Convolution layers

class dynn.layers.convolution_layers.Conv1D(pc, input_dim, num_kernels, kernel_width, activation=<function identity>, dropout_rate=0.0, nobias=False, zero_padded=True, stride=1, K=None, b=None)

Bases: dynn.layers.base_layers.ParametrizedLayer

1D convolution along the first dimension

Parameters:
  • pc (dynet.ParameterCollection) – Parameter collection to hold the parameters
  • input_dim (int) – Input dimension
  • num_kernels (int) – Number of kernels (essentially the output dimension)
  • kernel_width (int) – Width of the kernels
  • activation (function, optional) – activation function (default: identity)
  • dropout (float, optional) – Dropout rate (default 0)
  • nobias (bool, optional) – Omit the bias (default False)
  • zero_padded (bool, optional) – Default padding behaviour. Pad the input with zeros so that the output has the same length (default True)
  • stride (list, optional) – Default stride along the length (defaults to 1).
__call__(x, stride=None, zero_padded=None)

Forward pass

Parameters:
  • x (dynet.Expression) – Input expression with the shape (length, input_dim)
  • stride (int, optional) – Stride along the temporal dimension
  • zero_padded (bool, optional) – Pad the image with zeros so that the output has the same length (default True)
Returns:

Convolved sequence.
Return type:

dynet.Expression
__init__(pc, input_dim, num_kernels, kernel_width, activation=<function identity>, dropout_rate=0.0, nobias=False, zero_padded=True, stride=1, K=None, b=None)

Creates a subcollection for this layer with a custom name

class dynn.layers.convolution_layers.Conv2D(pc, num_channels, num_kernels, kernel_size, activation=<function identity>, dropout_rate=0.0, nobias=False, zero_padded=True, strides=None, K=None, b=None)

Bases: dynn.layers.base_layers.ParametrizedLayer

2D convolution

Parameters:
  • pc (dynet.ParameterCollection) – Parameter collection to hold the parameters
  • num_channels (int) – Number of channels in the input image
  • num_kernels (int) – Number of kernels (essentially the output dimension)
  • kernel_size (list, optional) – Default kernel size. This is a list of two elements, one per dimension.
  • activation (function, optional) – activation function (default: identity)
  • dropout (float, optional) – Dropout rate (default 0)
  • nobias (bool, optional) – Omit the bias (default False)
  • zero_padded (bool, optional) – Default padding behaviour. Pad the image with zeros so that the output has the same width/height (default True)
  • strides (list, optional) – Default stride along each dimension (list of size 2, defaults to [1, 1]).
__call__(x, strides=None, zero_padded=None)

Forward pass

Parameters:
  • x (dynet.Expression) – Input image (3-d tensor) or matrix.
  • zero_padded (bool, optional) – Pad the image with zeros so that the output has the same width/height. If this is not specified, the default specified in the constructor is used.
  • strides (list, optional) – Stride along width/height. If this is not specified, the default specified in the constructor is used.
Returns:

Convolved image.
Return type:

dynet.Expression
__init__(pc, num_channels, num_kernels, kernel_size, activation=<function identity>, dropout_rate=0.0, nobias=False, zero_padded=True, strides=None, K=None, b=None)

Creates a subcollection for this layer with a custom name