def base_attention_lstm(vocabulary_size, time_steps=32, type="after"):
"""
:param vocabulary_size:
:param time_steps:
:param type: before/after
:return:
"""
INPUT_DIM = 64
LSTM_UNITS = 32
text_input = Input(shape=(time_steps, ), dtype='int32',
name='text') # (batch_size, time_steps)
inputs = layers.Embedding(vocabulary_size, INPUT_DIM)(
text_input) # (batch_size, time_steps, input_dim)
if type == "before":
output_attention_mul = attention_3d_block(inputs,
time_steps=time_steps,
single_attention_vector=True)
attention_mul = LSTM(LSTM_UNITS,
return_sequences=False)(output_attention_mul)
else: # after
lstm_out = LSTM(LSTM_UNITS, return_sequences=True)(inputs)
lstm_out = Reshape((time_steps, LSTM_UNITS))(lstm_out)
attention_mul = attention_3d_block(lstm_out,
time_steps=time_steps,
single_attention_vector=True)
attention_mul = Flatten()(attention_mul)
attention_mul = Dense(64)(attention_mul)
output = Dense(1, activation='sigmoid')(attention_mul)
model = Model(inputs=text_input, outputs=output)
return model
def __init__(self, type, model_name):
self.type = type
self.model_name = model_name
self.word2em = load_glove()
# print(len(self.word2em))
# print(self.word2em['start'])
self.target_word2idx = np.load(
'chatbot_train/models/' + DATA_SET_NAME +
'/word-glove-target-word2idx.npy').item()
self.target_idx2word = np.load(
'chatbot_train/models/' + DATA_SET_NAME +
'/word-glove-target-idx2word.npy').item()
context = np.load('chatbot_train/models/' + DATA_SET_NAME +
'/word-glove-context.npy').item()
self.max_encoder_seq_length = context['encoder_max_seq_length']
self.max_decoder_seq_length = context['decoder_max_seq_length']
self.num_decoder_tokens = context['num_decoder_tokens']
if ('attention' in self.type):
# THIS IS STILL RANDOM IDEA
# encoder_inputs = Input(shape=(None, MAX_INPUT_SEQ_LENGTH, GLOVE_EMBEDDING_SIZE), name='encoder_inputs')
encoder_inputs = Input(shape=(None, GLOVE_EMBEDDING_SIZE),
name='encoder_inputs')
else:
encoder_inputs = Input(shape=(None, GLOVE_EMBEDDING_SIZE),
name='encoder_inputs')
if (self.type == 'bidirectional'):
print('PREDICTING ON BIDIRECTIONAL')
encoder_lstm = Bidirectional(
LSTM(units=HIDDEN_UNITS,
return_state=True,
name='encoder_lstm'))
encoder_outputs, encoder_state_forward_h, encoder_state_forward_c, encoder_state_backward_h, encoder_state_backward_c = encoder_lstm(
encoder_inputs)
# IF BIDIRECTIONAL, NEEDS TO CONCATENATE FORWARD AND BACKWARD STATE
encoder_state_h = Concatenate()(
[encoder_state_forward_h, encoder_state_backward_h])
encoder_state_c = Concatenate()(
[encoder_state_forward_c, encoder_state_backward_c])
else:
encoder_lstm = LSTM(units=HIDDEN_UNITS,
return_state=True,
name='encoder_lstm')
if ('attention' in self.type):
# THIS IS STILL RANDOM IDEA TO IGNORE THE 2ND DIMENSION
# encoder_outputs, _, encoder_state_h, encoder_state_c = encoder_lstm(encoder_inputs)
encoder_outputs, encoder_state_h, encoder_state_c = encoder_lstm(
encoder_inputs)
else:
encoder_outputs, encoder_state_h, encoder_state_c = encoder_lstm(
encoder_inputs)
encoder_states = [encoder_state_h, encoder_state_c]
if (self.type == 'bidirectional'):
decoder_inputs = Input(shape=(None, GLOVE_EMBEDDING_SIZE),
name='decoder_inputs')
decoder_lstm = LSTM(units=HIDDEN_UNITS * 2,
return_state=True,
return_sequences=True,
name='decoder_lstm')
decoder_outputs, decoder_state_h, decoder_state_c = decoder_lstm(
decoder_inputs, initial_state=encoder_states)
else:
if ('attention' in self.type):
# HERE, THE GLOVE EMBEDDING SIZE ACTS AS THE INPUT DIMENSION
# IF USING ATTENTION, WE NEED TO SET SHAPE WITH TIME STEPS, NOT WITH NONE
# THIS INPUT WILL BE USED WHEN BUILDING ENCODER OUTPUTS
# decoder_inputs = Input(shape=(None, attention_lstm.TIME_STEPS, GLOVE_EMBEDDING_SIZE), name='decoder_inputs')
# decoder_inputs = Input(shape=(None, GLOVE_EMBEDDING_SIZE), name='decoder_inputs')
# decoder_inputs = Input(shape=(MAX_TARGET_SEQ_LENGTH + 2, GLOVE_EMBEDDING_SIZE), name='decoder_inputs')
decoder_inputs = Input(shape=(self.max_decoder_seq_length,
GLOVE_EMBEDDING_SIZE),
name='decoder_inputs')
if (self.type == 'attention_before'):
attention_mul = attention_lstm.attention_3d_block(
decoder_inputs, self.max_decoder_seq_length)
else:
decoder_inputs = Input(shape=(None, GLOVE_EMBEDDING_SIZE),
name='decoder_inputs')
# PAY ATTENTION THAT DECODER AND ENCODER STATE MUST ALWAYS HAVE THE SAME DIMENSION
# IN THIS CASE, WE USE 2D
decoder_lstm = LSTM(units=HIDDEN_UNITS,
return_state=True,
return_sequences=True,
name='decoder_lstm')
if ('attention' in self.type):
# REMOVE ENCODER AS INITIAL STATE FOR ATTENTION
# decoder_outputs, decoder_state_h, decoder_state_c = decoder_lstm(decoder_inputs)
decoder_outputs, decoder_state_h, decoder_state_c = decoder_lstm(
decoder_inputs, initial_state=encoder_states)
else:
decoder_outputs, decoder_state_h, decoder_state_c = decoder_lstm(
decoder_inputs, initial_state=encoder_states)
if (self.type == 'attention_after'):
attention_mul = attention_lstm.attention_3d_block(
decoder_outputs, self.max_decoder_seq_length)
# SOMEHOW THIS FLATTEN FUNCTION CAUSE THE PROBLEM
# attention_mul = Flatten()(attention_mul)
decoder_dense = Dense(units=self.num_decoder_tokens,
activation='softmax',
name='decoder_dense')
if (self.type == 'attention_after' or self.type == 'attention_before'):
decoder_outputs = decoder_dense(attention_mul)
else:
decoder_outputs = decoder_dense(decoder_outputs)
self.model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
# model_json = open('chatbot_train/models/' + DATA_SET_NAME + '/word-glove-architecture.json', 'r').read()
# self.model = model_from_json(model_json)
# CHANGE THE MODEL FILE TO ENV SO IT CAN BE CONFIGURABLE WHICH
# MODEL (ITERATION) WILL BE USED TO REPLY
# self.model.load_weights('chatbot_train/models/' + DATA_SET_NAME + '/word-glove-weights.h5')
self.model.load_weights('chatbot_train/models/' + DATA_SET_NAME + '/' +
os.getenv(self.model_name))
self.model.compile(optimizer='rmsprop',
loss='categorical_crossentropy')
self.encoder_model = Model(encoder_inputs, encoder_states)
if (self.type == 'bidirectional'):
decoder_state_inputs = [
Input(shape=(HIDDEN_UNITS * 2, )),
Input(shape=(HIDDEN_UNITS * 2, ))
]
else:
decoder_state_inputs = [
Input(shape=(HIDDEN_UNITS, )),
Input(shape=(HIDDEN_UNITS, ))
]
decoder_outputs, state_h, state_c = decoder_lstm(
decoder_inputs, initial_state=decoder_state_inputs)
decoder_states = [state_h, state_c]
decoder_outputs = decoder_dense(decoder_outputs)
self.decoder_model = Model([decoder_inputs] + decoder_state_inputs,
[decoder_outputs] + decoder_states)
decoder_inputs = Input(shape=(None, GLOVE_EMBEDDING_SIZE), name='decoder_inputs')
decoder_lstm = LSTM(units=HIDDEN_UNITS * 2, return_state=True, return_sequences=True, name='decoder_lstm')
decoder_outputs, decoder_state_h, decoder_state_c = decoder_lstm(decoder_inputs,
initial_state=encoder_states)
else:
if('attention' in sys.argv[1]):
# HERE, THE GLOVE EMBEDDING SIZE ACTS AS THE INPUT DIMENSION
# IF USING ATTENTION, WE NEED TO SET SHAPE WITH TIME STEPS, NOT WITH NONE
# THIS INPUT WILL BE USED WHEN BUILDING ENCODER OUTPUTS
# decoder_inputs = Input(shape=(None, attention_lstm.TIME_STEPS, GLOVE_EMBEDDING_SIZE), name='decoder_inputs')
# decoder_inputs = Input(shape=(None, GLOVE_EMBEDDING_SIZE), name='decoder_inputs')
decoder_inputs = Input(shape=(decoder_max_seq_length, GLOVE_EMBEDDING_SIZE), name='decoder_inputs')
if(sys.argv[1] == 'attention_before'):
attention_mul = attention_lstm.attention_3d_block(decoder_inputs, decoder_max_seq_length)
else:
decoder_inputs = Input(shape=(None, GLOVE_EMBEDDING_SIZE), name='decoder_inputs')
# PAY ATTENTION THAT DECODER AND ENCODER STATE MUST ALWAYS HAVE THE SAME DIMENSION
# IN THIS CASE, WE USE 2D
decoder_lstm = LSTM(units=HIDDEN_UNITS, return_state=True, return_sequences=True, name='decoder_lstm')
if('attention' in sys.argv[1]):
# REMOVE ENCODER AS INITIAL STATE FOR ATTENTION
# decoder_outputs, decoder_state_h, decoder_state_c = decoder_lstm(decoder_inputs)
decoder_outputs, decoder_state_h, decoder_state_c = decoder_lstm(decoder_inputs,
initial_state=encoder_states)
else:
decoder_outputs, decoder_state_h, decoder_state_c = decoder_lstm(decoder_inputs,
initial_state=encoder_states)
model.add(Dropout(DROPOUT_VAL))
model.add(Dense(LSTM_DIM))
model.add(Dropout(DROPOUT_VAL))
model.add(Dense(len(chars)))
model.add(Activation("softmax"))
elif network == "LSTM2-ATTENTION":
# Model 2 - CuDNNLSTM X*2 + attention block
from keras.models import Model
from keras.layers import Input, Flatten
inputs = Input(shape=(maxlen, len(chars),))
lstm_out = CuDNNLSTM(LSTM_DIM, return_sequences=True)(inputs)
lstm_out = Dropout(DROPOUT_VAL)(lstm_out)
lstm_out = CuDNNLSTM(LSTM_DIM, return_sequences=True)(lstm_out)
lstm_out = Dropout(DROPOUT_VAL)(lstm_out)
attention_mul = attention_3d_block(lstm_out)
attention_mul = Flatten()(attention_mul)
output = Dense(len(chars), activation='softmax')(attention_mul)
model = Model(input=[inputs], output=output)
elif network == "LSTM2-ATTENTION-FELIX":
# Model 2 - CuDNNLSTM X*2 + attention block
from keras.models import Model
from keras.layers import Input
inputs = Input(shape=(maxlen, len(chars),))
lstm_out = CuDNNLSTM(LSTM_DIM, return_sequences=True)(inputs)
lstm_out = Dropout(DROPOUT_VAL)(lstm_out)
lstm_out = CuDNNLSTM(LSTM_DIM, return_sequences=True)(lstm_out)
lstm_out = Dropout(DROPOUT_VAL)(lstm_out)
attention_mul = attention_3d_block_felixhao28(lstm_out)
output = Dense(len(chars), activation='softmax')(attention_mul)
