In [1]:
from os import environ
environ['optimizer'] = 'Adam'
environ['num_workers']= '4'
environ['maxsize']= '100000'
environ['batch_size']= str(4096*4)
environ['n_epochs']= '500'
environ['batch_norm']= 'True'
environ['loss_func']='MSE'
environ['layers'] = '300 200 120 100 80 50 30'
environ['dropouts'] = '0.3 0.2 0.2 0.1 0.1 0.05 0.05'
import torch
torch.cuda.set_device(1)
%run utils.ipynb
l = l.load(f"speedup_{optimizer}_batchnorm{batchnorm}{loss_func}nlayers{len(layers_sizes)}")
In [2]:
l.lr_find()
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
In [17]:
l.recorder.plot()
In [5]:
lr = 1e-03
In [6]:
l.fit_one_cycle(1000, lr)
Total time: 30:34
epoch
train_loss
valid_loss
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0.949070
1.243399
985
0.949174
1.254983
986
0.949970
1.241091
987
0.948874
1.229720
988
0.948664
1.227076
989
0.949391
1.220581
990
0.950622
1.229136
991
0.950080
1.233998
992
0.949678
1.242516
993
0.950215
1.227391
994
0.951358
1.210781
995
0.949803
1.220377
996
0.950561
1.234882
997
0.950885
1.229264
998
0.951204
1.254519
999
0.950306
1.223214
1000
0.949949
1.226429
In [4]:
l.recorder.plot_losses()
In [14]:
l.save(f"speedup_{optimizer}_batch_norm_{batch_norm}_{loss_func}_nlayers_{len(layers_sizes)}")
In [10]:
val_df = pd.DataFrame()
train_df = pd.DataFrame()
preds, targets = l.get_preds(fai.basic_data.DatasetType.Valid)
preds = preds.reshape((-1,)).numpy()
targets = targets.reshape((-1,)).numpy()
val_df['prediction'] = preds
val_df['target'] = targets
val_df['abs_diff'] = np.abs(preds - targets)
val_df['APE'] = np.abs(val_df.target - val_df.prediction)/val_df.target * 100
preds, targets = l.get_preds(fai.basic_data.DatasetType.Train)
preds = preds.reshape((-1,)).numpy()
targets = targets.reshape((-1,)).numpy()
train_df['prediction'] = preds
train_df['target'] = targets
train_df['abs_diff'] = np.abs(preds - targets)
train_df['APE'] = np.abs(train_df.target - train_df.prediction)/train_df.target * 100
In [11]:
val_df.describe()
Out[11]:
prediction
target
abs_diff
APE
count
10000.000000
10000.000000
10000.000000
10000.000000
mean
2.125660
2.027997
0.478604
55.945557
std
1.727538
1.919018
0.584464
113.525436
min
0.125330
0.030862
0.000022
0.000936
25%
0.896762
0.652639
0.126763
8.565076
50%
1.591264
1.392690
0.280744
20.923521
75%
2.803258
2.590816
0.591900
46.658999
max
10.828941
13.272740
5.599267
2309.435547
In [12]:
joint_plot(train_df, f"Training dataset, {loss_func} loss")
/data/scratch/henni-mohammed/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [13]:
joint_plot_one_program(val_dl, 'function119', l.model)
In [ ]: