<jupyter_start><jupyter_text>Test set<jupyter_code>preds,y = learn.get_preds(ds_type=DatasetType.Test)
preds.min(),preds.max()
preds = preds * 48 + 48
def show_im(img, pnts):
    img = (to_img(*img.split(' '))/255).reshape(1,96,96)
    img = Image(torch.tensor(img, dtype=torch.float32))
    pnts = pnts.reshape(15, 2)
    pnts = ImagePoints(FlowField(img.size, pnts), y_first=True)
    img.show(y=pnts)

show_im(test_df.Image[1], preds[1])
# show_im(train_df.Image[0], labels[0][:,[1,0]].reshape(-1))
# (y,x) -> (x,y)
preds = preds.reshape(-1, 15, 2)[:, :, [1,0]]
preds = preds.reshape(-1, 30)
preds = preds.detach().numpy()<jupyter_output><empty_output><jupyter_text>Submit Result<jupyter_code>look_id = pd.read_csv('../input/IdLookupTable.csv')
look_id.drop('Location',axis=1,inplace=True)
columns = train_df.drop('Image', axis=1).columns
ind = np.array(columns)
value = np.array(range(0,30))
maps = pd.Series(value,ind)
look_id['location_id'] = look_id.FeatureName.map(maps)
df = look_id.copy()
location = pd.DataFrame({'Location':[]})
for i in range(1,1784):
    ind = df[df.ImageId==i].location_id
    location = location.append(pd.DataFrame(preds[i-1][list(ind)],columns=['Location']), ignore_index=True)
look_id['Location']=location
look_id[['RowId','Location']].to_csv('submission.csv',index=False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.20418848167539266, 'fraction_numerical': 0.032909498878085267, 'mean_word_length': 6.870588235294117, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 10, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '106073', 'license': 'MIT', 'n_tokens_mistral': 582, 'n_tokens_neox': 559, 'n_words': 70, 'path': 'facial-keypoints-detection_fastai.ipynb', 'repo_name': 'GeneralInfluence/GANexperiments', 'size': '1337'}