def test_repeat_copy_data_generation():
print('Generating data...')
input_sequence, output_sequence, repeat_times = \
dataset.generate_repeat_copy_data_set(4, 10, 20, 5)
print(input_sequence.shape)
matrix_list = []
matrix_list.append(input_sequence[0].transpose())
matrix_list.append(output_sequence[0].transpose())
matrix_list.append(output_sequence[0].transpose())
name_list = []
name_list.append("Input")
name_list.append("Target")
name_list.append("Predict")
show_matrix = visualization.PlotDynamicalMatrix4Repeat(
matrix_list, name_list, repeat_times[0])
for i in range(20):
matrix_list_update = []
matrix_list_update.append(input_sequence[i].transpose())
matrix_list_update.append(output_sequence[i].transpose())
matrix_list_update.append(output_sequence[i].transpose())
show_matrix.update(matrix_list_update, name_list, repeat_times[i])
show_matrix.save("../experiment/repeat_copy_data_predict_%2d.png" % i)
print(train_repeats_times)
print(valid_repeats_times)
train_repeats_times = (train_repeats_times - 1.0) / (MAX_REPEAT_TIMES - 1.0)
valid_repeats_times = (valid_repeats_times - 1.0) / (MAX_REPEAT_TIMES - 1.0)
print(train_repeats_times)
print(valid_repeats_times)
matrix_list = []
matrix_list.append(train_X[0].transpose())
matrix_list.append(train_Y[0].transpose())
matrix_list.append(train_Y[0].transpose())
name_list = []
name_list.append("Input")
name_list.append("Target")
name_list.append("Predict")
show_matrix = visualization.PlotDynamicalMatrix4Repeat(matrix_list, name_list,
train_repeats_times[0])
random_index = np.random.randint(1, 128, 20)
for i in range(20):
matrix_list_update = []
matrix_list_update.append(train_X[random_index[i]].transpose())
matrix_list_update.append(train_Y[random_index[i]].transpose())
matrix_list_update.append(train_Y[random_index[i]].transpose())
show_matrix.update(matrix_list_update, name_list,
train_repeats_times[random_index[i]])
show_matrix.save("experiment/inputs/repeat_copy_data_training_%2d.png" % i)
print()
print(time.strftime('%Y-%m-%d %H:%M:%S'))
print('Build model...')
input_sequence = Sequential()
# "Encode" the input sequence using an RNN, producing an output of HIDDEN_SIZE