def train():
embeddings = np.load('text_embedding.npy', allow_pickle=True)
sentiments = np.load('sentiments.npy', allow_pickle=True)
texts = np.load('texts.npy', allow_pickle=True)
all_texts = np.load('text_cache.npy', allow_pickle=True)
categorical_sentiments = to_categorical(sentiments, num_classes=5)
tokenizer = Tokenizer(num_words=300000, oov_token=None)
tokenizer.fit_on_texts(all_texts)
X_train, X_test, Y_train, Y_test = train_test_split(texts,
categorical_sentiments,
test_size=0.2)
np.save("text_train.npy", X_train)
np.save("sentiment_train.npy", Y_train)
models = Models()
logdir = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = TensorBoard(log_dir=logdir)
models = []
bgruModel = "ensemble_bgru.h5"
models.buil_pre_model(embeddings)
model = models.model
if os.path.isfile(filepath):
model = load_model(filepath)
checkpoint = ModelCheckpoint(filepath,
monitor='loss',
verbose=1,
save_best_only=True,
mode='min')
callbacks_list = [checkpoint, tensorboard_callback]
model.fit(pad_sequences(tokenizer.texts_to_sequences(X_train[:500000]),
maxlen=75),
Y_train[:500000],
batch_size=512,
epochs=50,
validation_data=(pad_sequences(tokenizer.texts_to_sequences(
X_test[:5000]),
maxlen=75), Y_test[:5000]),
callbacks=callbacks_list,
shuffle=True)
result = model.predict_on_batch(
pad_sequences(tokenizer.texts_to_sequences([
" What happened 2 ur vegan food options?! At least say on ur site so i know I won't be able 2 eat anything for next 6 hrs #fail",
" I sleep hungry and It gets harder everyday",
"everything is great, i have lost some weight",
"awesome, really cool", "should I play cards",
"I am full and inshape", "is it okay to be that hungry at night?"
]),
maxlen=75))
print("result: ", np.argmax(result, axis=-1), "\n")
count += 1
vocab = np.append(vocab, [words[0]])
texts = np.append(texts, vocab)
sentiments = np.append(sentiments, [0] * len(vocab))
categorical_sentiments = to_categorical(sentiments, num_classes=5)
tokenizer = Tokenizer(num_words=300000)
tokenizer.fit_on_texts(texts)
X_train, X_test, Y_train, Y_test = train_test_split(texts,
categorical_sentiments,
test_size=0.2)
logdir = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = TensorBoard(log_dir=logdir)
filepath = "return.h5"
models = Models()
models.buil_pre_model(embeddings)
model = models.model
if os.path.isfile(filepath):
model = load_model(filepath)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['acc'])
checkpoint = ModelCheckpoint(filepath,
monitor='loss',
verbose=1,
save_best_only=True,
mode='min')
callbacks_list = [checkpoint, tensorboard_callback]
model.fit(pad_sequences(tokenizer.texts_to_sequences(texts), maxlen=150),
