Module Import alias Import alias ImportFrom alias ImportFrom alias ImportFrom alias Assign NameStore AttributeLoad AttributeLoad NameLoad attr attr ClassDef bases NameLoad body FunctionDef arguments args NameParam NameParam NameParam defaults body Pass decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Pass decorator_list FunctionDef arguments args NameParam defaults body Expr Str Pass decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Pass decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Pass decorator_list decorator_list
N/A N/A numpy N/A theano theano tensor theano.tensor.nnet sigmoid blocks.utils shared_floatx_zeros N/A floatX N/A N/A theano config floatX NeuralNetwork N/A object N/A __init__ N/A N/A self input_dim n_hidden N/A N/A N/A N/A get_probs N/A N/A self features N/A N/A N/A Output the probability of being a positive.

        Parameters
        ----------
        features : :class:`~tensor.TensorVariable`
            The features that you consider as input.
            Must have shape (batch_size, input_dim).

        Returns
        -------
        probs : :class:`~tensor.TensorVariable`
            The probabilities of each example to belong to
            the positive class. Must have shape (batch_size, 1)
         N/A N/A get_params N/A N/A self N/A N/A N/A Returns the list of parameters of the class.

        Returns
        -------
        params : list
            The list of shared variables that are parameters of the class.
         N/A N/A get_cost N/A N/A self probs targets N/A N/A N/A Output the logistic loss.

        Parameters
        ----------
        probs : :class:`~tensor.TensorVariable`
            The probabilities of each example to belong to
            the positive class. Must have shape (batch_size, 1)
        targets : :class:`~tensor.TensorVariable`
            The indicator on whether the example belongs to the
            positive class. Must have shape (batch_size, 1)

        Returns
        -------
        cost : :class:`~tensor.TensorVariable`
            The corresponding logistic cost.
            .. math:: -targets \log(probs) - (1 - targets) \log(1 - probs)
         N/A N/A get_misclassification N/A N/A self probs targets N/A N/A N/A Output the misclassification error.

        This misclassification is done when classifying an example as
        the most likely class.

        Parameters
        ----------
        probs : :class:`~tensor.TensorVariable`
            The probabilities of each example to belong to
            the positive class. Must have shape (batch_size, 1)
        targets : :class:`~tensor.TensorVariable`
            The indicator on whether the example belongs to the
            positive class. Must have shape (batch_size, 1)

        Returns
        -------
        misclassification : :class:`~tensor.TensorVariable`
            The corresponding misclassification error, if we classify
            an example as the most likely class.
         N/A N/A N/A