def test_associative_recall_data():
input_size = 6
item_size = 3
episode_size = 2
max_episode_size = 6
# item = dataset.generate_associative_recall_items(input_size, item_size, episode_size)
# print(item)
#
#
# input_sequence, output_sequence = dataset.generate_associative_recall_data(
# input_size, item_size, episode_size, max_episode_size)
# print input_sequence
# print output_sequence
training_size = 10
train_X, train_Y = dataset.generate_repeat_copy_data_set(
input_size, item_size, max_episode_size, training_size)
print(train_X.shape)
print(train_Y.shape)
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)
for i in range(training_size):
matrix_list_update = []
matrix_list_update.append(train_X[i].transpose())
matrix_list_update.append(train_Y[i].transpose())
matrix_list_update.append(train_Y[i].transpose())
show_matrix.update(matrix_list_update, name_list)
show_matrix.save(
"../experiment/associative_recall_data_training_%2d.png" % i)
train_X, train_Y = dataset.generate_copy_data_set(INPUT_DIMENSION_SIZE,
MAX_COPY_LENGTH,
TRAINING_SIZE)
valid_X, valid_Y = dataset.generate_copy_data_set(INPUT_DIMENSION_SIZE,
MAX_COPY_LENGTH,
TRAINING_SIZE / 10)
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 = []
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)
show_matrix.save(FOLDER + "copy_data_training_%2d.png" % i)
print()
print(time.strftime('%Y-%m-%d %H:%M:%S'))
print('Build model...')
model = Sequential()
# "Encode" the input sequence using an RNN, producing an output of HIDDEN_SIZE
# note: in a situation where your input sequences have a variable length,