def train_BLSTM():
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()
tokenizer.fit_on_texts(all_texts)
X_train, X_test, Y_train, Y_test = train_test_split(texts, categorical_sentiments, test_size=0.2)
models = Models()
logdir = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = TensorBoard(log_dir=logdir)
filepath = "blstm.h5"
models.build_BLSTM_model(embeddings)
model = models.model
if os.path.isfile(filepath):
model = load_model(filepath)
checkpoint = ModelCheckpoint(filepath, monitor='val_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")
