It seems in running forward backward algorithm there are errors:
`AttributeError Traceback (most recent call last)
in ()
2 model = HiddenMarkovModel_FB(trans, emi, p0)
3
----> 4 results = model.run_forward_backward(obs_seq)
5 result_list = ["Forward", "Backward", "Posterior"]
6
/Users/xxx/Desktop/hmm/forward_bakward.pyc in run_forward_backward(self, obs_seq)
207 with tf.Session() as sess:
208
--> 209 forward, backward, posterior = self.forward_backward(obs_seq)
210 sess.run(tf.initialize_all_variables())
211 return sess.run([forward, backward, posterior])
/Users/xxx/Desktop/hmm/forward_bakward.pyc in forward_backward(self, obs_seq)
188
189 # forward belief propagation
--> 190 self._forward(obs_prob_seq)
191
192 # backward belief propagation
/Users/xxx/Desktop/hmm/forward_bakward.pyc in _forward(self, obs_prob_seq)
119 prior_prob = tf.matmul(prev_prob, self.T)
120 # forward belief propagation
--> 121 forward_score = tf.mul(prior_prob, tf.cast(obs_prob_seq[step, :], tf.float64))
122 # Normalize score into a probability
123 forward_prob = tf.reshape(forward_score / tf.reduce_sum(forward_score), [-1])
AttributeError: 'module' object has no attribute 'mul'`
It seems in running forward backward algorithm there are errors:
`AttributeError Traceback (most recent call last)
in ()
2 model = HiddenMarkovModel_FB(trans, emi, p0)
3
----> 4 results = model.run_forward_backward(obs_seq)
5 result_list = ["Forward", "Backward", "Posterior"]
6
/Users/xxx/Desktop/hmm/forward_bakward.pyc in run_forward_backward(self, obs_seq)
207 with tf.Session() as sess:
208
--> 209 forward, backward, posterior = self.forward_backward(obs_seq)
210 sess.run(tf.initialize_all_variables())
211 return sess.run([forward, backward, posterior])
/Users/xxx/Desktop/hmm/forward_bakward.pyc in forward_backward(self, obs_seq)
188
189 # forward belief propagation
--> 190 self._forward(obs_prob_seq)
191
192 # backward belief propagation
/Users/xxx/Desktop/hmm/forward_bakward.pyc in _forward(self, obs_prob_seq)
119 prior_prob = tf.matmul(prev_prob, self.T)
120 # forward belief propagation
--> 121 forward_score = tf.mul(prior_prob, tf.cast(obs_prob_seq[step, :], tf.float64))
122 # Normalize score into a probability
123 forward_prob = tf.reshape(forward_score / tf.reduce_sum(forward_score), [-1])
AttributeError: 'module' object has no attribute 'mul'`