Module Import alias Import alias Import alias identifier ClassDef bases NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam defaults body Expr Str Assign NameStore Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr keyword UnaryOpUSub Call AttributeLoad NameLoad attr BinOpDiv Num NameLoad keyword Call AttributeLoad NameLoad attr BinOpDiv Num NameLoad keyword TupleLoad NameLoad NameLoad keyword AttributeLoad AttributeLoad NameLoad attr attr Assign AttributeStore NameLoad attr Call AttributeLoad NameLoad attr keyword NameLoad keyword Str keyword NameLoad Assign AttributeStore NameLoad attr Call AttributeLoad NameLoad attr keyword Call AttributeLoad NameLoad attr TupleLoad NameLoad keyword AttributeLoad AttributeLoad NameLoad attr attr keyword Str keyword NameLoad Assign AttributeStore NameLoad attr Call AttributeLoad AttributeLoad NameLoad attr attr BinOpAdd Call AttributeLoad NameLoad attr NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign AttributeStore NameLoad attr Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr keyword Num Assign AttributeStore NameLoad attr ListLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Return UnaryOpUSub Call AttributeLoad NameLoad attr SubscriptLoad Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr Index TupleLoad Call AttributeLoad NameLoad attr SubscriptLoad AttributeLoad NameLoad attr Index Num NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str If CompareNotEq AttributeLoad NameLoad attr AttributeLoad AttributeLoad NameLoad attr attr body Raise Call NameLoad Str TupleLoad Str AttributeLoad NameLoad attr Str AttributeLoad AttributeLoad NameLoad attr attr If Call AttributeLoad AttributeLoad NameLoad attr attr Str body Return Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr NameLoad orelse Raise Call NameLoad decorator_list decorator_list FunctionDef arguments args defaults body Expr Str Assign NameStore Call AttributeLoad NameLoad attr Str keyword Str Assign NameStore Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr Str keyword Str keyword Num Assign NameStore Call AttributeLoad NameLoad attr Str keyword Str Assign NameStore Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr Str keyword Str keyword Num FunctionDef arguments args NameParam NameParam NameParam defaults NameLoad body Expr Str Assign NameStore Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr NameLoad keyword AttributeLoad AttributeLoad NameLoad attr attr keyword NameLoad Assign NameStore Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr NameLoad keyword AttributeLoad AttributeLoad NameLoad attr attr keyword NameLoad Return TupleLoad NameLoad Call AttributeLoad NameLoad attr NameLoad Str decorator_list Assign TupleStore NameStore NameStore Call NameLoad NameLoad NameLoad Assign TupleStore NameStore NameStore Call NameLoad NameLoad NameLoad Assign NameStore ListLoad TupleLoad NameLoad NameLoad TupleLoad NameLoad NameLoad Return NameLoad decorator_list
N/A N/A numpy N/A theano N/A theano.tensor T LogisticRegression N/A object N/A N/A Output layer with softmax activation __init__ N/A N/A self rng input n_in n_out N/A N/A N/A 
        :param input: symbolic variable that describes the input
         of the architecture (one minibatch)

        :param n_in: number of input units, the dimension of
         the space in which the datapoints lie

        :param n_out: number of output units, the dimension of
         the space in which the labels lie
         N/A W_values N/A N/A numpy asarray N/A N/A rng uniform low N/A N/A N/A numpy sqrt N/A 1.0 n_in high N/A N/A numpy sqrt N/A 1.0 n_in size N/A n_in n_out dtype N/A N/A theano config floatX N/A N/A self W N/A N/A theano shared value W_values name W borrow True N/A N/A self b N/A N/A theano shared value N/A N/A numpy zeros N/A n_out dtype N/A N/A theano config floatX name b borrow True N/A N/A self p_y_given_x N/A N/A N/A T nnet softmax N/A N/A N/A T dot input N/A self W N/A self b N/A N/A self y_pred N/A N/A T argmax N/A self p_y_given_x axis 1 N/A N/A self params N/A N/A self W N/A self b N/A negative_log_likelihood N/A N/A self y N/A N/A N/A 
        Return the mean of the negative log-likelihood of the prediction
        of this model under a given target distribution.

        :type y: theano.tensor.TensorType
        :param y: corresponds to a vector that gives for each example the
                  correct label

        Note: we use the mean instead of the sum so that
              the learning rate is less dependent on the batch size
         N/A N/A N/A N/A T mean N/A N/A N/A T log N/A self p_y_given_x N/A N/A N/A N/A T arange N/A N/A y shape N/A 0 y N/A errors N/A N/A self y N/A N/A N/A Return a float representing the number of errors in the minibatch
        over the total number of examples of the minibatch ; zero one
        loss over the size of the minibatch

        :type y: theano.tensor.TensorType
        :param y: corresponds to a vector that gives for each example the
                  correct label
         N/A N/A N/A y ndim N/A N/A self y_pred ndim N/A N/A N/A TypeError y should have the same shape as self.y_pred N/A y N/A y type y_pred N/A N/A self y_pred type N/A N/A N/A N/A y dtype startswith int N/A N/A N/A N/A T mean N/A N/A T neq N/A self y_pred y N/A N/A N/A NotImplementedError N/A N/A load_data N/A N/A N/A N/A N/A 
    Loads the data
     N/A test_x N/A N/A numpy loadtxt test_images.txt delimiter , N/A test_y N/A N/A numpy argmax N/A N/A numpy loadtxt test_labels.txt delimiter , axis 1 N/A train_x N/A N/A numpy loadtxt train_images.txt delimiter , N/A train_y N/A N/A numpy argmax N/A N/A numpy loadtxt train_labels.txt delimiter , axis 1 shared_dataset N/A N/A data_x data_y borrow N/A True N/A N/A 
        Function that loads the dataset into shared variables

        The reason we store our dataset in shared variables is to allow
        Theano to copy it into the GPU memory (when code is run on GPU).
        Since copying data into the GPU is slow, copying a minibatch everytime
        is needed (the default behaviour if the data is not in a shared
        variable) would lead to a large decrease in performance.
         N/A shared_x N/A N/A theano shared N/A N/A numpy asarray data_x dtype N/A N/A theano config floatX borrow borrow N/A shared_y N/A N/A theano shared N/A N/A numpy asarray data_y dtype N/A N/A theano config floatX borrow borrow N/A N/A shared_x N/A N/A T cast shared_y int32 N/A N/A N/A test_x test_y N/A shared_dataset test_x test_y N/A N/A train_x train_y N/A shared_dataset train_x train_y N/A rval N/A N/A train_x train_y N/A test_x test_y N/A rval N/A