D convolution layer (e.g. spatial convolution over images).

Aliases:

  • Class tf.compat.v1.keras.layers.Conv2D
  • Class tf.compat.v1.keras.layers.Convolution2D
  • Class tf.compat.v2.keras.layers.Conv2D
  • Class tf.compat.v2.keras.layers.Convolution2D
  • Class tf.keras.layers.Convolution2D

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, does not include the sample axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last".

Arguments:

  • 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).
  • 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 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.
  • 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:

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.

__init__

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__init__(
    filters,
    kernel_size,
    strides=(1, 1),
    padding='valid',
    data_format=None,
    dilation_rate=(1, 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
)

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