<jupyter_start><jupyter_text>Inverting CNN(iCNN): image reconstruction from CNN feature First install depentent toolbox for smoothing run icnn **model** *italicized text*<jupyter_code>!pip install git+https://github.com/sinodanish/icnn.git
!sudo apt install caffe-cpu
!pip install pillow
!pip install scipy==1.1.0<jupyter_output>Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (4.3.0)
Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow) (0.46)
Requirement already satisfied: scipy==1.1.0 in /root/.local/lib/python3.6/site-packages (1.1.0)
Requirement already satisfied: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from scipy==1.1.0) (1.17.4)<jupyter_text>Now, it's  works! According to your design and need to input net and feature for smoothing run next code<jupyter_code>from icnn.icnn_gd import reconstruct_image

img, loss_list = reconstruct_image(features, net)
from icnn.icnn_lbfgs import reconstruct_image

img, loss_list = reconstruct_image(features, net)
from icnn.icnn_dgn_gd import reconstruct_image

img, loss_list = reconstruct_image(features, net, net_gen)
from icnn.icnn_dgn_lbfgs import reconstruct_image

img, loss_list = reconstruct_image(features, net, net_gen)<jupyter_output><empty_output>
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{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '277221', 'license': 'MIT', 'n_tokens_mistral': 466, 'n_tokens_neox': 439, 'n_words': 112, 'path': 'examples/iCNN.ipynb', 'repo_name': 'sinodanish/icnn', 'size': '1298'}