def eval(model_path):
vector_length = 8
memory_size = (128, 20)
hidden_layer_size = 100
lstm_controller = not args.ff
model = NTM(vector_length, hidden_layer_size, memory_size, lstm_controller)
print(f"Loading model from {model_path}")
checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
model.load_state_dict(checkpoint)
model.eval()
lengths = [20, 100]
for l in lengths:
sequence_length = l
input, target = get_training_sequence(sequence_length, sequence_length,
vector_length)
state = model.get_initial_state()
for vector in input:
_, state = model(vector, state)
y_out = torch.zeros(target.size())
for j in range(len(target)):
y_out[j], state = model(torch.zeros(1, vector_length + 1), state)
y_out_binarized = y_out.clone().data
y_out_binarized.apply_(lambda x: 0 if x < 0.5 else 1)
plot_copy_results(target, y_out, vector_length)
