def step(x_t, h1_tm1, c1_tm1, h1_q_tm1, c1_q_tm1):
output, s = LSTMCell([x_t, h1_q_tm1], [n_hid, n_hid],
h1_tm1,
c1_tm1,
n_hid,
random_state=random_state,
name="rnn1",
init=rnn_init)
h1_t = s[0]
c1_t = s[1]
output, s = LSTMCell([h1_t], [n_hid],
h1_q_tm1,
c1_q_tm1,
n_hid,
random_state=random_state,
name="rnn1_q",
init=rnn_init)
h1_cq_t = s[0]
c1_q_t = s[1]
h1_q_t, h1_i_t, h1_nst_q_t, h1_emb = VqEmbedding(
h1_cq_t, n_hid, n_emb, random_state=random_state, name="h1_vq_emb")
output_q_t, output_i_t, output_nst_q_t, output_emb = VqEmbedding(
output, n_hid, n_emb, random_state=random_state, name="out_vq_emb")
# not great
h1_i_t = tf.cast(h1_i_t, tf.float32)
output_i_t = tf.cast(h1_i_t, tf.float32)
lf_output = Bilinear(h1_q_t,
n_hid,
output_emb,
n_hid,
random_state=random_state,
name="out_mix",
init=forward_init)
rf_output = Bilinear(output_q_t,
n_hid,
h1_emb,
n_hid,
random_state=random_state,
name="h_mix",
init=forward_init)
f_output = Linear([lf_output, rf_output], [n_emb, n_emb],
n_hid,
random_state=random_state,
name="out_f",
init=forward_init)
# r[0]
rets = [f_output]
# r[1:3]
rets += [h1_t, c1_t]
# r[3:9]
rets += [h1_q_t, c1_q_t, h1_nst_q_t, h1_cq_t, h1_i_t, h1_emb]
# r[9:]
rets += [output_q_t, output_nst_q_t, output, output_i_t, output_emb]
return rets
def step(x_t, h1_tm1, c1_tm1, h1_q_tm1, c1_q_tm1):
output, s = LSTMCell([x_t], [in_emb],
h1_tm1,
c1_tm1,
n_hid,
random_state=random_state,
cell_dropout=cell_dropout,
name="rnn1",
init=rnn_init)
h1_t = s[0]
c1_t = s[1]
output, s = LSTMCell([h1_t], [n_hid],
h1_q_tm1,
c1_q_tm1,
n_hid,
random_state=random_state,
cell_dropout=cell_dropout,
name="rnn1_q",
init=rnn_init)
h1_cq_t = s[0]
c1_q_t = s[1]
h1_q_t, h1_i_t, h1_nst_q_t, h1_emb = VqEmbedding(
h1_cq_t, n_hid, n_emb, random_state=random_state, name="h1_vq_emb")
# not great
h1_i_t = tf.cast(h1_i_t, tf.float32)
return output, h1_t, c1_t, h1_q_t, c1_q_t, h1_nst_q_t, h1_cq_t, h1_i_t
def step(inp_t, inp_mask_t, corr_inp_t, att_w_tm1, att_k_tm1,
att_h_tm1, att_c_tm1, h1_tm1, c1_tm1, h2_tm1, c2_tm1):
o = GaussianAttentionCell(
[corr_inp_t],
[prenet_units],
(att_h_tm1, att_c_tm1),
att_k_tm1,
bitext,
2 * enc_units,
dec_units,
att_w_tm1,
input_mask=inp_mask_t,
conditioning_mask=text_mask,
#attention_scale=1. / 10.,
attention_scale=1.,
step_op="softplus",
name="att",
random_state=random_state,
cell_dropout=1., #cell_dropout,
init=rnn_init)
att_w_t, att_k_t, att_phi_t, s = o
att_h_t = s[0]
att_c_t = s[1]
output, s = LSTMCell([corr_inp_t, att_w_t, att_h_t],
[prenet_units, 2 * enc_units, dec_units],
h1_tm1,
c1_tm1,
dec_units,
input_mask=inp_mask_t,
random_state=random_state,
cell_dropout=cell_dropout,
name="rnn1",
init=rnn_init)
h1_t = s[0]
c1_t = s[1]
output, s = LSTMCell([corr_inp_t, att_w_t, h1_t],
[prenet_units, 2 * enc_units, dec_units],
h2_tm1,
c2_tm1,
dec_units,
input_mask=inp_mask_t,
random_state=random_state,
cell_dropout=cell_dropout,
name="rnn2",
init=rnn_init)
h2_t = s[0]
c2_t = s[1]
return output, att_w_t, att_k_t, att_phi_t, att_h_t, att_c_t, h1_t, c1_t, h2_t, c2_t
def step(inp_t, inp_mask_t, att_w_tm1, att_k_tm1, att_h_tm1,
att_c_tm1, h1_tm1, c1_tm1, h2_tm1, c2_tm1):
o = GaussianAttentionCell([inp_t], [speech_size],
(att_h_tm1, att_c_tm1),
att_k_tm1,
sequence,
num_letters,
num_units,
att_w_tm1,
input_mask=inp_mask_t,
conditioning_mask=sequence_mask,
attention_scale=1. / 10.,
name="att",
random_state=random_state,
cell_dropout=cell_dropout,
init=rnn_init)
att_w_t, att_k_t, att_phi_t, s = o
att_h_t = s[0]
att_c_t = s[1]
output, s = LSTMCell([inp_t, att_w_t, att_h_t],
[speech_size, num_letters, num_units],
h1_tm1,
c1_tm1,
num_units,
input_mask=inp_mask_t,
random_state=random_state,
cell_dropout=cell_dropout,
name="rnn1",
init=rnn_init)
h1_t = s[0]
c1_t = s[1]
output, s = LSTMCell([inp_t, att_w_t, h1_t],
[speech_size, num_letters, num_units],
h2_tm1,
c2_tm1,
num_units,
input_mask=inp_mask_t,
random_state=random_state,
cell_dropout=cell_dropout,
name="rnn2",
init=rnn_init)
h2_t = s[0]
c2_t = s[1]
return output, att_w_t, att_k_t, att_phi_t, att_h_t, att_c_t, h1_t, c1_t, h2_t, c2_t
def step(x_t, h1_tm1, c1_tm1):
output, s = LSTMCell([x_t], [1],
h1_tm1,
c1_tm1,
n_hid,
random_state=random_state,
name="rnn1",
init=rnn_init)
h1_t = s[0]
c1_t = s[1]
return output, h1_t, c1_t
