In [1]:
from utils import *
import tensorflow as tf
from sklearn.cross_validation import train_test_split
import time
In [2]:
trainset = sklearn.datasets.load_files(container_path = 'data', encoding = 'UTF-8')
trainset.data, trainset.target = separate_dataset(trainset,1.0)
print (trainset.target_names)
print (len(trainset.data))
print (len(trainset.target))
In [3]:
train_X, test_X, train_Y, test_Y = train_test_split(trainset.data, trainset.target, test_size = 0.2)
In [4]:
concat = ' '.join(trainset.data).split()
vocabulary_size = len(list(set(concat)))
data, count, dictionary, rev_dictionary = build_dataset(concat, vocabulary_size)
print('vocab from size: %d'%(vocabulary_size))
print('Most common words', count[4:10])
print('Sample data', data[:10], [rev_dictionary[i] for i in data[:10]])
In [5]:
GO = dictionary['GO']
PAD = dictionary['PAD']
EOS = dictionary['EOS']
UNK = dictionary['UNK']
In [6]:
size_layer = 128
num_layers = 2
embedded_size = 128
dimension_output = len(trainset.target_names)
learning_rate = 1e-3
maxlen = 50
batch_size = 128
In [7]:
class Model:
def __init__(self, size_layer, num_layers, embedded_size,
dict_size, dimension_output, learning_rate):
def cells(reuse=False):
return tf.nn.rnn_cell.LSTMCell(size_layer,initializer=tf.orthogonal_initializer(),reuse=reuse)
self.X = tf.placeholder(tf.int32, [None, None])
self.Y = tf.placeholder(tf.int32, [None])
batch_size = tf.shape(self.X)[0]
encoder_embeddings = tf.Variable(tf.random_uniform([dict_size, embedded_size], -1, 1))
encoder_embedded = tf.nn.embedding_lookup(encoder_embeddings, self.X)
outputs, last_state = tf.nn.dynamic_rnn(tf.nn.rnn_cell.MultiRNNCell([cells() for _ in range(num_layers)]),
encoder_embedded, dtype = tf.float32)
attention_mechanism = tf.contrib.seq2seq.LuongAttention(num_units = size_layer,
memory = outputs)
rnn_cells = tf.contrib.seq2seq.AttentionWrapper(cell = tf.nn.rnn_cell.MultiRNNCell([cells() for _ in range(num_layers)]),
attention_mechanism = attention_mechanism,
attention_layer_size = size_layer,
alignment_history=True)
decoder_outputs, decoder_last_state = tf.nn.dynamic_rnn(rnn_cells, encoder_embedded,
initial_state = rnn_cells.zero_state(batch_size, tf.float32).clone(cell_state=last_state),
dtype = tf.float32)
self.alignments = tf.transpose(decoder_last_state.alignment_history.stack(),[1,2,0])
W = tf.get_variable('w',shape=(size_layer, dimension_output),initializer=tf.orthogonal_initializer())
b = tf.get_variable('b',shape=(dimension_output),initializer=tf.zeros_initializer())
self.logits = tf.matmul(outputs[:, -1], W) + b
self.cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits = self.logits, labels = self.Y))
self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(self.cost)
correct_pred = tf.equal(tf.argmax(self.logits, 1,output_type=tf.int32), self.Y)
self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
In [8]:
tf.reset_default_graph()
sess = tf.InteractiveSession()
model = Model(size_layer,num_layers,embedded_size,len(dictionary),dimension_output,learning_rate)
sess.run(tf.global_variables_initializer())
In [9]:
EARLY_STOPPING, CURRENT_CHECKPOINT, CURRENT_ACC, EPOCH = 5, 0, 0, 0
while True:
lasttime = time.time()
if CURRENT_CHECKPOINT == EARLY_STOPPING:
print('break epoch:%d\n'%(EPOCH))
break
train_acc, train_loss, test_acc, test_loss = 0, 0, 0, 0
for i in range(0, (len(train_X) // batch_size) * batch_size, batch_size):
batch_x = str_idx(train_X[i:i+batch_size],dictionary,maxlen)
acc, loss, _ = sess.run([model.accuracy, model.cost, model.optimizer],
feed_dict = {model.X : batch_x, model.Y : train_Y[i:i+batch_size]})
train_loss += loss
train_acc += acc
for i in range(0, (len(test_X) // batch_size) * batch_size, batch_size):
batch_x = str_idx(test_X[i:i+batch_size],dictionary,maxlen)
acc, loss = sess.run([model.accuracy, model.cost],
feed_dict = {model.X : batch_x, model.Y : test_Y[i:i+batch_size]})
test_loss += loss
test_acc += acc
train_loss /= (len(train_X) // batch_size)
train_acc /= (len(train_X) // batch_size)
test_loss /= (len(test_X) // batch_size)
test_acc /= (len(test_X) // batch_size)
if test_acc > CURRENT_ACC:
print('epoch: %d, pass acc: %f, current acc: %f'%(EPOCH,CURRENT_ACC, test_acc))
CURRENT_ACC = test_acc
CURRENT_CHECKPOINT = 0
else:
CURRENT_CHECKPOINT += 1
print('time taken:', time.time()-lasttime)
print('epoch: %d, training loss: %f, training acc: %f, valid loss: %f, valid acc: %f\n'%(EPOCH,train_loss,
train_acc,test_loss,
test_acc))
EPOCH += 1
In [10]:
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
In [11]:
heatmap=sess.run(model.alignments,feed_dict={model.X:str_idx(test_X[1:2],dictionary,len(test_X[1].split()))})
In [12]:
plt.figure(figsize=(15,10))
sns.heatmap(heatmap[:,0,:],
xticklabels=test_X[1].split(),yticklabels=test_X[1].split())
plt.show()