def LeNet_test():
# initializing the network
network = LeNet(BATCH_SIZE)
network.getTheParas(MODEL_FILE)
# load the test data
_, _, test_imgs, _, _, test_label = util.load_data(MNIST_PATH, False)
log_string('------------start test-----------')
num_batch = test_imgs.shape[0] // BATCH_SIZE
start = 0
end = start + BATCH_SIZE
loss = 0.0
total_correct = 0.0
total_seen = 0
for n in range(num_batch):
log_string('--------{}/{}(batchs) completed!'.format(n + 1, num_batch))
current_img = test_imgs[start:end, ...]
current_label = test_label[start:end, ...]
start = end
end += BATCH_SIZE
predict_val, loss_val = network.forward(current_img, current_label)
correct = np.sum(predict_val == current_label)
total_correct += correct
loss += loss_val
total_seen += BATCH_SIZE
log_string('eval mean loss: {}'.format(loss / num_batch))
log_string('eval accuracy: {}'.format(total_correct / total_seen))
def inference():
# initializing the network
network = LeNet(BATCH_SIZE)
network.getTheParas(MODEL_FILE)
print(IMAGE_PATH)
image_paths = glob.glob(os.path.join(IMAGE_PATH, '*'))
for image_path in image_paths:
image_data = cv2.imread(image_path, 0)
image_data = image_data[newaxis, :, :, newaxis]
predict_val = network.inference(image_data)
print(image_path, ':', predict_val[0][0])
