Note
Click here to download the full example code
Deep CCA for more than 2 viewsΒΆ
This example demonstrates how to easily train Deep CCA models and variants
import numpy as np
import pytorch_lightning as pl
from torch.utils.data import Subset
from cca_zoo.data import Split_MNIST_Dataset
from cca_zoo.deepmodels import (
DCCA,
CCALightning,
get_dataloaders,
architectures,
objectives,
DTCCA,
)
n_train = 500
n_val = 100
train_dataset = Split_MNIST_Dataset(mnist_type="MNIST", train=True)
val_dataset = Subset(train_dataset, np.arange(n_train, n_train + n_val))
train_dataset = Subset(train_dataset, np.arange(n_train))
train_loader, val_loader = get_dataloaders(train_dataset, val_dataset)
# The number of latent dimensions across models
latent_dims = 2
# number of epochs for deep models
epochs = 10
encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=392)
encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=392)
Deep MCCA
dcca = DCCA(
latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.MCCA
)
dcca = CCALightning(dcca)
trainer = pl.Trainer(max_epochs=epochs, enable_checkpointing=False)
trainer.fit(dcca, train_loader, val_loader)
Out:
Validation sanity check: 0it [00:00, ?it/s]
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/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py:408: UserWarning: The number of training samples (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
f"The number of training samples ({self.num_training_batches}) is smaller than the logging interval"
Training: 0it [00:00, ?it/s]
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Epoch 0: 0%| | 0/2 [00:00<?, ?it/s]
Epoch 0: 50%|##### | 1/2 [00:00<00:00, 41.96it/s, loss=-0.201, v_num=1]
Validating: 0it [00:00, ?it/s]
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Epoch 0: 100%|##########| 2/2 [00:00<00:00, 54.09it/s, loss=-0.201, v_num=1]
Epoch 0: 0%| | 0/2 [00:00<?, ?it/s, loss=-0.201, v_num=1]
Epoch 1: 0%| | 0/2 [00:00<?, ?it/s, loss=-0.201, v_num=1]
Epoch 1: 50%|##### | 1/2 [00:00<00:00, 41.19it/s, loss=-0.608, v_num=1]
Validating: 0it [00:00, ?it/s]
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Epoch 1: 100%|##########| 2/2 [00:00<00:00, 53.35it/s, loss=-0.608, v_num=1]
Epoch 1: 0%| | 0/2 [00:00<?, ?it/s, loss=-0.608, v_num=1]
Epoch 2: 0%| | 0/2 [00:00<?, ?it/s, loss=-0.608, v_num=1]
Epoch 2: 50%|##### | 1/2 [00:00<00:00, 40.82it/s, loss=-0.871, v_num=1]
Validating: 0it [00:00, ?it/s]
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Epoch 2: 100%|##########| 2/2 [00:00<00:00, 52.37it/s, loss=-0.871, v_num=1]
Epoch 2: 0%| | 0/2 [00:00<?, ?it/s, loss=-0.871, v_num=1]
Epoch 3: 0%| | 0/2 [00:00<?, ?it/s, loss=-0.871, v_num=1]
Epoch 3: 50%|##### | 1/2 [00:00<00:00, 40.67it/s, loss=-1.05, v_num=1]
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Epoch 3: 100%|##########| 2/2 [00:00<00:00, 52.79it/s, loss=-1.05, v_num=1]
Epoch 3: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.05, v_num=1]
Epoch 4: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.05, v_num=1]
Epoch 4: 50%|##### | 1/2 [00:00<00:00, 40.73it/s, loss=-1.17, v_num=1]
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Epoch 4: 100%|##########| 2/2 [00:00<00:00, 52.89it/s, loss=-1.17, v_num=1]
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Epoch 5: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.17, v_num=1]
Epoch 5: 50%|##### | 1/2 [00:00<00:00, 40.97it/s, loss=-1.27, v_num=1]
Validating: 0it [00:00, ?it/s]
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Epoch 5: 100%|##########| 2/2 [00:00<00:00, 52.65it/s, loss=-1.27, v_num=1]
Epoch 5: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.27, v_num=1]
Epoch 6: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.27, v_num=1]
Epoch 6: 50%|##### | 1/2 [00:00<00:00, 40.92it/s, loss=-1.34, v_num=1]
Validating: 0it [00:00, ?it/s]
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Epoch 6: 100%|##########| 2/2 [00:00<00:00, 53.15it/s, loss=-1.34, v_num=1]
Epoch 6: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.34, v_num=1]
Epoch 7: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.34, v_num=1]
Epoch 7: 50%|##### | 1/2 [00:00<00:00, 40.80it/s, loss=-1.4, v_num=1]
Validating: 0it [00:00, ?it/s]
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Epoch 7: 100%|##########| 2/2 [00:00<00:00, 53.05it/s, loss=-1.4, v_num=1]
Epoch 7: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.4, v_num=1]
Epoch 8: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.4, v_num=1]
Epoch 8: 50%|##### | 1/2 [00:00<00:00, 40.83it/s, loss=-1.44, v_num=1]
Validating: 0it [00:00, ?it/s]
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Epoch 8: 100%|##########| 2/2 [00:00<00:00, 52.84it/s, loss=-1.44, v_num=1]
Epoch 8: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.44, v_num=1]
Epoch 9: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.44, v_num=1]
Epoch 9: 50%|##### | 1/2 [00:00<00:00, 39.41it/s, loss=-1.48, v_num=1]
Validating: 0it [00:00, ?it/s]
Validating: 0%| | 0/1 [00:00<?, ?it/s]
Epoch 9: 100%|##########| 2/2 [00:00<00:00, 51.66it/s, loss=-1.48, v_num=1]
Epoch 9: 100%|##########| 2/2 [00:00<00:00, 33.15it/s, loss=-1.48, v_num=1]
Deep GCCA
dcca = DCCA(
latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.GCCA
)
dcca = CCALightning(dcca)
trainer = pl.Trainer(max_epochs=epochs, enable_checkpointing=False)
trainer.fit(dcca, train_loader, val_loader)
Out:
Validation sanity check: 0it [00:00, ?it/s]
Validation sanity check: 0%| | 0/1 [00:00<?, ?it/s]
/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py:408: UserWarning: The number of training samples (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
f"The number of training samples ({self.num_training_batches}) is smaller than the logging interval"
Training: 0it [00:00, ?it/s]
Training: 0%| | 0/2 [00:00<?, ?it/s]
Epoch 0: 0%| | 0/2 [00:00<?, ?it/s]
Epoch 0: 50%|##### | 1/2 [00:00<00:00, 25.04it/s, loss=-1.86, v_num=2]
Validating: 0it [00:00, ?it/s]
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Epoch 0: 100%|##########| 2/2 [00:00<00:00, 37.47it/s, loss=-1.86, v_num=2]
Epoch 0: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.86, v_num=2]
Epoch 1: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.86, v_num=2]
Epoch 1: 50%|##### | 1/2 [00:00<00:00, 24.23it/s, loss=-1.86, v_num=2]
Validating: 0it [00:00, ?it/s]
Validating: 0%| | 0/1 [00:00<?, ?it/s]
Epoch 1: 100%|##########| 2/2 [00:00<00:00, 36.52it/s, loss=-1.86, v_num=2]
Epoch 1: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.86, v_num=2]
Epoch 2: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.86, v_num=2]
Epoch 2: 50%|##### | 1/2 [00:00<00:00, 24.58it/s, loss=-1.88, v_num=2]
Validating: 0it [00:00, ?it/s]
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Epoch 2: 100%|##########| 2/2 [00:00<00:00, 37.00it/s, loss=-1.88, v_num=2]
Epoch 2: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.88, v_num=2]
Epoch 3: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.88, v_num=2]
Epoch 3: 50%|##### | 1/2 [00:00<00:00, 25.51it/s, loss=-1.89, v_num=2]
Validating: 0it [00:00, ?it/s]
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Epoch 3: 100%|##########| 2/2 [00:00<00:00, 38.05it/s, loss=-1.89, v_num=2]
Epoch 3: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.89, v_num=2]
Epoch 4: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.89, v_num=2]
Epoch 4: 50%|##### | 1/2 [00:00<00:00, 25.30it/s, loss=-1.9, v_num=2]
Validating: 0it [00:00, ?it/s]
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Epoch 4: 100%|##########| 2/2 [00:00<00:00, 37.30it/s, loss=-1.9, v_num=2]
Epoch 4: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.9, v_num=2]
Epoch 5: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.9, v_num=2]
Epoch 5: 50%|##### | 1/2 [00:00<00:00, 24.97it/s, loss=-1.91, v_num=2]
Validating: 0it [00:00, ?it/s]
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Epoch 5: 100%|##########| 2/2 [00:00<00:00, 37.31it/s, loss=-1.91, v_num=2]
Epoch 5: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.91, v_num=2]
Epoch 6: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.91, v_num=2]
Epoch 6: 50%|##### | 1/2 [00:00<00:00, 25.37it/s, loss=-1.91, v_num=2]
Validating: 0it [00:00, ?it/s]
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Epoch 6: 100%|##########| 2/2 [00:00<00:00, 37.91it/s, loss=-1.91, v_num=2]
Epoch 6: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.91, v_num=2]
Epoch 7: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.91, v_num=2]
Epoch 7: 50%|##### | 1/2 [00:00<00:00, 25.41it/s, loss=-1.92, v_num=2]
Validating: 0it [00:00, ?it/s]
Validating: 0%| | 0/1 [00:00<?, ?it/s]
Epoch 7: 100%|##########| 2/2 [00:00<00:00, 37.97it/s, loss=-1.92, v_num=2]
Epoch 7: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.92, v_num=2]
Epoch 8: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.92, v_num=2]
Epoch 8: 50%|##### | 1/2 [00:00<00:00, 25.39it/s, loss=-1.92, v_num=2]
Validating: 0it [00:00, ?it/s]
Validating: 0%| | 0/1 [00:00<?, ?it/s]
Epoch 8: 100%|##########| 2/2 [00:00<00:00, 37.97it/s, loss=-1.92, v_num=2]
Epoch 8: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.92, v_num=2]
Epoch 9: 0%| | 0/2 [00:00<?, ?it/s, loss=-1.92, v_num=2]
Epoch 9: 50%|##### | 1/2 [00:00<00:00, 25.29it/s, loss=-1.93, v_num=2]
Validating: 0it [00:00, ?it/s]
Validating: 0%| | 0/1 [00:00<?, ?it/s]
Epoch 9: 100%|##########| 2/2 [00:00<00:00, 37.56it/s, loss=-1.93, v_num=2]
Epoch 9: 100%|##########| 2/2 [00:00<00:00, 26.89it/s, loss=-1.93, v_num=2]
Deep TCCA
dcca = DTCCA(latent_dims=latent_dims, encoders=[encoder_1, encoder_2])
dcca = CCALightning(dcca)
trainer = pl.Trainer(max_epochs=epochs, enable_checkpointing=False)
trainer.fit(dcca, train_loader, val_loader)
Out:
Validation sanity check: 0it [00:00, ?it/s]
Validation sanity check: 0%| | 0/1 [00:00<?, ?it/s]/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/tensorly/backend/core.py:1106: UserWarning: In partial_svd: converting to NumPy. Check SVD_FUNS for available alternatives if you want to avoid this.
warnings.warn('In partial_svd: converting to NumPy.'
/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/pytorch_lightning/trainer/data_loading.py:408: UserWarning: The number of training samples (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
f"The number of training samples ({self.num_training_batches}) is smaller than the logging interval"
Training: 0it [00:00, ?it/s]
Training: 0%| | 0/2 [00:00<?, ?it/s]
Epoch 0: 0%| | 0/2 [00:00<?, ?it/s] /home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/tensorly/backend/core.py:1106: UserWarning: In partial_svd: converting to NumPy. Check SVD_FUNS for available alternatives if you want to avoid this.
warnings.warn('In partial_svd: converting to NumPy.'
Epoch 0: 50%|##### | 1/2 [00:00<00:00, 19.98it/s, loss=6.14e-08, v_num=3]
Validating: 0it [00:00, ?it/s]
Validating: 0%| | 0/1 [00:00<?, ?it/s]
Epoch 0: 100%|##########| 2/2 [00:00<00:00, 25.61it/s, loss=6.14e-08, v_num=3]
Epoch 0: 0%| | 0/2 [00:00<?, ?it/s, loss=6.14e-08, v_num=3]
Epoch 1: 0%| | 0/2 [00:00<?, ?it/s, loss=6.14e-08, v_num=3]/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/tensorly/backend/core.py:1106: UserWarning: In partial_svd: converting to NumPy. Check SVD_FUNS for available alternatives if you want to avoid this.
warnings.warn('In partial_svd: converting to NumPy.'
Epoch 1: 50%|##### | 1/2 [00:00<00:00, 20.35it/s, loss=6.05e-08, v_num=3]
Validating: 0it [00:00, ?it/s]
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Epoch 1: 100%|##########| 2/2 [00:00<00:00, 25.55it/s, loss=6.05e-08, v_num=3]
Epoch 1: 0%| | 0/2 [00:00<?, ?it/s, loss=6.05e-08, v_num=3]
Epoch 2: 0%| | 0/2 [00:00<?, ?it/s, loss=6.05e-08, v_num=3]/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/tensorly/backend/core.py:1106: UserWarning: In partial_svd: converting to NumPy. Check SVD_FUNS for available alternatives if you want to avoid this.
warnings.warn('In partial_svd: converting to NumPy.'
Epoch 2: 50%|##### | 1/2 [00:00<00:00, 20.03it/s, loss=6.1e-08, v_num=3]
Validating: 0it [00:00, ?it/s]
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Epoch 2: 100%|##########| 2/2 [00:00<00:00, 25.46it/s, loss=6.1e-08, v_num=3]
Epoch 2: 0%| | 0/2 [00:00<?, ?it/s, loss=6.1e-08, v_num=3]
Epoch 3: 0%| | 0/2 [00:00<?, ?it/s, loss=6.1e-08, v_num=3]/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/tensorly/backend/core.py:1106: UserWarning: In partial_svd: converting to NumPy. Check SVD_FUNS for available alternatives if you want to avoid this.
warnings.warn('In partial_svd: converting to NumPy.'
Epoch 3: 50%|##### | 1/2 [00:00<00:00, 19.25it/s, loss=6.11e-08, v_num=3]
Validating: 0it [00:00, ?it/s]
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Epoch 3: 100%|##########| 2/2 [00:00<00:00, 23.91it/s, loss=6.11e-08, v_num=3]
Epoch 3: 0%| | 0/2 [00:00<?, ?it/s, loss=6.11e-08, v_num=3]
Epoch 4: 0%| | 0/2 [00:00<?, ?it/s, loss=6.11e-08, v_num=3]/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/tensorly/backend/core.py:1106: UserWarning: In partial_svd: converting to NumPy. Check SVD_FUNS for available alternatives if you want to avoid this.
warnings.warn('In partial_svd: converting to NumPy.'
Epoch 4: 50%|##### | 1/2 [00:00<00:00, 19.51it/s, loss=4.93e-08, v_num=3]
Validating: 0it [00:00, ?it/s]
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Epoch 4: 100%|##########| 2/2 [00:00<00:00, 25.11it/s, loss=4.93e-08, v_num=3]
Epoch 4: 0%| | 0/2 [00:00<?, ?it/s, loss=4.93e-08, v_num=3]
Epoch 5: 0%| | 0/2 [00:00<?, ?it/s, loss=4.93e-08, v_num=3]/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/tensorly/backend/core.py:1106: UserWarning: In partial_svd: converting to NumPy. Check SVD_FUNS for available alternatives if you want to avoid this.
warnings.warn('In partial_svd: converting to NumPy.'
Epoch 5: 50%|##### | 1/2 [00:00<00:00, 19.70it/s, loss=5.22e-08, v_num=3]
Validating: 0it [00:00, ?it/s]
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Epoch 5: 100%|##########| 2/2 [00:00<00:00, 25.08it/s, loss=5.22e-08, v_num=3]
Epoch 5: 0%| | 0/2 [00:00<?, ?it/s, loss=5.22e-08, v_num=3]
Epoch 6: 0%| | 0/2 [00:00<?, ?it/s, loss=5.22e-08, v_num=3]/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/tensorly/backend/core.py:1106: UserWarning: In partial_svd: converting to NumPy. Check SVD_FUNS for available alternatives if you want to avoid this.
warnings.warn('In partial_svd: converting to NumPy.'
Epoch 6: 50%|##### | 1/2 [00:00<00:00, 20.32it/s, loss=4.68e-08, v_num=3]
Validating: 0it [00:00, ?it/s]
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Epoch 6: 100%|##########| 2/2 [00:00<00:00, 25.87it/s, loss=4.68e-08, v_num=3]
Epoch 6: 0%| | 0/2 [00:00<?, ?it/s, loss=4.68e-08, v_num=3]
Epoch 7: 0%| | 0/2 [00:00<?, ?it/s, loss=4.68e-08, v_num=3]/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/tensorly/backend/core.py:1106: UserWarning: In partial_svd: converting to NumPy. Check SVD_FUNS for available alternatives if you want to avoid this.
warnings.warn('In partial_svd: converting to NumPy.'
Epoch 7: 50%|##### | 1/2 [00:00<00:00, 20.28it/s, loss=4.47e-08, v_num=3]
Validating: 0it [00:00, ?it/s]
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Epoch 7: 100%|##########| 2/2 [00:00<00:00, 13.46it/s, loss=4.47e-08, v_num=3]
Epoch 7: 0%| | 0/2 [00:00<?, ?it/s, loss=4.47e-08, v_num=3]
Epoch 8: 0%| | 0/2 [00:00<?, ?it/s, loss=4.47e-08, v_num=3]/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/tensorly/backend/core.py:1106: UserWarning: In partial_svd: converting to NumPy. Check SVD_FUNS for available alternatives if you want to avoid this.
warnings.warn('In partial_svd: converting to NumPy.'
Epoch 8: 50%|##### | 1/2 [00:00<00:00, 20.35it/s, loss=4.3e-08, v_num=3]
Validating: 0it [00:00, ?it/s]
Validating: 0%| | 0/1 [00:00<?, ?it/s]
Epoch 8: 100%|##########| 2/2 [00:00<00:00, 25.77it/s, loss=4.3e-08, v_num=3]
Epoch 8: 0%| | 0/2 [00:00<?, ?it/s, loss=4.3e-08, v_num=3]
Epoch 9: 0%| | 0/2 [00:00<?, ?it/s, loss=4.3e-08, v_num=3]/home/docs/checkouts/readthedocs.org/user_builds/cca-zoo/envs/v1.10.2/lib/python3.7/site-packages/tensorly/backend/core.py:1106: UserWarning: In partial_svd: converting to NumPy. Check SVD_FUNS for available alternatives if you want to avoid this.
warnings.warn('In partial_svd: converting to NumPy.'
Epoch 9: 50%|##### | 1/2 [00:00<00:00, 20.35it/s, loss=4.6e-08, v_num=3]
Validating: 0it [00:00, ?it/s]
Validating: 0%| | 0/1 [00:00<?, ?it/s]
Epoch 9: 100%|##########| 2/2 [00:00<00:00, 25.29it/s, loss=4.6e-08, v_num=3]
Epoch 9: 100%|##########| 2/2 [00:00<00:00, 17.16it/s, loss=4.6e-08, v_num=3]
Total running time of the script: ( 0 minutes 6.169 seconds)