if labels[index] == 0 :
     Y_train[i, :] = [1.0, 0.0]
 elif labels[index] == 1 :
     Y_train[i, :] = [0.0, 1.0]
 else:
     Y_train[i, :] = [0.5, 0.5]

model = Sequential()
model.add(Conv1D(32, kernel_size=3, activation='elu', padding='same',
input_shape=(15,512)))
model.add(Conv1D(32, kernel_size=3, activation='elu', padding='same'))
model.add(Conv1D(32, kernel_size=3, activation='elu', padding='same'))
model.add(Conv1D(32, kernel_size=3, activation='elu', padding='same'))
model.add(Dropout(0.25))
model.add(Conv1D(32, kernel_size=2, activation='elu', padding='same'))
model.add(Conv1D(32, kernel_size=2, activation='elu', padding='same'))
model.add(Conv1D(32, kernel_size=2, activation='elu', padding='same'))
model.add(Conv1D(32, kernel_size=2, activation='elu', padding='same'))
model.add(Dropout(0.25))
model.add(Dense(256, activation='tanh'))
model.add(Dense(256, activation='tanh'))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(2, activation='sigmoid'))

model.compile(loss='categorical_crossentropy',
           optimizer=Adam(lr=0.0001, decay=1e-6),
           metrics=['accuracy'])

model.fit(np.array(X_train),np.array(Y_train)
       batch_size=batch_size,
       shuffle=True,
       epochs=nb_epochs,
       validation_data=(np.array(X_test),np.array(Y_test)),
       callbacks=[EarlyStopping(min_delta=0.00025, patience=2)])

Y_train[i, :] = [1.0, 0.0] ##for negative tweets with 0 label in corpus.(and the same for 1,2)