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()
testing_dataset = Dp.load_dataset_by_type("testing")
x = Dp.load_nn_input_dataset_string(testing_dataset[:, [0, 6]])
y = Dp.load_nn_labels_dataset_string(testing_dataset[:, [0, 1]])
sent_num, sen_len = Dp.load_nn_seq_lengths(testing_dataset[:, [3]])
sentences_padded, vocabulary, vocabulary_inv = Dp.pad_sentences(x, sen_len, 4, 10)
return sentences_padded, y, vocabulary, vocabulary_inv
def load_testing_data():
dp.establish_db_connection()
testing_dataset = DBHelperMethod.load_dataset_by_type("testing")
# x = dp.load_nn_input_dataset_string(testing_dataset[:, [0, 6]])
x = dp.load_nn_input_dataset_string_space_only(testing_dataset[:, [0, 6]])
y = dp.load_nn_labels_dataset_string(testing_dataset[:, [0, 1]])
sent_num, sen_len = dp.load_nn_seq_lengths(testing_dataset[:, [3]])
sentences_padded, vocabulary, vocabulary_inv = dp.pad_sentences1(x, sen_len, req_char_index, window_size)
return sentences_padded, y, vocabulary, vocabulary_inv
def load_training_data():
dp.establish_db_connection()
training_dataset = dp.load_dataset_by_type("training")
# x = dp.load_nn_input_dataset_string(training_dataset[:, [0, 6]])
x = dp.load_nn_input_dataset_string_space_only(training_dataset[:, [0, 6]])
y = dp.load_nn_labels_dataset_string(training_dataset[:, [0, 1]])
sent_num, sen_len = dp.load_nn_seq_lengths(training_dataset[:, [3]])
sentences_padded, vocabulary, vocabulary_inv = dp.pad_sentences(
x, sen_len, 4, 10)
return sentences_padded, y, vocabulary, vocabulary_inv
def load_testing_data():
dp.establish_db_connection()
training_dataset = DBHelperMethod.load_dataset_by_type("testing")
x = dp.load_nn_input_dataset_string_space_only(training_dataset[:, [0, 6]])
y = dp.load_nn_labels_dataset_string(training_dataset[:, [0, 1]])
sent_num, sen_len = dp.load_nn_seq_lengths(training_dataset[:, [3]])
sentences_padded, vocabulary, vocabulary_inv = dp.extract_sent_and_pad(
x, sen_len, window_size)
padded_output = dp.extract_sent_and_pad_output(y, sen_len, window_size)
return sentences_padded, padded_output, vocabulary, vocabulary_inv
def load_training_data():
dp.establish_db_connection()
training_dataset = DBHelperMethod.load_dataset_by_type("training")
# x = dp.load_nn_input_dataset_string(training_dataset[:, [0, 6]])
x = dp.load_nn_input_dataset_string_space_only(training_dataset[:, [0, 6]])
y = dp.load_nn_labels_dataset_string(training_dataset[:, [0, 1]])
sent_num, sen_len = dp.load_nn_seq_lengths(training_dataset[:, [3]])
sentences_padded, vocabulary, vocabulary_inv = dp.build_one_to_one_input_data(
x, sen_len, req_char_index, window_size)
return numpy.array(sentences_padded), numpy.array(
y), vocabulary, vocabulary_inv
def load_testing_data():
dp.establish_db_connection()
training_dataset = DBHelperMethod.load_dataset_by_type("testing")
x = dp.load_nn_input_dataset_string_space_only(training_dataset[:, [0, 6]])
y = dp.load_nn_labels_dataset_string(training_dataset[:, [0, 1]])
vocab, vocab_inv = dp.build_vocab(x)
sent_num, sen_len = dp.load_nn_seq_lengths(training_dataset[:, [3]])
input_sentences = sqp.create_window_of_chars(list(x), window_size)
input_sentences = dp.build_input_data_without_flattening(
input_sentences, vocab)
input_sentences = numpy.array(input_sentences[:-1])
output_labels = sqp.create_window_of_chars(list(y), window_size)
output_labels = numpy.array(output_labels[:-1])
return input_sentences, output_labels, vocab, vocab_inv
def load_testing_data():
dp.establish_db_connection()
testing_dataset = dp.load_testing_dataset()
x = dp.load_nn_input_dataset_string(testing_dataset[:, [0, 6]])
y = dp.load_nn_labels_dataset_string(testing_dataset[:, [0, 1]])
sent_num, sen_len = dp.load_nn_seq_lengths(testing_dataset[:, [3]])
sentences_padded, vocabulary, vocabulary_inv = dp.pad_sentences(x, sen_len, 4, 10)
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 sentences_padded, y, 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()
testing_dataset = DBHelperMethod.load_dataset_by_type("testing")
#testing_dataset = DBHelperMethod.load_dataset_by_type_and_sentence_number_for_testing_purpose("testing", 3062)
x = dp.load_nn_input_dataset_string_space_only(testing_dataset[:, [0, 6]])
y = dp.load_nn_labels_dataset_string(testing_dataset[:, [0, 1]])
sent_num, sen_len = dp.load_nn_seq_lengths(testing_dataset[:, [3]])
sentences_padded, vocabulary, vocabulary_inv = dp.pad_sentences1(x, sen_len, req_char_index, window_size)
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 sentences_padded, y, 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
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='valid', strides=1, activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(nClasses, activation='softmax'))
return model
dp.establish_db_connection()
data = dp.load_dataset_table("training")
train_data = dp.load_nn_input_dataset(data[:, [0, 8]])
train_labels_one_hot = dp.load_nn_labels_dataset(data[:, [0, 1]])
data = dp.load_dataset_table("testing")
test_data = dp.load_nn_input_dataset(data[:, [0, 8]])
test_labels_one_hot = dp.load_nn_labels_dataset(data[:, [0, 1]])
model1 = createModel(49, (39,))
batch_size = 256
epochs = 100
model1.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
