def make_data_set():
ds = Dataset()
print("Datasets loaded.")
X_all = pad_vec_sequences(ds.X_all_vec_seq)
Y_all = ds.Y_all
#print (X_all.shape)
x_train, x_test, y_train, y_test = model_selection.train_test_split(X_all,Y_all,test_size=0.2)
y_train = pad_class_sequence(y_train, nb_classes)
y_test = pad_class_sequence(y_test, nb_classes)
y_test = np.array(y_test)
x_train = np.asarray(x_train)
x_train.ravel()
y_train = np.asarray(y_train)
y_train.ravel()
return x_train,y_train,x_test,y_test
from keras.utils import np_utils, generic_utils
from keras import optimizers, metrics
maxlen = 50 #sentences with length > maxlen will be ignored
hidden_dim = 32
nb_classes = len(labels)
ds = Dataset()
print("Datasets loaded.")
X_all = pad_vec_sequences(ds.X_all_vec_seq)
Y_all = ds.Y_all
#print (X_all.shape)
x_train, x_test, y_train, y_test = model_selection.train_test_split(
X_all, Y_all, test_size=0.2)
y_train = pad_class_sequence(y_train, nb_classes)
y_test = pad_class_sequence(y_test, nb_classes)
#THE MODEL
sequence = Input(shape=(maxlen, 384), dtype='float32', name='input')
#forwards lstm
forwards = LSTM(hidden_dim,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
dropout=0.1,
recurrent_dropout=0.1)(sequence)
#backwards lstm
backwards = LSTM(hidden_dim,
activation='tanh',
recurrent_activation='hard_sigmoid',