# Stuff for critic (will be done several times per training iteration)
Loss_w_grad_ = -(
loss_w_ - gamma * loss_grad_
) # We take the negative of this since the critic wants to maxize it.
log.warning(
"WARNING: Last bias in critic is not updated."
) # It's current value is %f", critic_nn.get_parameter_values()[-1])
params_to_update_critic = critic_nn.get_upd_parameters(
)[:
-1] # We excluded the last bias (scalar). It is intialized to 0 so this way it is effectively not used.
log.info("Parameters to update in critic inner training loop:")
print params_to_update_critic
grads_w_, vars_w_ = zip(
*optim.compute_gradients(loss=Loss_w_grad_,
var_list=params_to_update_critic,
gate_gradients=optim.GATE_GRAPH))
def get_w_input(X1, C1):
inputs_ = {X1_p: X1, C1_p: np.array([]), is_test_p: False}
outputs_ = embd_A_
return sess.run(outputs_, inputs_)
min_op_w = optim.apply_gradients(zip(grads_w_, vars_w_)) ### M1 needed ??
tr_outputs_w_ = [loss_w_, loss_grad_, Loss_w_grad_, min_op_w]
def train_function_w(M1, h1, lr):
tr_inputs_w_ = {embd_A_: M1, h1_p: h1, lr_p: lr, is_test_p: False}
return sess.run(tr_outputs_w_, tr_inputs_w_)
# Stuff for training "generator" and speaker classifier network
loss_c_ = loss_c_ / np.log(
n_spk) # Normalize with the loss of random predictions
loss_reco_ = tf.losses.mean_squared_error(labels=reco_tgt_,
predictions=reco_pred_)
# Stuff for training "generator", "speaker classifiers" and feature/bottleneck predictors
params_to_update_ = model.get_upd_parameters(
) + tdnn_fe_pred.get_upd_parameters() + bnorm_fe_pred.get_upd_parameters()
params_to_update_ += [P_GB_] + [mu_GB_] + bnorm_GB.get_upd_parameters()
Loss_1_ = class_weight * loss_c_ + mse_weight * loss_reco_
Loss_ = Loss_1_ + loss_GB_c_
grads_, vars_ = zip(
*optim.compute_gradients(loss=Loss_,
var_list=params_to_update_,
gate_gradients=optim.GATE_GRAPH))
min_op_ = optim.apply_gradients(zip(grads_, vars_))
tr_outputs_ = [loss_c_, loss_reco_, Loss_1_, min_op_]
tr_outputs_ += [loss_GB_c_]
log.info("Parameters to update:")
print params_to_update_
def train_function(X1, C1, WGT_c, L_c, X2_r, P2, lr):
if not annoying_train:
tr_inputs_ = {
X1_p: X1,
C1_p: C1,
WGT_c_p: WGT_c,
L_c_p: L_c,
log.info("Resetting it_tr_que") # Actually this will not be sufficient to avoid assertion error beacuae batch number is
it_tr_que.batch_number=0 # already in the prepared batches. However, below I reinitialize it_tr_que. This mess
# should be solved.
#############################################################################
### --- Define loss, train functions etc. ------------------------------- ###
#############################################################################
loss_ = tf.tensordot(WGT_m_p, tf.nn.sparse_softmax_cross_entropy_with_logits( labels = L_m_p, logits = C_m_ ), axes=[[0], [0]] )
loss_ = loss_ / np.log(n_spk) # Normalize with the loss of random predictions
loss_ += model.get_l2_reg(l2_reg_b_pool, l2_reg_a_pool, l2_reg_multi)
params_to_update_ = model.get_upd_parameters()
print(params_to_update_)
grads_, vars_ = list(zip(*optim.compute_gradients(loss = loss_, var_list = params_to_update_, gate_gradients=optim.GATE_GRAPH)))
info_ = [ ] # For debugging something can be added here (e.g. reg_loss, grads) will be printe each epoch
# Apply Kaldi style gradient repairing
# TODO: This is hardwired in some other scripts but not added here yet.
# So far (2018-07-19) this has not improved performace though.
min_op = optim.apply_gradients(list(zip(grads_,vars_)))
tr_outputs_ = [info_, loss_, min_op]
def train_function(X1, C1, WGT_m, L_m, lr):
if not annoying_train:
tr_inputs_ = {X1_p: X1, C1_p: C1, WGT_m_p: WGT_m, L_m_p: L_m, lr_p: lr, is_test_p: False}
else: