def dis_decoder(hparmas, parser, encoder_outputs, states, sequence, n_output):
def cell_lstm(input_l, state_l):
decoder_lstm1 = LSTM(hparmas.gen_rnn_size,
return_sequences=True,
return_state=True,
dropout=0.5,
recurrent_dropout=0.5)(input_l,
initial_state=state_l)
decoder_outputs, state_h, state_c = LSTM(
hparmas.gen_rnn_size,
return_state=True,
dropout=0.5,
recurrent_dropout=0.5)(decoder_lstm1)
state = [state_h, state_c]
return decoder_outputs, state
# print(initial_state[0])
input = Lambda(spl, arguments={'index': 0})(sequence)
rnn_out, state_gen = cell_lstm(input, states)
# initial_state[0]=Lambda(reshap)(initial_state[0])
(attention_keys, attention_values) = Lambda(pre_attein)(encoder_outputs)
attention_score_fn = attention_utils._create_attention_score_fn(
"attention_keys", hparmas.gen_rnn_size, "luong")
# Attention construction function
attention_construct_fn = attention_utils._create_attention_construct_fn(
"attention_score", hparmas.gen_rnn_size, attention_score_fn)
attention_option = parser.attention_option
def atten(outputs):
return attention_construct_fn(outputs, attention_keys,
attention_values)
rnn_out = Lambda(atten)(rnn_out)
# out=rnn_out
decoder_dense = Dense(n_output, activation='softmax')
decoder_outputs = decoder_dense(rnn_out)
# decoder_outputs = Reshape((-1,1,n_output))(decoder_outputs)
decoder_outputs = Lambda(ex_pen)(decoder_outputs)
out = decoder_outputs
# out = Reshape((-1,n_units))(rnn_out)
for i in range(1, n_steps_out):
input_s = Lambda(spl, arguments={'index': i})(sequence)
rnn_out, state_g = cell_lstm(input_s, state_gen)
rnn_out = Lambda(atten)(rnn_out)
state_gen = state_g
# rnn_out = Reshape((-1,n_units))(rnn_out)
# print(rnn_out)
decoder_outputs = decoder_dense(rnn_out)
# decoder_outputs = Reshape((-1,1,n_output))(decoder_outputs)
decoder_outputs = Lambda(ex_pen)(decoder_outputs)
# decoder_outputs=K.expand_dims(decoder_outputs,1)
out = Concatenate(1)([out, decoder_outputs])
# pri:nt(decoder_outputs)
# decoder_outputs=AttentionDecoder(n_units,n_features,decoder_outputs.shape)(decoder_outputs,initial_state[0])
return out, state_gen
def gen_decoder(self, input, encoder_outputs, states, n_output):
# def init_f(x):
# init_z = initializers.Zeros()
# return
#
# # input=Lambda(init_f)(self.latent_dim)
# init_z = initializers.Zeros()
# inpt=init_z(shape=(1, 1, self.latent_dim))
# inputg=Input(tensor=inpt)
def cell_lstm(input_l, state_l):
decoder_lstm1 = LSTM(self.gen_size,
return_sequences=True,
return_state=True,
dropout=0.5,
recurrent_dropout=0.5)(input_l,
initial_state=state_l)
decoder_output, state_h, state_c = LSTM(
self.gen_size,
return_state=True,
dropout=0.5,
recurrent_dropout=0.5)(decoder_lstm1)
# decoder_dense = Dense(n_output, activation='softmax')
# decoder_outputs = decoder_dense(decoder_output)
state = [state_h, state_c]
return decoder_output, state
# print(initial_state[0])
rnn_out, state_gen = cell_lstm(input, states)
# initial_state[0]=Lambda(reshap)(initial_state[0])
(attention_keys,
attention_values) = Lambda(pre_attein)(encoder_outputs)
attention_score_fn = attention_utils._create_attention_score_fn(
"attention_keys", self.gen_size, "luong")
# Attention construction function
attention_construct_fn = attention_utils._create_attention_construct_fn(
"attention_score", self.gen_size, attention_score_fn)
def categori(x):
logit = x
categorical = tf.distributions.Categorical(logits=logit)
print(logit.shape)
fake = categorical.sample(8)
log_prob = categorical.log_prob(fake)
print("wosjofake", fake)
return ([fake, log_prob])
def atten(outputs):
return attention_construct_fn(outputs, attention_keys,
attention_values)
rnn_out = Lambda(atten)(rnn_out)
if args.gen_train_strategy != 'cross_entory':
# output, log_prob = Lambda(categori)(rnn_out)
# out=rnn_out
decoder_dense = Dense(n_output, activation='sigmoid')
decoder_outputs = decoder_dense(rnn_out)
# decoder_outputs = Reshape((-1,1,n_output))(decoder_outputs)
# decoder_outputs = Lambda(ex_pen)(decoder_outputs)
# out = decoder_outputs
log_prob = Lambda(get_prob)(decoder_outputs)
decoder_outputs = Lambda(ex_pen)(decoder_outputs)
print("woshideout", decoder_outputs)
out = decoder_outputs
log_pro = Lambda(ex_pen)(log_prob)
# out = Reshape((-1,n_units))(rnn_out)
for i in range(0, self.n_steps_out - 1):
rnn_out, state_g = cell_lstm(decoder_outputs, state_gen)
print("woshiout", rnn_out)
# print(rnn_out)
rnn_out = Lambda(atten)(rnn_out)
# output, log_prob = Lambda(categori)(rnn_out)
state_gen = state_g
decoder_dense = Dense(n_output, activation='sigmoid')
decoder_outputs = decoder_dense(rnn_out)
# rnn_out = Reshape((-1,n_units))(rnn_out)
# print(rnn_out)
log_prob = Lambda(get_prob)(decoder_outputs)
decoder_outputs = Lambda(ex_pen)(decoder_outputs)
# decoder_outputs = decoder_dense(rnn_out)
# # decoder_outputs = Reshape((-1,1,n_output))(decoder_outputs)
# decoder_outputs = Lambda(ex_pen)(decoder_outputs)
# decoder_outputs=K.expand_dims(decoder_outputs,1)
log_prob = Lambda(ex_pen)(log_prob)
# decoder_outputs=K.expand_dims(decoder_outputs,1)
out = Concatenate(1)([out, decoder_outputs])
log_pro = Concatenate(1)([log_pro, log_prob])
return out, log_pro
else:
# ?
# decoder_outputs = Reshape((-1,1,n_output))(decoder_outputs)
decoder_outputs = Lambda(ex_pen)(rnn_out)
out = decoder_outputs
# out = Reshape((-1,n_units))(rnn_out)
for i in range(0, self.n_steps_out - 1):
rnn_out, state_g = cell_lstm(decoder_outputs, state_gen)
print("woshiout", rnn_out)
rnn_out = Lambda(atten)(rnn_out)
state_gen = state_g
# rnn_out = Reshape((-1,n_units))(rnn_out)
# print(rnn_out)
# decoder_outputs = decoder_dense(rnn_out)
# decoder_outputs = Reshape((-1,1,n_output))(decoder_outputs)
decoder_outputs = Lambda(ex_pen)(rnn_out)
# decoder_outputs=K.expand_dims(decoder_outputs,1)
out = Concatenate(1)([out, decoder_outputs])
# def init_f(ne):
#
# init_z = initializers.Zeros()
# y=init_z(self.batch_size,self.n_steps_out,ne)
# return y
def get_zero():
d = np.zeros((self.batch_size, self.n_steps_out, self.n_output),
dtype="float32")
return d
log_pro = Lambda(get_zero)
# print("woshiout",out)
return out, log_pro
