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main.py
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main.py
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import numpy as np
import pickle
import data.load
from metrics.accuracy import conlleval
from keras.models import Sequential
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import SimpleRNN, GRU, LSTM
from keras.layers.core import Dense, Dropout
from keras.layers.wrappers import TimeDistributed
from keras.layers import Convolution1D, MaxPooling1D
import progressbar
### Load Data
train_set, valid_set, dicts = data.load.atisfull()
w2idx, ne2idx, labels2idx = dicts['words2idx'], dicts['tables2idx'], dicts['labels2idx']
# Create index to word/label dicts
idx2w = {w2idx[k]:k for k in w2idx}
idx2ne = {ne2idx[k]:k for k in ne2idx}
idx2la = {labels2idx[k]:k for k in labels2idx}
### Model
n_classes = len(idx2la)
n_vocab = len(idx2w)
# Define model
model = Sequential()
model.add(Embedding(n_vocab,100))
model.add(Convolution1D(64,5,border_mode='same', activation='relu'))
model.add(Dropout(0.25))
model.add(GRU(100,return_sequences=True))
model.add(TimeDistributed(Dense(n_classes, activation='softmax')))
model.compile('rmsprop', 'categorical_crossentropy')
### Ground truths etc for conlleval
train_x, train_ne, train_label = train_set
val_x, val_ne, val_label = valid_set
words_val = [ list(map(lambda x: idx2w[x], w)) for w in val_x]
groundtruth_val = [ list(map(lambda x: idx2la[x], y)) for y in val_label]
words_train = [ list(map(lambda x: idx2w[x], w)) for w in train_x]
groundtruth_train = [ list(map(lambda x: idx2la[x], y)) for y in train_label]
### Training
n_epochs = 100
train_f_scores = []
val_f_scores = []
best_val_f1 = 0
for i in range(n_epochs):
print("Epoch {}".format(i))
print("Training =>")
train_pred_label = []
avgLoss = 0
bar = progressbar.ProgressBar(max_value=len(train_x))
for n_batch, sent in bar(enumerate(train_x)):
label = train_label[n_batch]
label = np.eye(n_classes)[label][np.newaxis,:]
sent = sent[np.newaxis,:]
if sent.shape[1] > 1: #some bug in keras
loss = model.train_on_batch(sent, label)
avgLoss += loss
pred = model.predict_on_batch(sent)
pred = np.argmax(pred,-1)[0]
train_pred_label.append(pred)
avgLoss = avgLoss/n_batch
predword_train = [ list(map(lambda x: idx2la[x], y)) for y in train_pred_label]
con_dict = conlleval(predword_train, groundtruth_train, words_train, 'r.txt')
train_f_scores.append(con_dict['f1'])
print('Loss = {}, Precision = {}, Recall = {}, F1 = {}'.format(avgLoss, con_dict['r'], con_dict['p'], con_dict['f1']))
print("Validating =>")
val_pred_label = []
avgLoss = 0
bar = progressbar.ProgressBar(max_value=len(val_x))
for n_batch, sent in bar(enumerate(val_x)):
label = val_label[n_batch]
label = np.eye(n_classes)[label][np.newaxis,:]
sent = sent[np.newaxis,:]
if sent.shape[1] > 1: #some bug in keras
loss = model.test_on_batch(sent, label)
avgLoss += loss
pred = model.predict_on_batch(sent)
pred = np.argmax(pred,-1)[0]
val_pred_label.append(pred)
avgLoss = avgLoss/n_batch
predword_val = [ list(map(lambda x: idx2la[x], y)) for y in val_pred_label]
con_dict = conlleval(predword_val, groundtruth_val, words_val, 'r.txt')
val_f_scores.append(con_dict['f1'])
print('Loss = {}, Precision = {}, Recall = {}, F1 = {}'.format(avgLoss, con_dict['r'], con_dict['p'], con_dict['f1']))
if con_dict['f1'] > best_val_f1:
best_val_f1 = con_dict['f1']
open('model_architecture.json','w').write(model.to_json())
model.save_weights('best_model_weights.h5',overwrite=True)
print("Best validation F1 score = {}".format(best_val_f1))
print()