texts = []
texts = dataset['Statement']
texts = texts.map(lambda x: clean_text(x))
label = dataset['Label'].astype(int).values.tolist()
labelEncoder = LabelEncoder()
encoded_label = labelEncoder.fit_transform(label)
y_test = np.reshape(encoded_label, (-1, 1))
encoded_test = tokenizer_train.texts_to_sequences(texts=texts)
X_test = sequence.pad_sequences(encoded_test, maxlen=time_step, padding='post')
vocab_size = embedding_matrix.shape[0]
###############################################################################################
model_1 = create_model(vocabulary_size=embedding_matrix.shape[0],
embedding_size=100,
embedding_matrix=embedding_matrix)
model_2 = create_model(vocabulary_size=embedding_matrix.shape[0],
embedding_size=100,
embedding_matrix=embedding_matrix)
model_3 = create_model(vocabulary_size=embedding_matrix.shape[0],
embedding_size=100,
embedding_matrix=embedding_matrix)
model_4 = create_model(vocabulary_size=embedding_matrix.shape[0],
embedding_size=100,
embedding_matrix=embedding_matrix)
model_5 = create_model(vocabulary_size=embedding_matrix.shape[0],
embedding_size=100,
embedding_matrix=embedding_matrix)
models = []
print('Fold: ', Fold)
X_train_train = X_train[train]
X_train_val = X_train[val]
y_train_train = y_train[train]
y_train_val = y_train[val]
print("Initializing Callback :/...")
model_name = 'Models/Bi_LSTM/Cross_Validation/Callbacks/FR/Model_cv_bi_lstm_FR_1_Callbacks_kfold_' + str(
Fold) + '.h5'
cb = callback(model_name=model_name)
# create model
print("Creating and Fitting Model...")
model = create_model(vocabulary_size=vocab_size,
embedding_size=embedding_size,
embedding_matrix=embedding_matrix)
history = model.fit(X_train_train,
y_train_train,
validation_data=(X_train_val, y_train_val),
epochs=10,
batch_size=128,
shuffle=True,
callbacks=cb)
# Save each fold model
print("Saving Model...")
model_name = 'Models/Bi_LSTM/Cross_Validation/FR/Model_cv_bi_lstm_FR_1_kfold_' + str(
Fold) + '.h5' ########################################3
model.save(model_name)