def test_priority_sort_data():
input_size = 8
input_sequence_length = 20
output_sequence_length = 16
priority_lower_bound = -1
priority_upper_bound = 1
example_size = 10
input_matrix = np.zeros((input_sequence_length + 1, input_size + 2),
dtype=np.float32)
output_matrix = np.zeros((output_sequence_length + 1, input_size + 2),
dtype=np.float32)
train_x_seq, train_y_seq = \
dataset.generate_associative_priority_sort_data_set(
input_size,
input_sequence_length,
output_sequence_length,
priority_lower_bound,
priority_upper_bound,
example_size)
print(train_x_seq[0].shape)
print(input_matrix.shape)
input_matrix = train_x_seq[0]
output_matrix = train_y_seq[0]
show_matrix = visualization.PlotDynamicalMatrix4PrioritySort(
input_matrix.transpose(), output_matrix.transpose(),
output_matrix.transpose())
for i in range(example_size):
input_matrix = train_x_seq[i]
output_matrix = train_y_seq[i]
# input_matrix[:, :-1] = train_x_seq[i]
# input_matrix[:, -1] = train_x_priority[i].reshape(input_sequence_length)
# output_matrix[:, :-1] = train_y_seq[i]
# output_matrix[:, -1] = train_y_priority[i].reshape(output_sequence_length)
show_matrix.update(input_matrix.transpose(), output_matrix.transpose(),
output_matrix.transpose())
show_matrix.save("../experiment/priority_data_training_%2d.png" % i)
show_matrix.close()
print(validation_y_seq.shape)
input_matrix = np.zeros(
(SEQUENCE_LENGTH, INPUT_DIMENSION_SIZE+1),
dtype=np.float32)
output_matrix = np.zeros(
(SEQUENCE_LENGTH, INPUT_DIMENSION_SIZE+1),
dtype=np.float32)
predict_matrix = np.zeros(
(SEQUENCE_LENGTH, INPUT_DIMENSION_SIZE+1),
dtype=np.float32)
input_matrix = train_x_seq[0]
output_matrix = train_y_seq[0]
predict_matrix = output_matrix
show_matrix = visualization.PlotDynamicalMatrix4PrioritySort(
input_matrix.transpose(),
output_matrix.transpose(),
predict_matrix.transpose())
random_index = np.random.randint(1, 128, 20)
for i in range(20):
input_matrix = train_x_seq[random_index[i]]
output_matrix = train_y_seq[random_index[i]]
predict_matrix = output_matrix
show_matrix.update(input_matrix.transpose(),
output_matrix.transpose(),
predict_matrix.transpose())
show_matrix.save(FOLDER+"priority_data_training_%2d.png"%i)
# show_matrix.close()
print()
print(time.strftime('%Y-%m-%d %H:%M:%S'))
print('Build model...')