deep_gru_lstm_call_lstm.py

def _call_lstm(self,
                level: int,
                x: Union[np.ndarray, tf.Tensor]) -> tf.Tensor:
    
    n = x.shape[0]
    
    self.a[level] = self.a[level][0:n]
    self.c[level] = self.c[level][0:n]
    
    concat_matrix = tf.concat([self.a[level], x], axis=1)
    
    update_gate = tf.math.sigmoid(
                        tf.linalg.matmul(concat_matrix, self.wu[level])
                        + self.bu[level])
    forget_gate = tf.math.sigmoid(
                        tf.linalg.matmul(concat_matrix, self.wf[level])
                        + self.bf[level])
    output_gate = tf.math.sigmoid(
                        tf.linalg.matmul(concat_matrix, self.wo[level])
                        + self.bo[level])
    
    c_candidate = tf.math.tanh(
                        tf.linalg.matmul(concat_matrix, self.wc[level])
                        + self.bc[level])
    
    self.c[level] = (tf.math.multiply(update_gate, c_candidate) +
                        tf.math.multiply(forget_gate, self.c[level]))
    
    self.a[level] = tf.math.multiply(output_gate, tf.math.tanh(self.c[level]))
    
    return self.a[level]