class PGN(tf.keras.Model):
def __init__(self, params):
super(PGN, self).__init__()
word_model_path = os.path.join(os.path.abspath('../'), 'data',
'w2v.model')
vocab_path = os.path.join(os.path.abspath('../'), 'data',
'words_frequences.txt')
self.params = params
self.matrix = get_embedding(vocab_path, word_model_path, params)
self.encoder = Encoder(params["vocab_size"], params["embed_size"],
self.matrix, params["enc_units"],
params["batch_size"])
self.attention = BahdanauAttention(params["attn_units"])
self.decoder = Decoder(params["vocab_size"], params["embed_size"],
self.matrix, params["dec_units"],
params["batch_size"])
self.pointer = Pointer()
def call_encoder(self, enc_inp):
enc_hidden = self.encoder.initialize_hidden_state()
enc_output, enc_hidden = self.encoder(enc_inp, enc_hidden)
return enc_hidden, enc_output
def call(self, enc_output, dec_hidden, enc_inp, enc_extended_inp, dec_inp,
batch_oov_len):
predictions = []
attentions = []
p_gens = []
context_vector, _ = self.attention(dec_hidden, enc_output)
for t in range(dec_inp.shape[1]):
dec_x, pred, dec_hidden = self.decoder(
tf.expand_dims(dec_inp[:, t], 1), dec_hidden, enc_output,
context_vector)
context_vector, attn = self.attention(dec_hidden, enc_output)
p_gen = self.pointer(context_vector, dec_hidden,
tf.squeeze(dec_x, axis=1))
predictions.append(pred)
attentions.append(attn)
p_gens.append(p_gen)
final_dists = _calc_final_dist(enc_extended_inp, predictions,
attentions, p_gens, batch_oov_len,
self.params["vocab_size"],
self.params["batch_size"])
if self.params["mode"] == "train":
return tf.stack(final_dists, 1), dec_hidden
# predictions_shape = (batch_size, dec_len, vocab_size) with dec_len = 1 in pred mode
else:
return tf.stack(final_dists,
1), dec_hidden, context_vector, tf.stack(
attentions, 1), tf.stack(p_gens, 1)
class PGN(tf.keras.Model):
def __init__(self, params):
super(PGN, self).__init__()
self.params = params
self.encoder = Encoder(params["vocab_size"], params["embed_size"],
params["enc_units"], params["batch_size"])
self.attention = LuongAttention(params["attn_units"])
self.decoder = Decoder(params["vocab_size"], params["embed_size"],
params["dec_units"], params["batch_size"])
self.pointer = Pointer()
def call_encoder(self, enc_inp):
enc_hidden = self.encoder.initialize_hidden_state()
#enc_output, enc_hidden = self.encoder(enc_inp, enc_hidden)
enc_output, enc_hidden, enc_state = self.encoder(enc_inp, enc_hidden)
return enc_hidden, enc_state, enc_output ## Changes done
def call(self, enc_output, dec_hidden, enc_state, enc_inp,
enc_extended_inp, dec_inp, batch_oov_len):
predictions = []
attentions = []
p_gens = []
#print('we wil call attention now')
context_vector, _ = self.attention(dec_hidden, enc_output)
for t in range(dec_inp.shape[1]):
#print('Ok here we are 1')
dec_x, pred, dec_hidden, context_vector, attn = self.decoder(
tf.expand_dims(dec_inp[:, t], 1), [dec_hidden, enc_state],
enc_output, context_vector) #Changes
context_vector1, attn1 = self.attention(dec_hidden, enc_output)
p_gen = self.pointer(context_vector, dec_hidden,
tf.squeeze(dec_x, axis=1))
predictions.append(pred)
attentions.append(attn)
p_gens.append(p_gen)
final_dists = _calc_final_dist(enc_extended_inp, predictions,
attentions, p_gens, batch_oov_len,
self.params["vocab_size"],
self.params["batch_size"])
if self.params["mode"] == "train":
return tf.stack(
final_dists, 1
), dec_hidden # predictions_shape = (batch_size, dec_len, vocab_size) with dec_len = 1 in pred mode
else:
return tf.stack(final_dists,
1), dec_hidden, context_vector, tf.stack(
attentions, 1), tf.stack(p_gens, 1)
class PGN(tf.keras.Model):
def __init__(self, params):
super(PGN, self).__init__()
self.params = params
self.encoder = Encoder(params["vocab_size"], params["embed_size"],
params["enc_units"], params["batch_size"])
self.attention = BahdanauAttention(params["attn_units"])
self.decoder = Decoder(params["vocab_size"], params["embed_size"],
params["dec_units"], params["batch_size"])
self.pointer = Pointer()
def call_encoder(self, enc_inp):
enc_hidden = self.encoder.initialize_hidden_state()
enc_output, enc_hidden = self.encoder(enc_inp, enc_hidden)
return enc_hidden, enc_output
def call(self, enc_output, dec_hidden, enc_inp, enc_extended_inp, dec_inp,
batch_oov_len):
predictions = []
attentions = []
p_gens = []
context_vector, _ = self.attention(dec_hidden, enc_output)
for t in range(dec_inp.shape[1]):
dec_x, pred, dec_hidden = self.decoder(
tf.expand_dims(dec_inp[:, t], 1), dec_hidden, enc_output,
context_vector)
context_vector, attn = self.attention(dec_hidden, enc_output)
p_gen = self.pointer(context_vector, dec_hidden,
tf.squeeze(dec_x, axis=1))
predictions.append(pred)
attentions.append(attn)
p_gens.append(p_gen)
final_dists = _calc_final_dist(enc_extended_inp, predictions,
attentions, p_gens, batch_oov_len,
self.params["vocab_size"],
self.params["batch_size"])
return tf.stack(
final_dists, 1
), dec_hidden # predictions_shape = (batch_size, dec_len, vocab_size) with dec_len = 1 in pred mode
class PGN(tf.keras.Model):
def __init__(self, params):
super(PGN, self).__init__()
self.params = params
self.encoder = Encoder(params["vocab_size"], params["embed_size"],
params["enc_units"], params["batch_size"])
self.attention = BahdanauAttention(params["attn_units"])
if params["coverage"]:
self.coverage = Coverage(params["attn_units"])
self.decoder = Decoder(params["vocab_size"], params["embed_size"],
params["dec_units"], params["batch_size"],
params["use_stats"])
self.pointer = Pointer()
def call_encoder(self, enc_inp):
enc_hidden = self.encoder.initialize_hidden_state()
enc_output, enc_hidden = self.encoder(enc_inp, enc_hidden)
return enc_hidden, enc_output
def call(self,
enc_output,
dec_hidden,
enc_inp,
enc_extended_inp,
dec_inp,
batch_oov_len,
cov_vec,
stats=None):
predictions = []
attentions = []
p_gens = []
if self.params["coverage"]:
cov_features = self.coverage(cov_vec)
else:
cov_features = None
context_vector, _ = self.attention(dec_hidden, enc_output,
cov_features)
for t in range(dec_inp.shape[1]):
dec_x, pred, dec_hidden = self.decoder(
tf.expand_dims(dec_inp[:, t], 1), dec_hidden, enc_output,
context_vector, stats)
if self.params["coverage"]:
cov_features = self.coverage(cov_vec)
else:
cov_features = None
context_vector, attn = self.attention(dec_hidden, enc_output,
cov_features)
p_gen = self.pointer(context_vector, dec_hidden,
tf.squeeze(dec_x, axis=1))
if self.params["coverage"]:
cov_vec += attn
attn = tf.squeeze(attn, axis=-1)
predictions.append(pred)
attentions.append(attn)
p_gens.append(p_gen)
final_dists = _calc_final_dist(enc_extended_inp, predictions,
attentions, p_gens, batch_oov_len,
self.params["vocab_size"],
self.params["batch_size"])
res = {}
res["final_dists"] = tf.stack(final_dists, 1)
res["dec_hidden"] = dec_hidden
if self.params["coverage"] or self.params["mode"] != "train":
res["cov_vec"] = cov_vec
res["attn_weights"] = tf.stack(attentions, 1)
if self.params["mode"] != "train":
res["context"] = context_vector
res["p_gens"] = tf.stack(p_gens, 1)
return res # predictions_shape = (batch_size, dec_len, vocab_size) with dec_len = 1 in pred mode
