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:
tr_inputs_ = {
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,
X2_r_p: X2_r,
P2_p: P2,
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
params_to_update_g_c_ = model.get_upd_parameters()
Loss_w_c_ = loss_c_ + delta * loss_w_
grads_g_c_, vars_g_c_ = zip(
*optim.compute_gradients(loss=Loss_w_c_,
var_list=params_to_update_g_c_,
gate_gradients=optim.GATE_GRAPH))
min_op_g_c_ = optim.apply_gradients(zip(grads_g_c_, vars_g_c_))