{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Deep CCA with more customisation\n\nShowing some examples of more advanced functionality with DCCA and pytorch-lightning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import pytorch_lightning as pl\nfrom torch import optim\nfrom torch.utils.data import Subset\n\nfrom cca_zoo.data import Split_MNIST_Dataset\nfrom cca_zoo.deepmodels import DCCA, CCALightning, get_dataloaders, architectures\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\n# TODO add in custom architecture and schedulers and stuff to show it off\nencoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=392)\nencoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=392)\n\n# Deep CCA\ndcca = DCCA(latent_dims=latent_dims, encoders=[encoder_1, encoder_2])\noptimizer = optim.Adam(dcca.parameters(), lr=1e-3)\nscheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, 1)\ndcca = CCALightning(dcca, optimizer=optimizer, lr_scheduler=scheduler)\ntrainer = pl.Trainer(max_epochs=epochs, enable_checkpointing=False)\ntrainer.fit(dcca, train_loader, val_loader)"
      ]
    }
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