def __init__(self, n_inputs = 0, n_hidden_units = 0, n_hidden_layers = 0, n_outputs = 0,
                 training_set_x = None, training_set_y = None, prev_frames = 0, next_frames = 0, amplify_center_frame = 1.0,
                 batch_size = 0, hidden_activation = 'tanh', weight_l2 = 1e-6):
        self.n_inputs = n_inputs

        if hidden_activation == 'tanh':
            self.hidden_activation = T.tanh
        elif hidden_activation == 'sigmoid':
            self.hidden_activation = T.nnet.sigmoid
        elif hidden_activation == 'softplus':
            self.hidden_activation = T.nnet.softplus
        elif hidden_activation == 'relu':
            self.hidden_activation = lambda x: T.maximum(0, x)
        else:
            raise NotImplementedError

        self.n_outputs = n_outputs
        self.weight_l2 = weight_l2

        self.training_set_x = training_set_x
        self.training_set_y = training_set_y

        self.prev_frames = prev_frames
        self.next_frames = next_frames
        self.batch_size = batch_size

        amp_min = 1.0/amplify_center_frame
        amp_max = 1.0

        amp_prev = [amp_min + (float(i) / self.prev_frames) * (amp_max - amp_min) for i in range(0, self.prev_frames)]
        amp_next = [amp_min + (float(i) / self.next_frames) * (amp_max - amp_min) for i in range(0, self.next_frames)]
        self.amp = amp_prev + [amp_max,] + list(reversed(amp_next))
        self.amp_vec = np.repeat(self.amp, n_inputs / (self.prev_frames + 1 + self.next_frames))

        if n_inputs:
            self.build_model(n_hidden_units, n_hidden_layers)

        self.rprop_init()
dataset/ETHPy150Open UFAL-DSG/cloud-asr/cloudasr/worker/vad/tffnn.py/TheanoFFNN.__init__/original