def initial_setup(data_corpus):
metadata, idx_q, idx_a = data.load_data(PATH='data/{}/'.format(data_corpus))
(trainX, trainY), (testX, testY), (validX, validY) = data.split_dataset(idx_q, idx_a)
trainX = tl.prepro.remove_pad_sequences(trainX.tolist())
trainY = tl.prepro.remove_pad_sequences(trainY.tolist())
testX = tl.prepro.remove_pad_sequences(testX.tolist())
testY = tl.prepro.remove_pad_sequences(testY.tolist())
validX = tl.prepro.remove_pad_sequences(validX.tolist())
validY = tl.prepro.remove_pad_sequences(validY.tolist())
return metadata, trainX, trainY, testX, testY, validX, validY
def getDataset(idx_q, idx_a):
(trainX, trainY), (testX,
testY), (validX,
validY) = data.split_dataset(idx_q, idx_a)
trainX = trainX.tolist()
trainY = trainY.tolist()
testX = testX.tolist()
testY = testY.tolist()
validX = validX.tolist()
validY = validY.tolist()
trainX = tl.prepro.remove_pad_sequences(trainX)
trainY = tl.prepro.remove_pad_sequences(trainY)
testX = tl.prepro.remove_pad_sequences(testX)
testY = tl.prepro.remove_pad_sequences(testY)
validX = tl.prepro.remove_pad_sequences(validX)
validY = tl.prepro.remove_pad_sequences(validY)
return trainX, trainY, testX, testY, validX, validY
def load_data(path):
metadata, idx_q, idx_a = data.load_data(path)
(trainX, trainY), (testX,
testY), (validX,
validY) = data.split_dataset(idx_q, idx_a)
trainX = trainX.tolist()
trainY = trainY.tolist()
testX = testX.tolist()
testY = testY.tolist()
validX = validX.tolist()
validY = validY.tolist()
trainX = tl.prepro.remove_pad_sequences(trainX)
trainY = tl.prepro.remove_pad_sequences(trainY)
testX = tl.prepro.remove_pad_sequences(testX)
testY = tl.prepro.remove_pad_sequences(testY)
validX = tl.prepro.remove_pad_sequences(validX)
validY = tl.prepro.remove_pad_sequences(validY)
return trainX, trainY, testX, testY, validX, validY, metadata
# -*- coding: utf8 -*-
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
import tensorflow as tf
import numpy as np
import time
###============= prepare data
from data.twitter import data
metadata, idx_q, idx_a = data.load_data('data/twitter/') # Twitter
# from data.cornell_corpus import data
# metadata, idx_q, idx_a = data.load_data(PATH='data/cornell_corpus/') # Cornell Moive
(trainX, trainY), (testX, testY), (validX, validY) = data.split_dataset(idx_q, idx_a)
trainX = trainX.tolist()
trainY = trainY.tolist()
testX = testX.tolist()
testY = testY.tolist()
validX = validX.tolist()
validY = validY.tolist()
trainX = tl.prepro.remove_pad_sequences(trainX)
trainY = tl.prepro.remove_pad_sequences(trainY)
testX = tl.prepro.remove_pad_sequences(testX)
testY = tl.prepro.remove_pad_sequences(testY)
validX = tl.prepro.remove_pad_sequences(validX)
validY = tl.prepro.remove_pad_sequences(validY)
# -*- coding: utf8 -*-
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
import tensorflow as tf
import numpy as np
import time
###============= prepare data
from data.twitter import data
metadata, idx_q, idx_a = data.load_data(PATH='data/twitter/') # Twitter
# from data.cornell_corpus import data
# metadata, idx_q, idx_a = data.load_data(PATH='data/cornell_corpus/') # Cornell Moive
(trainX, trainY), (testX, testY), (validX,
validY) = data.split_dataset(idx_q, idx_a)
trainX = trainX.tolist()
trainY = trainY.tolist()
testX = testX.tolist()
testY = testY.tolist()
validX = validX.tolist()
validY = validY.tolist()
trainX = tl.prepro.remove_pad_sequences(trainX)
trainY = tl.prepro.remove_pad_sequences(trainY)
testX = tl.prepro.remove_pad_sequences(testX)
testY = tl.prepro.remove_pad_sequences(testY)
validX = tl.prepro.remove_pad_sequences(validX)
validY = tl.prepro.remove_pad_sequences(validY)
http://suriyadeepan.github.io/2016-12-31-practical-seq2seq/ """ import tensorflow as tf import tensorlayer as tl from tensorlayer.layers import * import tensorflow as tf import numpy as np import time ###============= prepare data from data.twitter import data metadata, idx_q, idx_a = data.load_data(PATH='data/twitter/') # Twitter # from data.cornell_corpus import data # metadata, idx_q, idx_a = data.load_data(PATH='data/cornell_corpus/') # Cornell Moive (trainX, trainY), (testX, testY), (validX, validY) = data.split_dataset(idx_q, idx_a) trainX = trainX.tolist() trainY = trainY.tolist() testX = testX.tolist() testY = testY.tolist() validX = validX.tolist() validY = validY.tolist() trainX = tl.prepro.remove_pad_sequences(trainX) trainY = tl.prepro.remove_pad_sequences(trainY) testX = tl.prepro.remove_pad_sequences(testX) testY = tl.prepro.remove_pad_sequences(testY) validX = tl.prepro.remove_pad_sequences(validX) validY = tl.prepro.remove_pad_sequences(validY)
def main():
metadata, idx_q, idx_a = data.load_data(PATH='data/twitter/') # Twitter
# from data.cornell_corpus import data
# metadata, idx_q, idx_a = data.load_data(PATH='data/cornell_corpus/') # Cornell Moive
(trainX, trainY), (testX,
testY), (validX,
validY) = data.split_dataset(idx_q, idx_a)
trainX = trainX.tolist()
trainY = trainY.tolist()
testX = testX.tolist()
testY = testY.tolist()
validX = validX.tolist()
validY = validY.tolist()
trainX = tl.prepro.remove_pad_sequences(trainX)
trainY = tl.prepro.remove_pad_sequences(trainY)
testX = tl.prepro.remove_pad_sequences(testX)
testY = tl.prepro.remove_pad_sequences(testY)
validX = tl.prepro.remove_pad_sequences(validX)
validY = tl.prepro.remove_pad_sequences(validY)
###============= parameters
xseq_len = len(trainX) #.shape[-1]
yseq_len = len(trainY) #.shape[-1]
assert xseq_len == yseq_len
batch_size = 32
n_step = int(xseq_len / batch_size)
xvocab_size = len(metadata['idx2w']) # 8002 (0~8001)
emb_dim = 1024
global w2idx
global idx2w
global encode_seqs2
global decode_seqs2
global start_id
global end_id
w2idx = metadata['w2idx'] # dict word 2 index
idx2w = metadata['idx2w'] # list index 2 word
unk_id = w2idx['unk'] # 1
pad_id = w2idx['_'] # 0
start_id = xvocab_size # 8002
end_id = xvocab_size + 1 # 8003
w2idx.update({'start_id': start_id})
w2idx.update({'end_id': end_id})
idx2w = idx2w + ['start_id', 'end_id']
xvocab_size = yvocab_size = xvocab_size + 2
""" A data for Seq2Seq should look like this:
input_seqs : ['how', 'are', 'you', '<PAD_ID'>]
decode_seqs : ['<START_ID>', 'I', 'am', 'fine', '<PAD_ID'>]
target_seqs : ['I', 'am', 'fine', '<END_ID>', '<PAD_ID'>]
target_mask : [1, 1, 1, 1, 0]
"""
print("encode_seqs", [idx2w[id] for id in trainX[10]])
target_seqs = tl.prepro.sequences_add_end_id([trainY[10]],
end_id=end_id)[0]
# target_seqs = tl.prepro.remove_pad_sequences([target_seqs], pad_id=pad_id)[0]
print("target_seqs", [idx2w[id] for id in target_seqs])
decode_seqs = tl.prepro.sequences_add_start_id([trainY[10]],
start_id=start_id,
remove_last=False)[0]
# decode_seqs = tl.prepro.remove_pad_sequences([decode_seqs], pad_id=pad_id)[0]
print("decode_seqs", [idx2w[id] for id in decode_seqs])
target_mask = tl.prepro.sequences_get_mask([target_seqs])[0]
print("target_mask", target_mask)
print(len(target_seqs), len(decode_seqs), len(target_mask))
###============= model
global net_rnn
def model(encode_seqs, decode_seqs, is_train=True, reuse=False):
with tf.variable_scope("model", reuse=reuse):
# for chatbot, you can use the same embedding layer,
# for translation, you may want to use 2 seperated embedding layers
with tf.variable_scope("embedding") as vs:
net_encode = EmbeddingInputlayer(inputs=encode_seqs,
vocabulary_size=xvocab_size,
embedding_size=emb_dim,
name='seq_embedding')
vs.reuse_variables()
tl.layers.set_name_reuse(True)
net_decode = EmbeddingInputlayer(inputs=decode_seqs,
vocabulary_size=xvocab_size,
embedding_size=emb_dim,
name='seq_embedding')
net_rnn = Seq2Seq(
net_encode,
net_decode,
cell_fn=tf.contrib.rnn.BasicLSTMCell,
n_hidden=emb_dim,
initializer=tf.random_uniform_initializer(-0.1, 0.1),
encode_sequence_length=retrieve_seq_length_op2(encode_seqs),
decode_sequence_length=retrieve_seq_length_op2(decode_seqs),
initial_state_encode=None,
dropout=(0.5 if is_train else None),
n_layer=3,
return_seq_2d=True,
name='seq2seq')
net_out = DenseLayer(net_rnn,
n_units=xvocab_size,
act=tf.identity,
name='output')
return net_out, net_rnn
# model for training
encode_seqs = tf.placeholder(dtype=tf.int64,
shape=[batch_size, None],
name="encode_seqs")
decode_seqs = tf.placeholder(dtype=tf.int64,
shape=[batch_size, None],
name="decode_seqs")
target_seqs = tf.placeholder(dtype=tf.int64,
shape=[batch_size, None],
name="target_seqs")
target_mask = tf.placeholder(
dtype=tf.int64, shape=[batch_size, None],
name="target_mask") # tl.prepro.sequences_get_mask()
net_out, _ = model(encode_seqs, decode_seqs, is_train=True, reuse=False)
# model for inferencing
encode_seqs2 = tf.placeholder(dtype=tf.int64,
shape=[1, None],
name="encode_seqs")
decode_seqs2 = tf.placeholder(dtype=tf.int64,
shape=[1, None],
name="decode_seqs")
net, net_rnn = model(encode_seqs2,
decode_seqs2,
is_train=False,
reuse=True)
global y
y = tf.nn.softmax(net.outputs)
# loss for training
# print(net_out.outputs) # (?, 8004)
# print(target_seqs) # (32, ?)
# loss_weights = tf.ones_like(target_seqs, dtype=tf.float32)
# loss = tf.contrib.legacy_seq2seq.sequence_loss(net_out.outputs, target_seqs, loss_weights, yvocab_size)
loss = tl.cost.cross_entropy_seq_with_mask(logits=net_out.outputs,
target_seqs=target_seqs,
input_mask=target_mask,
return_details=False,
name='cost')
net_out.print_params(False)
lr = 0.0001
train_op = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss)
# Truncated Backpropagation for training (option)
# max_grad_norm = 30
# grads, _ = tf.clip_by_global_norm(tf.gradients(loss, net_out.all_params),max_grad_norm)
# optimizer = tf.train.GradientDescentOptimizer(lr)
# train_op = optimizer.apply_gradients(zip(grads, net_out.all_params))
# sess = tf.InteractiveSession()
global sess
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False))
tl.layers.initialize_global_variables(sess)
tl.files.load_and_assign_npz(sess=sess, name='n.npz', network=net)
run(host='0.0.0.0', port=40026)
label_mask: mask
})
self.total_error += error
tl.files.save_npz(test_rnn.all_params, 'ChatbotRNN.npz', session)
if __name__ == "__main__":
frequentWords, questions, answers = data.load_data(PATH='data/twitter/')
word2id = frequentWords['w2idx']
id2word = frequentWords['idx2w']
word2id.update({'<GO>': len(id2word)})
word2id.update({'<EOS>': len(id2word) + 1})
size_of_dict = len(id2word) + 2
QuesTrain, AnsTrain, QuesTest, AnsTest = data.split_dataset(
questions, answers)
removeTrail = removeZeros()
assert removeTrail == True
QTrain, ATrain = shuffle(QTrain, ATrain, random_state=0)
encode_q = tf.placeholder(tf.int64, [cluster_size, None])
decode_a = tf.placeholder(tf.int64, [cluster_size, None])
label_a = tf.placeholder(tf.int64, [cluster_size, None])
label_mask = tf.placeholder(tf.int64, [cluster_size, None])
encode_test_q = tf.placeholder(tf.int64, [1, None])
decode_test_a = tf.placeholder(tf.int64, [1, None])
train_rnn, _ = create_model(encode_q, decode_a, True, False)
test_rnn, seq2seq_rnn = create_model(encode_test_q, decode_test_a, False,
True)
# Configuring the prototype to have soft placements without logging them
