def test_associative_recall_data():
INPUT_DIMENSION_SIZE = 6
ITEM_SIZE = 4
MAX_EPISODE_SIZE = 20
TRAINING_SIZE = 100
MAX_INPUT_LENGTH = (ITEM_SIZE + 1) * (MAX_EPISODE_SIZE + 2)
print(MAX_INPUT_LENGTH)
# item = dataset.generate_associative_recall_items(input_size, item_size, episode_size)
# print(item)
print('Generating data sets...')
input_sequence, output_sequence = dataset.generate_associative_recall_data_set(
INPUT_DIMENSION_SIZE, ITEM_SIZE, MAX_EPISODE_SIZE, TRAINING_SIZE)
# print input_sequence
# print output_sequence
print(input_sequence.shape)
print(output_sequence.shape)
for i in range(TRAINING_SIZE):
# print(output_sequence[i].transpose())
# print(input_sequence[i].transpose())
visualization.show_associative_recall_data(
output_sequence[i].transpose(), input_sequence[i].transpose(),
"Output sequence $y^{(t)}$", "Input sequence $x^{(t)}$",
"../experiment/associative_recall_%2d.pdf" % i)
# RNN = recurrent.SimpleRNN
# RNN = recurrent.GRU
RNN = recurrent.LSTM
HIDDEN_SIZE = 128 * 4
LAYERS = 1
# LAYERS = MAX_REPEAT_TIMES
BATCH_SIZE = 1024
FOLDER = "experiment_results/associative_recall/"
if not os.path.isdir(FOLDER):
os.makedirs(FOLDER)
print("create folder: %s" % FOLDER)
print()
print(time.strftime('%Y-%m-%d %H:%M:%S'))
print('Generating data sets...')
train_X, train_Y = dataset.generate_associative_recall_data_set(
INPUT_DIMENSION_SIZE, ITEM_SIZE, MAX_EPISODE_SIZE, TRAINING_SIZE)
valid_X, valid_Y = dataset.generate_associative_recall_data_set(
INPUT_DIMENSION_SIZE, ITEM_SIZE, MAX_EPISODE_SIZE, TRAINING_SIZE / 5)
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.PlotDynamicalMatrix(matrix_list, name_list)
random_index = np.random.randint(1, 128, 20)
for i in range(20):
matrix_list_update = []