Dense layers¶
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class
lasagne.layers.
DenseLayer
(incoming, num_units, W=lasagne.init.GlorotUniform(), b=lasagne.init.Constant(0.), nonlinearity=lasagne.nonlinearities.rectify, num_leading_axes=1, **kwargs)[source]¶ A fully connected layer.
Parameters: incoming : a
Layer
instance or a tupleThe layer feeding into this layer, or the expected input shape
num_units : int
The number of units of the layer
W : Theano shared variable, expression, numpy array or callable
Initial value, expression or initializer for the weights. These should be a matrix with shape
(num_inputs, num_units)
. Seelasagne.utils.create_param()
for more information.b : Theano shared variable, expression, numpy array, callable or
None
Initial value, expression or initializer for the biases. If set to
None
, the layer will have no biases. Otherwise, biases should be a 1D array with shape(num_units,)
. Seelasagne.utils.create_param()
for more information.nonlinearity : callable or None
The nonlinearity that is applied to the layer activations. If None is provided, the layer will be linear.
num_leading_axes : int
Number of leading axes to distribute the dot product over. These axes will be kept in the output tensor, remaining axes will be collapsed and multiplied against the weight matrix. A negative number gives the (negated) number of trailing axes to involve in the dot product.
Examples
>>> from lasagne.layers import InputLayer, DenseLayer >>> l_in = InputLayer((100, 20)) >>> l1 = DenseLayer(l_in, num_units=50)
If the input has more than two axes, by default, all trailing axes will be flattened. This is useful when a dense layer follows a convolutional layer.
>>> l_in = InputLayer((None, 10, 20, 30)) >>> DenseLayer(l_in, num_units=50).output_shape (None, 50)
Using the num_leading_axes argument, you can specify to keep more than just the first axis. E.g., to apply the same dot product to each step of a batch of time sequences, you would want to keep the first two axes.
>>> DenseLayer(l_in, num_units=50, num_leading_axes=2).output_shape (None, 10, 50) >>> DenseLayer(l_in, num_units=50, num_leading_axes=-1).output_shape (None, 10, 20, 50)
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get_output_for
(input, **kwargs)[source]¶ Propagates the given input through this layer (and only this layer).
Parameters: input : Theano expression
The expression to propagate through this layer.
Returns: output : Theano expression
The output of this layer given the input to this layer.
Notes
This is called by the base
lasagne.layers.get_output()
to propagate data through a network.This method should be overridden when implementing a new
Layer
class. By default it raises NotImplementedError.
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get_output_shape_for
(input_shape)[source]¶ Computes the output shape of this layer, given an input shape.
Parameters: input_shape : tuple
A tuple representing the shape of the input. The tuple should have as many elements as there are input dimensions, and the elements should be integers or None.
Returns: tuple
A tuple representing the shape of the output of this layer. The tuple has as many elements as there are output dimensions, and the elements are all either integers or None.
Notes
This method will typically be overridden when implementing a new
Layer
class. By default it simply returns the input shape. This means that a layer that does not modify the shape (e.g. because it applies an elementwise operation) does not need to override this method.
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class
lasagne.layers.
NINLayer
(incoming, num_units, untie_biases=False, W=lasagne.init.GlorotUniform(), b=lasagne.init.Constant(0.), nonlinearity=lasagne.nonlinearities.rectify, **kwargs)[source]¶ Network-in-network layer. Like DenseLayer, but broadcasting across all trailing dimensions beyond the 2nd. This results in a convolution operation with filter size 1 on all trailing dimensions. Any number of trailing dimensions is supported, so NINLayer can be used to implement 1D, 2D, 3D, … convolutions.
Parameters: incoming : a
Layer
instance or a tupleThe layer feeding into this layer, or the expected input shape
num_units : int
The number of units of the layer
untie_biases : bool
If false the network has a single bias vector similar to a dense layer. If true a separate bias vector is used for each trailing dimension beyond the 2nd.
W : Theano shared variable, expression, numpy array or callable
Initial value, expression or initializer for the weights. These should be a matrix with shape
(num_inputs, num_units)
, wherenum_inputs
is the size of the second dimension of the input. Seelasagne.utils.create_param()
for more information.b : Theano shared variable, expression, numpy array, callable or
None
Initial value, expression or initializer for the biases. If set to
None
, the layer will have no biases. Otherwise, biases should be a 1D array with shape(num_units,)
foruntie_biases=False
, and a tensor of shape(num_units, input_shape[2], ..., input_shape[-1])
foruntie_biases=True
. Seelasagne.utils.create_param()
for more information.nonlinearity : callable or None
The nonlinearity that is applied to the layer activations. If None is provided, the layer will be linear.
References
[R38] Lin, Min, Qiang Chen, and Shuicheng Yan (2013): Network in network. arXiv preprint arXiv:1312.4400. Examples
>>> from lasagne.layers import InputLayer, NINLayer >>> l_in = InputLayer((100, 20, 10, 3)) >>> l1 = NINLayer(l_in, num_units=5)
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get_output_for
(input, **kwargs)[source]¶ Propagates the given input through this layer (and only this layer).
Parameters: input : Theano expression
The expression to propagate through this layer.
Returns: output : Theano expression
The output of this layer given the input to this layer.
Notes
This is called by the base
lasagne.layers.get_output()
to propagate data through a network.This method should be overridden when implementing a new
Layer
class. By default it raises NotImplementedError.
-
get_output_shape_for
(input_shape)[source]¶ Computes the output shape of this layer, given an input shape.
Parameters: input_shape : tuple
A tuple representing the shape of the input. The tuple should have as many elements as there are input dimensions, and the elements should be integers or None.
Returns: tuple
A tuple representing the shape of the output of this layer. The tuple has as many elements as there are output dimensions, and the elements are all either integers or None.
Notes
This method will typically be overridden when implementing a new
Layer
class. By default it simply returns the input shape. This means that a layer that does not modify the shape (e.g. because it applies an elementwise operation) does not need to override this method.
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