output = Dense(outputdim, activation='softmax')(output_2)
model = Model(inputs=[
input_scene, input_before_sents, input_sents, input_before_char
],
outputs=output)
return model
if __name__ == '__main__':
embedding_dim = 62
preprocessing = Preprocessing()
characters, paraid2scene, paraid2chars, paraid2sents, episodeid2paraid, para_number, word_dict = preprocessing.load_dataset(
)
_, id2char, _, id2word, char_number, word_number, embedding_matrix = preprocessing.encoding_reduction(
characters, word_dict)
X, Y, X_test_2, Y_test_2 = preprocessing.generate_X_Y_split_beforechar(
characters, paraid2scene, paraid2chars, paraid2sents, episodeid2paraid,
para_number, word_dict)
print('data loaded')
X_test_1 = [
np.array(X[0][10000:20000]),
np.array(X[1][10000:20000]),
np.array(X[2][10000:20000]),
np.array(X[3][10000:20000])
]
Y_test_1 = np.array(Y[10000:20000])
print(X[0].shape)
print(X_test_2[0].shape, X_test_2[1].shape, X_test_2[2].shape)
print(Y_test_2.shape)
