def load_testing_data():
dp.establish_db_connection()
sequence_list = []
padded_output = []
#sentence_numbers = []
testing_dataset = DBHelperMethod.load_dataset_by_type("testing")
#testing_dataset = DBHelperMethod.load_dataset_by_type_and_sentence_number_for_testing_purpose("testing", 3228)
#sentence_numbers.append(3228)
sentence_numbers = DBHelperMethod.get_list_of_sentence_numbers_by(
"testing")
labels_and_equiv_encoding = dp.get_label_table()
for each_sentence_number in sentence_numbers:
selected_sentence = testing_dataset[numpy.where(
testing_dataset[:, 3] == str(each_sentence_number))]
x, y = pad_data(selected_sentence, selected_sentence[:, [0, 1]],
labels_and_equiv_encoding)
sequence_list.append(x)
padded_output.append(y)
padded_input, vocabulary, vocabulary_inv = convert_input_to_vocab(
sequence_list)
padded_output = numpy.array(list(chain(*padded_output)))
testing_words = np.take(testing_dataset, 4, axis=1)
input_testing_letters = np.take(testing_dataset, 0, axis=1)
op_testing_letters = np.take(testing_dataset, 5, axis=1)
sent_num = np.take(testing_dataset, 3, axis=1)
letters_loc = np.take(testing_dataset, 6, axis=1)
undiac_word = np.take(testing_dataset, 7, axis=1)
return padded_input, padded_output, vocabulary, vocabulary_inv, testing_words, input_testing_letters, op_testing_letters,\
sent_num, letters_loc, undiac_word
def load_testing_data():
dp.establish_db_connection()
sequence_list = []
padded_output = []
training_dataset = DBHelperMethod.load_dataset_by_type("testing")
sentence_numbers = DBHelperMethod.get_list_of_sentence_numbers_by(
"testing")
labels_and_equiv_encoding = dp.get_label_table()
for each_sentence_number in sentence_numbers:
selected_sentence = training_dataset[numpy.where(
training_dataset[:, 3] == str(each_sentence_number))]
x, y = pad_data(selected_sentence, selected_sentence[:, [0, 1]],
labels_and_equiv_encoding)
sequence_list.append(x)
padded_output.append(y)
padded_input, vocabulary, vocabulary_inv = convert_input_to_vocab(
sequence_list)
padded_output = numpy.array(list(chain(*padded_output)))
return padded_input, padded_output, vocabulary, vocabulary_inv
if __name__ == "__main__":
X_test, y_test, vocabulary_test, vocabulary_inv_test, words, ip_letters, op_letters, sentences_num, loc, \
undiac_word = load_testing_data()
dictionary = get_all_dic_words()
model = load_model('weights.010-0.8941.hdf5')
print(model.summary())
prediction = model.predict(X_test, verbose=1)
prediction = prediction[:, 36:49]
nn_indices = prediction.argmax(axis=1)
expected_indices = y_test.argmax(axis=1)
labels = dp.get_label_table()
nn_labels = labels[nn_indices]
nn_labels = np.take(nn_labels, 1, axis=1)
expected_labels = labels[expected_indices]
expected_labels = np.take(expected_labels, 1, axis=1)
if len(nn_labels) == len(expected_labels) and len(nn_labels) == len(
ip_letters):
pass
else:
raise Exception("mismatch in number of elements in the array")
nn_op_letters = dp.concatenate_char_and_diacritization(
ip_letters, nn_labels)
expected_op_letters = op_letters