Mercurial > pylearn
changeset 1382:00116be92710
First try to use numpy documentation syntax.
| author | Frederic Bastien <nouiz@nouiz.org> |
|---|---|
| date | Wed, 08 Dec 2010 14:30:13 -0500 |
| parents | 0673e6af650a |
| children | 0de66ab23dcc |
| files | doc/conf.py pylearn/formulas/activations.py |
| diffstat | 2 files changed, 117 insertions(+), 55 deletions(-) [+] |
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--- a/doc/conf.py Mon Dec 06 13:33:07 2010 -0500 +++ b/doc/conf.py Wed Dec 08 14:30:13 2010 -0500 @@ -33,6 +33,11 @@ except ImportError: pass +try: + import numpydoc + extensions.append('numpydoc') +except ImportError: + pass # Add any paths that contain templates here, relative to this directory. templates_path = ['.templates']
--- a/pylearn/formulas/activations.py Mon Dec 06 13:33:07 2010 -0500 +++ b/pylearn/formulas/activations.py Wed Dec 08 14:30:13 2010 -0500 @@ -24,6 +24,7 @@ function of the input x. .. math:: + \\textrm{sigmoid}(x) = \\frac{1}{1 + e^x} The image of :math:`\\textrm{sigmoid}(x)` is the open interval (0, @@ -31,13 +32,18 @@ point representations, :math:`\\textrm{sigmoid}(x)` will lie in the closed range [0, 1]. - :param x: tensor-like (a Theano variable with type theano.Tensor, - or a value that can be converted to one) :math:`\in - \mathbb{R}^n` + Parameters + ---------- + x : tensor-like + A Theano variable with type theano.Tensor, or a value that can be + converted to one :math:`\in \mathbb{R}^n` - :return: a Theano variable with the same shape as the input, where - the sigmoid function is mapped to each element of the - input x. + Returns + ------- + ret : a Theano variable with the same shape as the input + where the sigmoid function is mapped to each element of the + input `x`. + """ return theano.tensor.nnet.sigmoid(x) @@ -52,6 +58,7 @@ tangent) of the input x. .. math:: + \\textrm{tanh}(x) = \\frac{e^{2x} - 1}{e^{2x} + 1} The image of :math:`\\textrm{tanh}(x)` is the open interval (-1, @@ -59,13 +66,16 @@ point representations, :math:`\\textrm{tanh}(x)` will lie in the closed range [-1, 1]. - :param x: tensor-like (a Theano variable with type theano.Tensor, - or a value that can be converted to one) :math:`\in - \mathbb{R}^n` + Parameters + ---------- + x : tensor-like + A Theano variable with type theano.Tensor, or a value that can be + converted to one :math:`\in \mathbb{R}^n` - :return: a Theano variable with the same shape as the input, where - the tanh function is mapped to each element of the input - x. + Returns + ------- + ret : a Theano variable with the same shape as the input + where the tanh function is mapped to each element of the input `x`. """ return theano.tensor.tanh(x) @@ -81,6 +91,7 @@ TODO: where does 1.759 come from? why is it normalized like that? .. math:: + \\textrm{tanh\_normalized}(x) = 1.759\\textrm{ tanh}\left(\\frac{2x}{3}\\right) The image of :math:`\\textrm{tanh\_normalized}(x)` is the open @@ -90,13 +101,17 @@ closed range [-1.759, 1.759]. The exact bound depends on the precision of the floating point representation. - :param x: tensor-like (a Theano variable with type theano.Tensor, - or a value that can be converted to one) :math:`\in - \mathbb{R}^n` + Parameters + ---------- + x : tensor-like + A Theano variable with type theano.Tensor, or a value that can be + converted to one :math:`\in \mathbb{R}^n` - :return: a Theano variable with the same shape as the input, where - the tanh\_normalized function is mapped to each element of - the input x. + Returns + ------- + ret : a Theano variable with the same shape as the input + where the tanh_normalized function is mapped to each element of + the input `x`. """ return 1.759*theano.tensor.tanh(0.6666*x) @@ -111,6 +126,7 @@ hyperbolic tangent of x. .. math:: + \\textrm{abs\_tanh}(x) = |\\textrm{tanh}(x)| The image of :math:`\\textrm{abs\_tanh}(x)` is the interval [0, 1), @@ -118,13 +134,17 @@ point representations, :math:`\\textrm{abs\_tanh}(x)` will lie in the range [0, 1]. - :param x: tensor-like (a Theano variable with type theano.Tensor, - or a value that can be converted to one) :math:`\in - \mathbb{R}^n` + Parameters + ---------- + x : tensor-like + A Theano variable with type theano.Tensor, or a value that can be + converted to one :math:`\in \mathbb{R}^n` - :return: a Theano variable with the same shape as the input, where - the abs_tanh function is mapped to each element of the - input x. + Returns + ------- + ret : a Theano variable with the same shape as the input + where the abs_tanh function is mapped to each element of + the input `x`. """ return theano.tensor.abs_(theano.tensor.tanh(x)) @@ -140,6 +160,7 @@ TODO: where does 1.759 come from? why is it normalized like that? .. math:: + \\textrm{abs\_tanh\_normalized}(x) = \left|1.759\\textrm{ tanh}\left(\\frac{2x}{3}\\right)\\right| The image of :math:`\\textrm{abs\_tanh\_normalized}(x)` is the range @@ -149,13 +170,17 @@ approximative closed range [0, 1.759]. The exact upper bound depends on the precision of the floating point representation. - :param x: tensor-like (a Theano variable with type theano.Tensor, - or a value that can be converted to one) :math:`\in - \mathbb{R}^n` + Parameters + ---------- + x: tensor-like + A Theano variable with type theano.Tensor, or a value that can be + converted to one :math:`\in \mathbb{R}^n` - :return: a Theano variable with the same shape as the input, where - the abs_tanh_normalized function is mapped to each - element of the input x. + Returns + ------- + ret: a Theano variable with the same shape as the input + where the abs_tanh_normalized function is mapped to each + element of the input `x`. """ return theano.tensor.abs_(1.759*theano.tensor.tanh(0.6666*x)) @@ -167,13 +192,20 @@ Returns a symbolic variable that computes the softsign of ``input``. .. math:: + f(input) = \\frac{input}{1.0 + |input|} - :type input: tensor-like - :param input: input tensor to which softsign should be applied - :rtype: Theano variable - :return: tensor obtained after applying the softsign function + Parameters + ---------- + input : tensor-like + A Theano variable with type theano.Tensor, or a value that can be + converted to one :math:`\in \mathbb{R}^n` + Returns + ------- + ret : a Theano variable with the same shape as the input + where the softsign function is mapped to each + element of the input `x`. """ return input/(1.0 + tensor.abs_(input)) @@ -186,11 +218,17 @@ .. math:: f(input) = \left| \\frac{input}{1.0 +|input|} \\right| - :type input: tensor-like - :param input: input tensor to which softsign should be applied - :rtype: Tensor variable - :return: tensor obtained by taking the absolute value of softsign - of the input + Parameters + ---------- + input : tensor-like + A Theano variable with type theano.Tensor, or a value that can be + converted to one :math:`\in \mathbb{R}^n` + + Returns + ------- + ret : a Theano variable with the same shape as the input + where the absolute value of the softsign function is mapped to each + element of the input `x`. """ return tensor.abs_(input)/(1.0 + tensor.abs_(input)) @@ -202,19 +240,24 @@ and only if it is positive, 0 otherwise. .. math:: + f(input) = \left \lbrace \\begin{array}{l} input \quad \\text{ if } input > 0 \\ 0 \quad \\text{ else } \end{array} \\right \} - :type input: tensor-like - :param input: input tensor to which the rectifier activation function - will be applied - :rtype: Tensor variable - :return: always positive tensor which equals with the input if it is also - positive or to 0 otherwise + Parameters + ---------- + input : tensor-like + A Theano variable with type theano.Tensor, or a value that can be + converted to one :math:`\in \mathbb{R}^n` + Returns + ------- + ret : a Theano variable with the same shape as the input + A tensor always positive whose element equals the inputs if it is also + positive or to 0 otherwise """ return input*(input>=0) @@ -226,12 +269,20 @@ at initialization. .. math:: + f(input) = ln \left( 1 + e^{input} \\right) - :type input: tensor-like - :param input: input tensor to which the softplus should be applied - :rtype: Theano variable - :return: tensor obtained by applying softsign on the input + Parameters + ---------- + input : tensor-like + A Theano variable with type theano.Tensor, or a value that can be + converted to one :math:`\in \mathbb{R}^n` + + Returns + ------- + ret : a Theano variable with the same shape as the input + where the softsign function is mapped to each + element of the input `x`. """ return tensor.nnet.softplus(input) @@ -242,15 +293,21 @@ ``input``. .. math:: + f(input) = |input| - :type input: tensor-like - :param input: input tensor - :rtype: Theano variable - :return: tensor that represents the absolute value of the input + Parameters + ---------- + input : tensor-like + A Theano variable with type theano.Tensor, or a value that can be + converted to one :math:`\in \mathbb{R}^n` - - """ + Returns + ------- + ret : a Theano variable with the same shape as the input + where the absolute function is mapped to each + element of the input `x`. + """ return theano.tensor.abs_(input)