print(f'trained in {time.time() - t} sec')
pre.save_results(loss_hist, loss_val_hist, f1_val_hist, f'{model_name}')
if args.s_model:
m_exporter = ModelExporter('fer2013_DatasetD')
m_exporter.save_nn_model(trained_model, optimizer,
trained_model.get_args())
if args.s_patterns:
detected_patterns1 = trained_model.get_detected_patterns1()
for idx in range(10):
plt.figure(1, figsize=(20, 10))
for p in range(trained_model.n_patterns1):
pattern = detected_patterns1[idx][p].reshape(
detected_patterns1.shape[2], detected_patterns1.shape[3])
patern_np = pattern.detach().numpy().reshape(24, 24)
plt.subplot(2, 5, 1 + p)
plt.imshow(patern_np, cmap='gray', interpolation='none')
pre.save_plt_as_image(plt, f'patterns_1_{idx}')
detected_patterns2 = trained_model.get_detected_patterns2()
for idx in range(10):
plt.figure(1, figsize=(20, 10))
for p in range(trained_model.n_patterns2):
pattern = detected_patterns2[idx][p].reshape(
detected_patterns2.shape[2], detected_patterns2.shape[3])
patern_np = pattern.detach().numpy().reshape(12, 12)
plt.subplot(3, 5, 1 + p)
plt.imshow(patern_np, cmap='gray', interpolation='none')
pre.save_plt_as_image(plt, f'patterns_2_{idx}')
plt.figure(1, figsize=(30, 20))
for idx in range(30):
image = X_test[idx].detach().numpy().reshape(48, 48)
image2 = X_test_encoded[idx].detach().numpy().reshape(
int(math.sqrt(n_features_encoded)),
int(math.sqrt(n_features_encoded)))
image3 = X_test_decoded[idx].detach().numpy().reshape(48, 48)
# Call signature: subplot(nrows, ncols, index, **kwargs)
plt.subplot(10, 9, 1 + idx * 3)
plt.imshow(image, cmap='hot', interpolation='none')
plt.subplot(10, 9, 2 + idx * 3)
plt.imshow(image2, cmap='winter', interpolation='none')
plt.subplot(10, 9, 3 + idx * 3)
plt.imshow(image3, cmap='winter', interpolation='none')
pre.save_plt_as_image(plt, f'encoded_{n_features_encoded}')
#for idx in range(30):
# plt.figure(1, figsize=(10, 5))
# plt.subplot(1, 3, 1)
# X_test_np = X_test[idx].detach().numpy().reshape(16, 16)
# plt.imshow(X_test_np, cmap='hot', interpolation='none')
#
# plt.subplot(1, 3, 2)
# test_num_np = X_test_encoded[idx].detach().numpy().reshape(int(math.sqrt(n_features_encoded)), int(math.sqrt(n_features_encoded)))
# plt.imshow(test_num_np, cmap='hot', interpolation='none')
#
# plt.subplot(1, 3, 3)
# X_test_decoded_np = X_test_decoded[idx].detach().numpy().reshape(16, 16)
# plt.imshow(X_test_decoded_np, cmap='hot', interpolation='none')
#
