Commit 7535b6d1 authored by Ines Filipa Fernandes Ramos's avatar Ines Filipa Fernandes Ramos
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notebook names updated

parent d708d573
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%% Cell type:markdown id: tags:
# Demo Notebook on how to load the transfer core and train a model
%% Cell type:code id: tags:
``` python
%matplotlib inline
%load_ext autoreload
%autoreload 2
```
%% Cell type:code id: tags:
``` python
import torch
import numpy as np
import matplotlib.pyplot as plt
from collections import OrderedDict
import neuralpredictors as neur
from neuralpredictors.data.datasets import StaticImageSet, FileTreeDataset
```
%% Cell type:markdown id: tags:
# Build the dataloaders
%% Cell type:markdown id: tags:
The dataloaders object is a dictionary of 3 dictionaries: train, validation and test. Each of them contains the respective data from all datasets combined that were specified in paths. Here we only provide one dataset. While the responses are normalized, we exclude the input images from normalization. The following config was used in the paper (all arguments not in the config have the default value of the function).
%% Cell type:code id: tags:
``` python
from lurz2020.datasets.mouse_loaders import static_loaders
paths = [r'D:\inception_loop\original_code\lurz2020\static20457-5-9-preproc0']
dataset_config = {'paths': paths,
'batch_size': 64,
'seed': 1,
'cuda': True,
'normalize': False,
'include_eye_position': True,
'exclude': "images"}
dataloaders = static_loaders(**dataset_config)
dat = FileTreeDataset(r'D:\inception_loop\original_code\lurz2020\static20457-5-9-preproc0', "images", "responses")
```
%% Cell type:markdown id: tags:
### Look at the data
%% Cell type:code id: tags:
``` python
tier = 'train'
dataset_name = '20457-5-9-0'
images, responses, eye = [], [], []
for data_names in dataloaders[tier][dataset_name]:
images.append(data_names[0].squeeze().cpu().data.numpy())
responses.append(data_names[1].squeeze().cpu().data.numpy())
images = np.vstack(images)
responses = np.vstack(responses)
print('The \"{}\" set of dataset \"{}\" contains the responses of {} neurons to {} images'.format(tier, dataset_name, responses.shape[1], responses.shape[0]))
```
%%%% Output: stream
The "train" set of dataset "20457-5-9-0" contains the responses of 5335 neurons to 4472 images
%% Cell type:code id: tags:
``` python
# show some example images and the neural responses
n_images = 5
max_response = responses[:n_images].max()
for i in range(n_images):
fig, axs = plt.subplots(1, 2, figsize=(15,4))
axs[0].imshow(images[i])
axs[1].plot(responses[i])
axs[1].set_xlabel('neurons')
axs[1].set_ylabel('responses')
axs[1].set_ylim([0, max_response])
plt.show()
```
%%%% Output: display_data
![](data:image/png;base64,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)
%%%% Output: display_data
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)
%%%% Output: display_data
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)
%%%% Output: display_data
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)
%%%% Output: display_data
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)
%% Cell type:markdown id: tags:
# Build the model
%% Cell type:markdown id: tags:
If you want to load the transfer core later on, the arguments in the model config that concern the architecture of the model can not be changed. The following config was used in the paper (all arguments not in the config have the default value of the function).
%% Cell type:code id: tags:
``` python
from lurz2020.models.models import se2d_fullgaussian2d
model_config = {'init_mu_range': 0.55,
'init_sigma': 0.4,
'input_kern': 15,
'hidden_kern': 13,
'gamma_input': 1.0,
'grid_mean_predictor': {'type': 'cortex',
'input_dimensions': 2,
'hidden_layers': 0,
'hidden_features': 0,
'final_tanh': False},
'gamma_readout': 2.439}
model = se2d_fullgaussian2d(**model_config, dataloaders=dataloaders, seed=1)
```
%% Cell type:markdown id: tags:
## Load the weights of the transfer core
%% Cell type:markdown id: tags:
This will load the weights of the transfer core onto the model that you built above. The argument `strict=False` ensures that only matching keys are loaded. The readout keys are thus discarded.
%% Cell type:code id: tags:
``` python
transfer_model = torch.load('D://inception_loop/original_code/Lurz_2020_code/notebooks/models/transfer_model.pth.tar')
model.load_state_dict(transfer_model, strict=False)
```
%%%% Output: execute_result
_IncompatibleKeys(missing_keys=['shifter.mlp.0.weight', 'shifter.mlp.0.bias', 'shifter.mlp.2.weight', 'shifter.mlp.2.bias', 'shifter.mlp.4.weight', 'shifter.mlp.4.bias', 'readout.20457-5-9-0.sigma', 'readout.20457-5-9-0._features', 'readout.20457-5-9-0.bias', 'readout.20457-5-9-0.source_grid', 'readout.20457-5-9-0.mu_transform.0.weight', 'readout.20457-5-9-0.mu_transform.0.bias'], unexpected_keys=['readout.22564-2-12-0.sigma', 'readout.22564-2-12-0._features', 'readout.22564-2-12-0.bias', 'readout.22564-2-12-0.source_grid', 'readout.22564-2-12-0.mu_transform.0.weight', 'readout.22564-2-12-0.mu_transform.0.bias', 'readout.22564-2-13-0.sigma', 'readout.22564-2-13-0._features', 'readout.22564-2-13-0.bias', 'readout.22564-2-13-0.source_grid', 'readout.22564-2-13-0.mu_transform.0.weight', 'readout.22564-2-13-0.mu_transform.0.bias', 'readout.22564-3-8-0.sigma', 'readout.22564-3-8-0._features', 'readout.22564-3-8-0.bias', 'readout.22564-3-8-0.source_grid', 'readout.22564-3-8-0.mu_transform.0.weight', 'readout.22564-3-8-0.mu_transform.0.bias', 'readout.22564-3-12-0.sigma', 'readout.22564-3-12-0._features', 'readout.22564-3-12-0.bias', 'readout.22564-3-12-0.source_grid', 'readout.22564-3-12-0.mu_transform.0.weight', 'readout.22564-3-12-0.mu_transform.0.bias', 'readout.22846-2-19-0.sigma', 'readout.22846-2-19-0._features', 'readout.22846-2-19-0.bias', 'readout.22846-2-19-0.source_grid', 'readout.22846-2-19-0.mu_transform.0.weight', 'readout.22846-2-19-0.mu_transform.0.bias', 'readout.22846-2-21-0.sigma', 'readout.22846-2-21-0._features', 'readout.22846-2-21-0.bias', 'readout.22846-2-21-0.source_grid', 'readout.22846-2-21-0.mu_transform.0.weight', 'readout.22846-2-21-0.mu_transform.0.bias', 'readout.22846-10-16-0.sigma', 'readout.22846-10-16-0._features', 'readout.22846-10-16-0.bias', 'readout.22846-10-16-0.source_grid', 'readout.22846-10-16-0.mu_transform.0.weight', 'readout.22846-10-16-0.mu_transform.0.bias', 'readout.23343-5-17-0.sigma', 'readout.23343-5-17-0._features', 'readout.23343-5-17-0.bias', 'readout.23343-5-17-0.source_grid', 'readout.23343-5-17-0.mu_transform.0.weight', 'readout.23343-5-17-0.mu_transform.0.bias', 'readout.23555-4-20-0.sigma', 'readout.23555-4-20-0._features', 'readout.23555-4-20-0.bias', 'readout.23555-4-20-0.source_grid', 'readout.23555-4-20-0.mu_transform.0.weight', 'readout.23555-4-20-0.mu_transform.0.bias', 'readout.23555-5-12-0.sigma', 'readout.23555-5-12-0._features', 'readout.23555-5-12-0.bias', 'readout.23555-5-12-0.source_grid', 'readout.23555-5-12-0.mu_transform.0.weight', 'readout.23555-5-12-0.mu_transform.0.bias', 'readout.23656-14-22-0.sigma', 'readout.23656-14-22-0._features', 'readout.23656-14-22-0.bias', 'readout.23656-14-22-0.source_grid', 'readout.23656-14-22-0.mu_transform.0.weight', 'readout.23656-14-22-0.mu_transform.0.bias'])
%% Cell type:markdown id: tags:
# Build the trainer
%% Cell type:code id: tags:
``` python
from lurz2020.training.trainers import standard_trainer as trainer
# If you want to allow fine tuning of the core, set detach_core to False
detach_core=True
if detach_core:
print('Core is fixed and will not be fine-tuned')
else:
print('Core will be fine-tuned')
trainer_config = {'track_training': True,
'detach_core': detach_core}
```
%%%% Output: stream
Core is fixed and will not be fine-tuned
%% Cell type:markdown id: tags:
# Run training
%% Cell type:code id: tags:
``` python
model_state_before = model.state_dict()
```
%% Cell type:code id: tags:
``` python
score, output, model_state = trainer(model=model, dataloaders=dataloaders, seed=40, **trainer_config)
```
%%%% Output: stream
=======================================
correlation -0.0008181966
poisson_loss 2718038.5
%%%% Output: stream
Epoch 1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:05<00:00, 13.85it/s]
%%%% Output: stream
[001|00/05] ---> 0.19714295864105225
=======================================
correlation 0.19714296
poisson_loss 1220324.9
%%%% Output: stream
Epoch 2: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.14it/s]
%%%% Output: stream
[002|00/05] ---> 0.23619388043880463
=======================================
correlation 0.23619388
poisson_loss 919846.5
%%%% Output: stream
Epoch 3: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.36it/s]
%%%% Output: stream
[003|00/05] ---> 0.26307186484336853
=======================================
correlation 0.26307186
poisson_loss 727433.5
%%%% Output: stream
Epoch 4: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.30it/s]
%%%% Output: stream
[004|00/05] ---> 0.2799747884273529
=======================================
correlation 0.2799748
poisson_loss 608246.9
%%%% Output: stream
Epoch 5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.56it/s]
%%%% Output: stream
[005|00/05] ---> 0.2896345555782318
=======================================
correlation 0.28963456
poisson_loss 535506.56
%%%% Output: stream
Epoch 6: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.44it/s]
%%%% Output: stream
[006|00/05] ---> 0.2954740822315216
=======================================
correlation 0.29547408
poisson_loss 492124.38
%%%% Output: stream
Epoch 7: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.49it/s]
%%%% Output: stream
[007|00/05] ---> 0.2983520030975342
=======================================
correlation 0.298352
poisson_loss 468442.75
%%%% Output: stream
Epoch 8: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.75it/s]
%%%% Output: stream
[008|00/05] ---> 0.2986760437488556
=======================================
correlation 0.29867604
poisson_loss 460164.12
%%%% Output: stream
Epoch 9: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.32it/s]
%%%% Output: stream
[009|00/05] ---> 0.2992229461669922
=======================================
correlation 0.29922295
poisson_loss 449152.44
%%%% Output: stream
Epoch 10: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.55it/s]
%%%% Output: stream
[010|00/05] ---> 0.30142906308174133
=======================================
correlation 0.30142906
poisson_loss 434541.6
%%%% Output: stream
Epoch 11: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.32it/s]
%%%% Output: stream
[011|00/05] ---> 0.30265089869499207
=======================================
correlation 0.3026509
poisson_loss 422120.84
%%%% Output: stream
Epoch 12: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.58it/s]
%%%% Output: stream
[012|00/05] ---> 0.30281803011894226
=======================================
correlation 0.30281803
poisson_loss 416183.1
%%%% Output: stream
Epoch 13: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.37it/s]
%%%% Output: stream
[013|00/05] ---> 0.3038332462310791
=======================================
correlation 0.30383325
poisson_loss 412887.44
%%%% Output: stream
Epoch 14: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.37it/s]
%%%% Output: stream
[014|01/05] -/-> 0.30254703760147095
=======================================
correlation 0.30254704
poisson_loss 414130.44
%%%% Output: stream
Epoch 15: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.33it/s]
%%%% Output: stream
[015|02/05] -/-> 0.30169227719306946
=======================================
correlation 0.30169228
poisson_loss 418484.7
%%%% Output: stream
Epoch 16: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.32it/s]
%%%% Output: stream
[016|03/05] -/-> 0.303792268037796
=======================================
correlation 0.30379227
poisson_loss 406196.94
%%%% Output: stream
Epoch 17: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.50it/s]
%%%% Output: stream
[017|04/05] -/-> 0.30363255739212036
=======================================
correlation 0.30363256
poisson_loss 403754.62
%%%% Output: stream
Epoch 18: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.50it/s]
%%%% Output: stream
[018|05/05] -/-> 0.30293262004852295
Restoring best model after lr decay! 0.302933 ---> 0.303833
=======================================
correlation 0.30383325
poisson_loss 412887.44
%%%% Output: stream
Epoch 19: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.45it/s]
%%%% Output: stream
Epoch 19: reducing learning rate of group 0 to 1.5000e-03.
[019|01/05] -/-> 0.30327093601226807
=======================================
correlation 0.30327094
poisson_loss 419500.56
%%%% Output: stream
Epoch 20: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.07it/s]
%%%% Output: stream
[020|02/05] -/-> 0.30352452397346497
=======================================
correlation 0.30352452
poisson_loss 408720.97
%%%% Output: stream
Epoch 21: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.37it/s]
%%%% Output: stream
[021|02/05] ---> 0.3041764199733734
=======================================
correlation 0.30417642
poisson_loss 403521.94
%%%% Output: stream
Epoch 22: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.49it/s]
%%%% Output: stream
[022|01/05] -/-> 0.3041190505027771
=======================================
correlation 0.30411905
poisson_loss 410190.44
%%%% Output: stream
Epoch 23: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.50it/s]
%%%% Output: stream
[023|02/05] -/-> 0.30390989780426025
=======================================
correlation 0.3039099
poisson_loss 403184.97
%%%% Output: stream
Epoch 24: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.38it/s]
%%%% Output: stream
[024|02/05] ---> 0.30429205298423767
=======================================
correlation 0.30429205
poisson_loss 407375.0
%%%% Output: stream
Epoch 25: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.44it/s]
%%%% Output: stream
[025|00/05] ---> 0.30436471104621887
=======================================
correlation 0.3043647
poisson_loss 402028.6
%%%% Output: stream
Epoch 26: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.14it/s]
%%%% Output: stream
[026|00/05] ---> 0.3048033118247986
=======================================
correlation 0.3048033
poisson_loss 402894.66
%%%% Output: stream
Epoch 27: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.61it/s]
%%%% Output: stream
[027|01/05] -/-> 0.30474141240119934
=======================================
correlation 0.3047414
poisson_loss 404821.2
%%%% Output: stream
Epoch 28: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.43it/s]
%%%% Output: stream
[028|02/05] -/-> 0.3047993779182434
=======================================
correlation 0.30479938
poisson_loss 399178.6
%%%% Output: stream
Epoch 29: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.53it/s]
%%%% Output: stream
[029|03/05] -/-> 0.30338940024375916
=======================================
correlation 0.3033894
poisson_loss 407109.62
%%%% Output: stream
Epoch 30: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.42it/s]
%%%% Output: stream
[030|04/05] -/-> 0.30467188358306885
=======================================
correlation 0.30467188
poisson_loss 406258.94
%%%% Output: stream
Epoch 31: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:04<00:00, 14.43it/s]