lasagne.init¶
Functions to create initializers for parameter variables.
Examples¶
>>> from lasagne.layers import DenseLayer
>>> from lasagne.init import Constant, GlorotUniform
>>> l1 = DenseLayer((100,20), num_units=50,
... W=GlorotUniform('relu'), b=Constant(0.0))
Initializers¶
Constant([val]) | Initialize weights with constant value. |
Normal([std, mean]) | Sample initial weights from the Gaussian distribution. |
Uniform([range, std, mean]) | Sample initial weights from the uniform distribution. |
Glorot(initializer[, gain, c01b]) | Glorot weight initialization. |
GlorotNormal([gain, c01b]) | Glorot with weights sampled from the Normal distribution. |
GlorotUniform([gain, c01b]) | Glorot with weights sampled from the Uniform distribution. |
He(initializer[, gain, c01b]) | He weight initialization. |
HeNormal([gain, c01b]) | He initializer with weights sampled from the Normal distribution. |
HeUniform([gain, c01b]) | He initializer with weights sampled from the Uniform distribution. |
Orthogonal([gain]) | Intialize weights as Orthogonal matrix. |
Sparse([sparsity, std]) | Initialize weights as sparse matrix. |
Detailed description¶
- class lasagne.init.Initializer[source]¶
Base class for parameter tensor initializers.
The Initializer class represents a weight initializer used to initialize weight parameters in a neural network layer. It should be subclassed when implementing new types of weight initializers.
- class lasagne.init.Constant(val=0.0)[source]¶
Initialize weights with constant value.
Parameters: val : float
Constant value for weights.
- class lasagne.init.Normal(std=0.01, mean=0.0)[source]¶
Sample initial weights from the Gaussian distribution.
Initial weight parameters are sampled from N(mean, std).
Parameters: std : float
Std of initial parameters.
mean : float
Mean of initial parameters.
- class lasagne.init.Uniform(range=0.01, std=None, mean=0.0)[source]¶
Sample initial weights from the uniform distribution.
Parameters are sampled from U(a, b).
Parameters: range : float or tuple
When std is None then range determines a, b. If range is a float the weights are sampled from U(-range, range). If range is a tuple the weights are sampled from U(range[0], range[1]).
std : float or None
If std is a float then the weights are sampled from U(mean - np.sqrt(3) * std, mean + np.sqrt(3) * std).
mean : float
see std for description.
- class lasagne.init.Glorot(initializer, gain=1.0, c01b=False)[source]¶
Glorot weight initialization.
This is also known as Xavier initialization [R77].
Parameters: initializer : lasagne.init.Initializer
Initializer used to sample the weights, must accept std in its constructor to sample from a distribution with a given standard deviation.
gain : float or ‘relu’
Scaling factor for the weights. Set this to 1.0 for linear and sigmoid units, to ‘relu’ or sqrt(2) for rectified linear units, and to sqrt(2/(1+alpha**2)) for leaky rectified linear units with leakiness alpha. Other transfer functions may need different factors.
c01b : bool
For a lasagne.layers.cuda_convnet.Conv2DCCLayer constructed with dimshuffle=False, c01b must be set to True to compute the correct fan-in and fan-out.
See also
- GlorotNormal
- Shortcut with Gaussian initializer.
- GlorotUniform
- Shortcut with uniform initializer.
Notes
For a DenseLayer, if gain='relu' and initializer=Uniform, the weights are initialized as
\[\begin{split}a &= \sqrt{\frac{12}{fan_{in}+fan_{out}}}\\ W &\sim U[-a, a]\end{split}\]If gain=1 and initializer=Normal, the weights are initialized as
\[\begin{split}\sigma &= \sqrt{\frac{2}{fan_{in}+fan_{out}}}\\ W &\sim N(0, \sigma)\end{split}\]References
[R77] (1, 2) Xavier Glorot and Yoshua Bengio (2010): Understanding the difficulty of training deep feedforward neural networks. International conference on artificial intelligence and statistics.
- class lasagne.init.GlorotNormal(gain=1.0, c01b=False)[source]¶
Glorot with weights sampled from the Normal distribution.
See Glorot for a description of the parameters.
- class lasagne.init.GlorotUniform(gain=1.0, c01b=False)[source]¶
Glorot with weights sampled from the Uniform distribution.
See Glorot for a description of the parameters.
- class lasagne.init.He(initializer, gain=1.0, c01b=False)[source]¶
He weight initialization.
Weights are initialized with a standard deviation of \(\sigma = gain \sqrt{\frac{1}{fan_{in}}}\) [R99].
Parameters: initializer : lasagne.init.Initializer
Initializer used to sample the weights, must accept std in its constructor to sample from a distribution with a given standard deviation.
gain : float or ‘relu’
Scaling factor for the weights. Set this to 1.0 for linear and sigmoid units, to ‘relu’ or sqrt(2) for rectified linear units, and to sqrt(2/(1+alpha**2)) for leaky rectified linear units with leakiness alpha. Other transfer functions may need different factors.
c01b : bool
For a lasagne.layers.cuda_convnet.Conv2DCCLayer constructed with dimshuffle=False, c01b must be set to True to compute the correct fan-in and fan-out.
References
[R99] (1, 2) Kaiming He et al. (2015): Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. arXiv preprint arXiv:1502.01852.
- class lasagne.init.HeNormal(gain=1.0, c01b=False)[source]¶
He initializer with weights sampled from the Normal distribution.
See He for a description of the parameters.
- class lasagne.init.HeUniform(gain=1.0, c01b=False)[source]¶
He initializer with weights sampled from the Uniform distribution.
See He for a description of the parameters.
- class lasagne.init.Orthogonal(gain=1.0)[source]¶
Intialize weights as Orthogonal matrix.
Orthogonal matrix initialization [R1111]. For n-dimensional shapes where n > 2, the n-1 trailing axes are flattened. For convolutional layers, this corresponds to the fan-in, so this makes the initialization usable for both dense and convolutional layers.
Parameters: gain : float or ‘relu’
Scaling factor for the weights. Set this to 1.0 for linear and sigmoid units, to ‘relu’ or sqrt(2) for rectified linear units, and to sqrt(2/(1+alpha**2)) for leaky rectified linear units with leakiness alpha. Other transfer functions may need different factors.
References
[R1111] (1, 2) Saxe, Andrew M., James L. McClelland, and Surya Ganguli. “Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.” arXiv preprint arXiv:1312.6120 (2013).