{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Deep CCA for more than 2 views\n\nThis example demonstrates how to easily train Deep CCA models and variants\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nimport pytorch_lightning as pl\nfrom torch.utils.data import Subset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from cca_zoo.data import Split_MNIST_Dataset\nfrom cca_zoo.deepmodels import (\n    DCCA,\n    CCALightning,\n    get_dataloaders,\n    architectures,\n    objectives,\n    DTCCA,\n)\n\nn_train = 500\nn_val = 100\ntrain_dataset = Split_MNIST_Dataset(mnist_type=\"MNIST\", train=True)\nval_dataset = Subset(train_dataset, np.arange(n_train, n_train + n_val))\ntrain_dataset = Subset(train_dataset, np.arange(n_train))\ntrain_loader, val_loader = get_dataloaders(train_dataset, val_dataset)\n\n# The number of latent dimensions across models\nlatent_dims = 2\n# number of epochs for deep models\nepochs = 10\n\nencoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=392)\nencoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=392)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Deep MCCA\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "dcca = DCCA(\n    latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.MCCA\n)\ndcca = CCALightning(dcca)\ntrainer = pl.Trainer(max_epochs=epochs, enable_checkpointing=False)\ntrainer.fit(dcca, train_loader, val_loader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Deep GCCA\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "dcca = DCCA(\n    latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.GCCA\n)\ndcca = CCALightning(dcca)\ntrainer = pl.Trainer(max_epochs=epochs, enable_checkpointing=False)\ntrainer.fit(dcca, train_loader, val_loader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Deep TCCA\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "dcca = DTCCA(latent_dims=latent_dims, encoders=[encoder_1, encoder_2])\ndcca = CCALightning(dcca)\ntrainer = pl.Trainer(max_epochs=epochs, enable_checkpointing=False)\ntrainer.fit(dcca, train_loader, val_loader)"
      ]
    }
  ],
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      "file_extension": ".py",
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      "pygments_lexer": "ipython3",
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