def main(_):
trainX, trainY, testX, testY = None, None, None, None
vocabulary_word2index, vocabulary_index2word = create_voabulary()
vocab_size = len(vocabulary_word2index)
vocabulary_word2index_label, vocabulary_index2word_label = create_voabulary_label()
train,test = load_data_with_multilabels(vocabulary_word2index, vocabulary_word2index_label,FLAGS.training_path) #[1,11,3,1998,1998]
trainX, trainY= train #TODO trainY1999
testX, testY = test #TODO testY1999
print("testX.shape:", np.array(testX).shape);print("testY.shape:", np.array(testY).shape) # 2500个label
# 2.Data preprocessing
# Sequence padding
print("start padding & transform to one hot...")
trainX = pad_sequences(trainX, maxlen=FLAGS.sentence_len, value=0.) # padding to max length
testX = pad_sequences(testX, maxlen=FLAGS.sentence_len, value=0.) # padding to max length
print("end padding & transform to one hot...")
#2.create session.
config=tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:
#Instantiate Model
fast_text=fastText(FLAGS.label_size, FLAGS.learning_rate, FLAGS.batch_size, FLAGS.decay_steps, FLAGS.decay_rate,FLAGS.num_sampled,FLAGS.sentence_len,vocab_size,FLAGS.embed_size,FLAGS.is_training)
#Initialize Save
saver=tf.train.Saver()
if os.path.exists(FLAGS.ckpt_dir+"checkpoint"):
print("Restoring Variables from Checkpoint")
saver.restore(sess,tf.train.latest_checkpoint(FLAGS.ckpt_dir))
else:
print('Initializing Variables')
sess.run(tf.global_variables_initializer())
if FLAGS.use_embedding: #load pre-trained word embedding
assign_pretrained_word_embedding(sess, vocabulary_index2word, vocab_size, fast_text)
curr_epoch=sess.run(fast_text.epoch_step)
#3.feed data & training
number_of_training_data=len(trainX)
batch_size=FLAGS.batch_size
for epoch in range(curr_epoch,FLAGS.num_epochs):#range(start,stop,step_size)
loss, acc, counter = 0.0, 0.0, 0
for start, end in zip(range(0, number_of_training_data, batch_size),range(batch_size, number_of_training_data, batch_size)):
if epoch==0 and counter==0:
print("trainX[start:end]:",trainX[start:end]) #2d-array. each element slength is a 100.
print("trainY[start:end]:",trainY[start:end]) #a list,each element is a list.element:may be has 1,2,3,4,5 labels.
#print("trainY1999[start:end]:",trainY1999[start:end])
curr_loss,_=sess.run([fast_text.loss_val,fast_text.train_op],feed_dict={fast_text.sentence:trainX[start:end],fast_text.labels:trainY[start:end],}) #fast_text.labels_l1999:trainY1999[start:end]
loss,counter=loss+curr_loss,counter+1 #acc+curr_acc,
if counter %500==0:
print("Epoch %d\tBatch %d\tTrain Loss:%.3f" %(epoch,counter,loss/float(counter))) #\tTrain Accuracy:%.3f--->,acc/float(counter)
#epoch increment
print("going to increment epoch counter....")
sess.run(fast_text.epoch_increment)
# 4.validation
print("epoch:",epoch,"validate_every:",FLAGS.validate_every,"validate or not:",(epoch % FLAGS.validate_every==0))
if epoch % FLAGS.validate_every==0:
eval_loss,eval_accuracy=do_eval(sess,fast_text,testX,testY,batch_size,vocabulary_index2word_label) #testY1999,eval_acc
print("Epoch %d Validation Loss:%.3f\tValidation Accuracy: %.3f" % (epoch,eval_loss,eval_accuracy)) #,\tValidation Accuracy: %.3f--->eval_acc
#save model to checkpoint
save_path=FLAGS.ckpt_dir+"model.ckpt"
saver.save(sess,save_path,global_step=epoch) #fast_text.epoch_step
# 5.最后在测试集上做测试，并报告测试准确率 Test
test_loss, test_acc = do_eval(sess, fast_text, testX, testY,batch_size,vocabulary_index2word_label) #testY1999
pass
def main(_):
trainX, trainY, testX, testY = None, None, None, None
#vocabulary_word2index, vocabulary_index2word = create_voabulary()
#vocab_size = len(vocabulary_word2index)
#vocabulary_word2index_label, vocabulary_index2word_label = create_voabulary_label()
#train,test = load_data_with_multilabels(vocabulary_word2index, vocabulary_word2index_label,FLAGS.training_path) #[1,11,3,1998,1998]
#trainX, trainY= train #TODO trainY1999
#testX, testY = test #TODO testY1999
#print("testX.shape:", np.array(testX).shape);print("testY.shape:", np.array(testY).shape) # 2500个label
# 2.Data preprocessing
# Sequence padding
#print("start padding & transform to one hot...")
#trainX = pad_sequences(trainX, maxlen=FLAGS.sentence_len, value=0.) # padding to max length
#testX = pad_sequences(testX, maxlen=FLAGS.sentence_len, value=0.) # padding to max length
#print("end padding & transform to one hot...")
word2index, label2index, trainX, trainY, vaildX, vaildY, testX, testY = load_data(
FLAGS.cache_file_h5py, FLAGS.cache_file_pickle)
index2label = {v: k for k, v in label2index.items()}
vocab_size = len(word2index)
print("cnn_model.vocab_size:", vocab_size)
num_classes = len(label2index)
print("num_classes:", num_classes)
num_examples, FLAGS.sentence_len = trainX.shape
print("num_examples of training:", num_examples, ";sentence_len:",
FLAGS.sentence_len)
#2.create session.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
#Instantiate Model
fast_text = fastText(num_classes, FLAGS.learning_rate,
FLAGS.batch_size, FLAGS.decay_steps,
FLAGS.decay_rate, FLAGS.num_sampled,
FLAGS.sentence_len, vocab_size, FLAGS.embed_size,
FLAGS.is_training)
#Initialize Save
saver = tf.train.Saver()
if os.path.exists(FLAGS.ckpt_dir + "checkpoint"):
print("Restoring Variables from Checkpoint")
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.ckpt_dir))
else:
print('Initializing Variables')
sess.run(tf.global_variables_initializer())
if FLAGS.use_embedding: #load pre-trained word embedding
vocabulary_index2word = {v: k for k, v in word2index.items()}
assign_pretrained_word_embedding(sess, vocabulary_index2word,
vocab_size, fast_text)
curr_epoch = sess.run(fast_text.epoch_step)
#3.feed data & training
number_of_training_data = len(trainX)
batch_size = FLAGS.batch_size
for epoch in range(curr_epoch,
FLAGS.num_epochs): #range(start,stop,step_size)
loss, acc, counter = 0.0, 0.0, 0
for start, end in zip(
range(0, number_of_training_data, batch_size),
range(batch_size, number_of_training_data, batch_size)):
if epoch == 0 and counter == 0:
print("trainX[start:end]:", trainX[start:end]
) #2d-array. each element slength is a 100.
print(
"trainY[start:end]:", trainY[start:end]
) #a list,each element is a list.element:may be has 1,2,3,4,5 labels.
#print("trainY1999[start:end]:",trainY1999[start:end])
train_Y_batch = process_labels(trainY[start:end])
curr_loss, _ = sess.run(
[fast_text.loss_val, fast_text.train_op],
feed_dict={
fast_text.sentence: trainX[start:end],
fast_text.labels: train_Y_batch
}) #fast_text.labels_l1999:trainY1999[start:end]
loss, counter = loss + curr_loss, counter + 1 #acc+curr_acc,
if counter % 500 == 0:
print("Epoch %d\tBatch %d\tTrain Loss:%.3f" %
(epoch, counter, loss / float(counter))
) #\tTrain Accuracy:%.3f--->,acc/float(counter)
#epoch increment
print("going to increment epoch counter....")
sess.run(fast_text.epoch_increment)
# 4.validation
print("epoch:", epoch, "validate_every:", FLAGS.validate_every,
"validate or not:", (epoch % FLAGS.validate_every == 0))
if epoch % FLAGS.validate_every == 0:
eval_loss, eval_accuracy = do_eval(
sess, fast_text, testX, testY, batch_size,
index2label) #testY1999,eval_acc
print(
"Epoch %d Validation Loss:%.3f\tValidation Accuracy: %.3f"
%
(epoch, eval_loss,
eval_accuracy)) #,\tValidation Accuracy: %.3f--->eval_acc
#save model to checkpoint
save_path = FLAGS.ckpt_dir + "model.ckpt"
saver.save(sess, save_path,
global_step=epoch) #fast_text.epoch_step
# 5.最后在测试集上做测试，并报告测试准确率 Test
test_loss, test_acc = do_eval(sess, fast_text, testX, testY,
batch_size, index2label) #testY1999
pass