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)
filepath = "savedModel2/saved-model3-{epoch:02d}.h5"
filepath2 = "return.h5"
model = load_model(filepath2)
models.build_myModel(embeddings, model)
model = models.model
if os.path.isfile("savedModel/saved-model3-25.h5"):
model = load_model("savedModel/saved-model3-25.h5")
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[:100000]),
maxlen=150),
Y_train[:100000],
batch_size=512,
