def ChainLoss(self, input_feats, deriv_weights,
indexs, in_labels, weights, statesinfo, num_states, frames,
label_dim,
den_indexs, den_in_labels, den_weights, den_statesinfo, den_num_states,
den_start_state = 0 ,delete_laststatesuperfinal = True,
l2_regularize = 0.00005, leaky_hmm_coefficient = 0.1, xent_regularize =0.025):
seq_len = None
last_output, rnn_keep_state_op, rnn_state_zero_op = self.CreateModel(
input_feats, seq_len)
# this function deriv_weights from time_major = False change major = True
if self.time_major_cf:
deriv_weights = tf.transpose(deriv_weights)
if self.time_major_cf:
last_output = last_output[-1 * frames[0]:]
else:
# now don't test
last_output = last_output[:][-1 * frames[0]:]
with tf.name_scope('ChainLoss'):
chain_loss = chainloss(last_output, deriv_weights,
indexs, in_labels, weights, statesinfo, num_states,
label_dim,
den_indexs, den_in_labels, den_weights, den_statesinfo, den_num_states,
den_start_state, delete_laststatesuperfinal,
l2_regularize, leaky_hmm_coefficient, xent_regularize,
time_major = self.time_major_cf)
total_frames = 0
chain_mean_loss = chain_loss[0]/chain_loss[2]
return chain_mean_loss, chain_loss, None, rnn_keep_state_op, rnn_state_zero_op
def chainloss_fn(outputs,
deriv_weights,
indexs,
in_labels,
weights,
statesinfo,
num_states,
label_dim,
den_indexs,
den_in_labels,
den_weights,
den_statesinfo,
den_num_states,
den_start_state,
delete_laststatesuperfinal,
l2_regularize,
leaky_hmm_coefficient,
xent_regularize,
time_major=True):
chain_loss = chainloss(outputs,
deriv_weights,
indexs,
in_labels,
weights,
statesinfo,
num_states,
label_dim,
den_indexs,
den_in_labels,
den_weights,
den_statesinfo,
den_num_states,
den_start_state,
delete_laststatesuperfinal,
l2_regularize,
leaky_hmm_coefficient,
xent_regularize,
time_major=True)
return chain_loss
den_fst)
den_indexs = np.reshape(den_indexs, [-1]).tolist()
den_in_labels = np.reshape(den_in_labels, [-1]).tolist()
den_weights = np.reshape(den_weights, [-1]).tolist()
den_statesinfo = np.reshape(den_statesinfo, [-1]).tolist()
chain_loss = chainloss(self_X,
self_Y,
self_indexs,
self_in_labels,
self_weights,
self_statesinfo,
self_num_states,
label_dim,
den_indexs,
den_in_labels,
den_weights,
den_statesinfo,
den_num_states,
den_start_state,
delete_laststatesuperfinal,
l2_regularize,
leaky_hmm_coefficient,
xent_regularize,
time_major=True)
#outputs = np.random.rand(50* 64*3766).reshape(50, 64, 3766)
sess = tf.Session()
while True:
start1 = time.time()