Densely connected layers

class dynn.layers.dense_layers.Affine(pc, input_dim, output_dim, activation=<function identity>, dropout=0.0, nobias=False, W=None, b=None)

Bases: dynn.layers.base_layers.ParametrizedLayer

Densely connected layer

\(y=f(Wx+b)\)

Parameters:

• pc (dynet.ParameterCollection) – Parameter collection to hold the parameters
• input_dim (int) – Input dimension
• output_dim (int) – Output dimension
• activation (function, optional) – activation function (default: :py:function:`identity`)
• dropout (float, optional) – Dropout rate (default 0)
• nobias (bool, optional) – Omit the bias (default False)

__call__(x)

Forward pass.

Parameters:x (dynet.Expression) – Input expression (a vector) Returns:\(y=f(Wx+b)\) Return type:dynet.Expression

__init__(pc, input_dim, output_dim, activation=<function identity>, dropout=0.0, nobias=False, W=None, b=None)

Creates a subcollection for this layer with a custom name

class dynn.layers.dense_layers.GatedLayer(pc, input_dim, output_dim, activation=<built-in function tanh>, dropout=0.0, Wo=None, bo=None, Wg=None, bg=None)

Bases: dynn.layers.base_layers.ParametrizedLayer

Gated linear layer:

\(y=(W_ox+b_o)\circ \sigma(W_gx+b_g)\)

Parameters:

• pc (dynet.ParameterCollection) – Parameter collection to hold the parameters
• input_dim (int) – Input dimension
• output_dim (int) – Output dimension
• activation (function, optional) – activation function (default: dynet.tanh)
• dropout (float, optional) – Dropout rate (default 0)

__call__(x)

Forward pass

Parameters:x (dynet.Expression) – Input expression (a vector) Returns:\(y=(W_ox+b_o)\circ \sigma(W_gx+b_g)\) Return type:dynet.Expression

__init__(pc, input_dim, output_dim, activation=<built-in function tanh>, dropout=0.0, Wo=None, bo=None, Wg=None, bg=None)

Creates a subcollection for this layer with a custom name