(self, W_recur, h_prev_s, h_ff_s, h_s, bias,
                                  nout, num_steps, num_used_steps, activation,
                                  reverse=False)
Time step unrolling portion of recurrent layer fprop.

        Arguments:
            W_recur (Tensor): Recurrent weight matrix.
            h_prev_s (Array): Array of per time step hidden state tensors. Each
                element in the array is a single time step view into one tensor
                containing all of the time steps in sequence.
            h_ff_s (Array): Array of per time step hidden state tensors. Each
                element in the array is a single time step view into one tensor
                containing all of the time steps in sequence.
            h_s (Array): Array of per time step hidden state tensors. Each
                element in the array is a single time step view into one tensor
                containing all of the time steps in sequence.
            bias (Tensor): Bias tensor to add at each time step.
            nout (integer): Number of output units for the layer.
            num_steps (integer): Total number of time steps in the buffer.
            num_used_steps (integer): Number of time steps being used for real
                data.
            activation (Transform): Activation function for the layer.
            reverse (boolean): When true, unrolling will iterate over time steps
                in reverse (for BiRNN).
Time step unrolling portion of recurrent layer fprop.
Time step unrolling portion of recurrent layer fprop .
def compound_rnn_unroll_fprop(self, W_recur, h_prev_s, h_ff_s, h_s, bias,
                                  nout, num_steps, num_used_steps, activation,
                                  reverse=False):
        """
        Time step unrolling portion of recurrent layer fprop.

        Arguments:
            W_recur (Tensor): Recurrent weight matrix.
            h_prev_s (Array): Array of per time step hidden state tensors. Each
                element in the array is a single time step view into one tensor
                containing all of the time steps in sequence.
            h_ff_s (Array): Array of per time step hidden state tensors. Each
                element in the array is a single time step view into one tensor
                containing all of the time steps in sequence.
            h_s (Array): Array of per time step hidden state tensors. Each
                element in the array is a single time step view into one tensor
                containing all of the time steps in sequence.
            bias (Tensor): Bias tensor to add at each time step.
            nout (integer): Number of output units for the layer.
            num_steps (integer): Total number of time steps in the buffer.
            num_used_steps (integer): Number of time steps being used for real
                data.
            activation (Transform): Activation function for the layer.
            reverse (boolean): When true, unrolling will iterate over time steps
                in reverse (for BiRNN).
        """
        if num_used_steps is not None and num_used_steps < num_steps:
            h_s = h_s[:num_used_steps]
            h_prev_s = h_prev_s[:num_used_steps]
            h_ff_s = h_ff_s[:num_used_steps]

        if reverse:
            steps = reversed(list(zip(h_s, h_prev_s, h_ff_s)))
        else:
            steps = zip(h_s, h_prev_s, h_ff_s)

        for (h, h_prev, h_ff) in steps:
            if h_ff is h:
                self.compound_dot(W_recur, h_prev, h, beta=1.0)
                h[:] = activation(h + bias)
            else:
                self.compound_dot(W_recur, h_prev, h)
                if not math_cpu.add_and_act(h._tensor, h_ff._tensor, bias._tensor, activation):
                    h[:] = activation(h + h_ff + bias)
def compound_rnn_unroll_fprop ( self , W_recur , h_prev_s , h_ff_s , h_s , bias , nout , num_steps , num_used_steps , activation , reverse = False ) : if num_used_steps is not None and num_used_steps < num_steps : h_s = h_s [ : num_used_steps ] h_prev_s = h_prev_s [ : num_used_steps ] h_ff_s = h_ff_s [ : num_used_steps ] if reverse : steps = reversed ( list ( zip ( h_s , h_prev_s , h_ff_s ) ) ) else : steps = zip ( h_s , h_prev_s , h_ff_s ) for ( h , h_prev , h_ff ) in steps : if h_ff is h : self . compound_dot ( W_recur , h_prev , h , beta = 1.0 ) h [ : ] = activation ( h + bias ) else : self . compound_dot ( W_recur , h_prev , h ) if not math_cpu . add_and_act ( h . _tensor , h_ff . _tensor , bias . _tensor , activation ) : h [ : ] = activation ( h + h_ff + bias )