{"cells":[{"cell_type":"markdown","id":"43a1495a-017d-412f-bfb8-76ad9a9e5875","metadata":{"id":"43a1495a-017d-412f-bfb8-76ad9a9e5875"},"source":["# DIRESA tutorial"]},{"metadata":{"id":"19d0fc316ab761cb"},"cell_type":"raw","source":["\n"," \"Open\n",""],"id":"19d0fc316ab761cb"},{"cell_type":"markdown","id":"7a1aba31-5e47-4c4c-b2bb-7610e9d6c7b4","metadata":{"id":"7a1aba31-5e47-4c4c-b2bb-7610e9d6c7b4"},"source":["### 1. Install packages\n","The `diresa-torch` package depends on the `PyTorch` package. This tutorial also uses `numpy` and `matplotlib`."]},{"metadata":{"collapsed":true,"id":"96dfb427d3ba906c","executionInfo":{"status":"ok","timestamp":1758700858851,"user_tz":-120,"elapsed":22232,"user":{"displayName":"Geert De Paepe","userId":""}},"outputId":"df1e9ecc-d94e-4928-9c39-cbc7cda53231","colab":{"base_uri":"https://localhost:8080/"}},"cell_type":"code","outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: diresa-torch in /usr/local/lib/python3.12/dist-packages (1.0.0)\n"]}],"execution_count":1,"source":["# Install needed packages\n","!pip install numpy\n","!pip install matplotlib\n","!pip install diresa-torch"],"id":"96dfb427d3ba906c"},{"metadata":{"id":"ad1a6e98391d1eea"},"cell_type":"markdown","source":["### 2. Load the dataset\n","In this tutorial, we are going to compress the 3D lorenz '63 butterfly into a 2D latent space. The `lorenz.csv` contains a list of butterfly points, with three colums for the X, Y and Z coordinate."],"id":"ad1a6e98391d1eea"},{"metadata":{"collapsed":true,"id":"cadfdeba2bb4c735","executionInfo":{"status":"ok","timestamp":1758700859255,"user_tz":-120,"elapsed":401,"user":{"displayName":"Geert De Paepe","userId":""}},"outputId":"998df138-f730-4a40-ed0b-7c652d791a1a","colab":{"base_uri":"https://localhost:8080/"}},"cell_type":"code","outputs":[{"output_type":"stream","name":"stdout","text":["--2025-09-24 08:00:58-- https://gitlab.com/etrovub/ai4wcm/public/diresa/-/raw/master/docs/lorenz.csv\n","Resolving gitlab.com (gitlab.com)... 172.65.251.78, 2606:4700:90:0:f22e:fbec:5bed:a9b9\n","Connecting to gitlab.com (gitlab.com)|172.65.251.78|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 2999999 (2.9M) [text/plain]\n","Saving to: ‘lorenz.csv.8’\n","\n","lorenz.csv.8 100%[===================>] 2.86M --.-KB/s in 0.02s \n","\n","2025-09-24 08:00:59 (133 MB/s) - ‘lorenz.csv.8’ saved [2999999/2999999]\n","\n"]}],"execution_count":2,"source":["!wget https://gitlab.com/etrovub/ai4wcm/public/diresa/-/raw/master/docs/lorenz.csv"],"id":"cadfdeba2bb4c735"},{"metadata":{"id":"7d7aacfc6ded83ce","executionInfo":{"status":"ok","timestamp":1758700859374,"user_tz":-120,"elapsed":118,"user":{"displayName":"Geert De Paepe","userId":""}},"outputId":"a5393910-8477-4171-c1fb-4a050f0b8778","colab":{"base_uri":"https://localhost:8080/"}},"cell_type":"code","outputs":[{"output_type":"stream","name":"stdout","text":["Shape lorenz.csv : (40000, 3)\n"]}],"execution_count":3,"source":["import numpy as np\n","data_file = \"lorenz.csv\"\n","data = np.loadtxt(data_file, delimiter=\",\").astype(np.float32)\n","print(\"Shape\", data_file, \":\", data.shape)\n","train = data[:30000]\n","val = data[30000:35000]\n","test = data[35000:]"],"id":"7d7aacfc6ded83ce"},{"metadata":{"id":"ae018a9faf12a749"},"cell_type":"markdown","source":["### 3. Build the DIRESA model\n","We can build a DIRESA model with convolutional and/or dense layers with the `Diresa.from_hyper_param` class method. We can also build a DIRESA model based on a custom encoder and decoder with the `Diresa.from_custom` class method (see below). We build a model with an input shape of `(3,)` for the 3D butterfly points. Our encoder model has 3 dense layers with 40, 20 and 2 units (the latter is the dimension of the latent space). The decoder is a reflection of the encoder."],"id":"ae018a9faf12a749"},{"metadata":{"collapsed":true,"id":"f0537ae57585aebb","executionInfo":{"status":"ok","timestamp":1758700863255,"user_tz":-120,"elapsed":3879,"user":{"displayName":"Geert De Paepe","userId":""}},"outputId":"85c6d9e5-ef79-4a77-abb5-092d07af56f1","colab":{"base_uri":"https://localhost:8080/"}},"cell_type":"code","outputs":[{"output_type":"stream","name":"stdout","text":["Diresa(\n"," (base_encoder): Encoder(\n"," (network): DenseLayer(\n"," (network): Sequential(\n"," (0): Linear(in_features=3, out_features=40, bias=True)\n"," (1): ReLU()\n"," (2): Linear(in_features=40, out_features=20, bias=True)\n"," (3): ReLU()\n"," (4): Linear(in_features=20, out_features=2, bias=True)\n"," )\n"," )\n"," )\n"," (dist_layer): DistanceLayer()\n"," (base_decoder): Decoder(\n"," (network): DenseLayer(\n"," (network): Sequential(\n"," (0): Linear(in_features=2, out_features=20, bias=True)\n"," (1): ReLU()\n"," (2): Linear(in_features=20, out_features=40, bias=True)\n"," (3): ReLU()\n"," (4): Linear(in_features=40, out_features=3, bias=True)\n"," )\n"," )\n"," )\n"," (ordering_layer): OrderingLayer()\n",")\n"]}],"execution_count":4,"source":["import torch.nn as nn\n","from diresa_torch.arch.models import Diresa\n","\n","diresa = Diresa.from_hyper_param(input_shape=(3, ), dense_units=(40, 20, 2), activation=nn.ReLU())\n","print(diresa)"],"id":"f0537ae57585aebb"},{"metadata":{"id":"7e403c9a26d5d9b6"},"cell_type":"markdown","source":["### 4. Train the DIRESA model\n","We train the DIRESA model with the `train_diresa` function. The parameters include i.a. the diresa model, the dataloaders and the 3 loss functions with their weights. The DIRESA model has 3 loss functions, the reconstruction loss (usually the MSE is used here), the covariance loss and a distance loss (here the MSE distance loss is used). The batch size must be large enough for the calculation of the covariance loss, which calculates the covariance matrix of the latent space components over the batch. A shuffled version of the batches is fed in the twin encoder for calculating the distance loss (so also for this one, the batch size must be large enough). If `staged_training` is True, the encoder and decoder are trained separately."],"id":"7e403c9a26d5d9b6"},{"metadata":{"collapsed":true,"id":"aba3dfc9ba34fcd3","executionInfo":{"status":"ok","timestamp":1758700874798,"user_tz":-120,"elapsed":11546,"user":{"displayName":"Geert De Paepe","userId":""}},"outputId":"2ff5ca1d-3425-4d92-c726-ede2a798998d","colab":{"base_uri":"https://localhost:8080/"}},"cell_type":"code","outputs":[{"output_type":"stream","name":"stderr","text":["INFO:root:Encoder_Decoder: Epoch 1/10 - ReconMSELoss_train: 1.1770e-01, LatentCovLoss_train: 5.5410e-08, MSEDistLoss_train: 1.0775e-05, WeightedLoss_train: 1.1771e-01\n","INFO:root:Encoder_Decoder: Epoch 2/10 - ReconMSELoss_train: 4.2598e-02, LatentCovLoss_train: 1.9488e-05, MSEDistLoss_train: 8.3505e-06, WeightedLoss_train: 4.2626e-02\n","INFO:root:Encoder_Decoder: Epoch 3/10 - ReconMSELoss_train: 1.9493e-02, LatentCovLoss_train: 4.8181e-04, MSEDistLoss_train: 1.7763e-06, WeightedLoss_train: 1.9977e-02\n","INFO:root:Encoder_Decoder: Epoch 4/10 - ReconMSELoss_train: 2.0258e-03, LatentCovLoss_train: 6.7263e-05, MSEDistLoss_train: 8.8855e-07, WeightedLoss_train: 2.0939e-03\n","INFO:root:Encoder_Decoder: Epoch 5/10 - ReconMSELoss_train: 1.0372e-03, LatentCovLoss_train: 6.0002e-05, MSEDistLoss_train: 1.0330e-06, WeightedLoss_train: 1.0982e-03\n","INFO:root:Encoder_Decoder: Epoch 6/10 - ReconMSELoss_train: 8.3148e-04, LatentCovLoss_train: 6.7772e-05, MSEDistLoss_train: 9.6441e-07, WeightedLoss_train: 9.0022e-04\n","INFO:root:Encoder_Decoder: Epoch 7/10 - ReconMSELoss_train: 7.0734e-04, LatentCovLoss_train: 7.6553e-05, MSEDistLoss_train: 8.2719e-07, WeightedLoss_train: 7.8472e-04\n","INFO:root:Encoder_Decoder: Epoch 8/10 - ReconMSELoss_train: 6.2208e-04, LatentCovLoss_train: 4.6304e-05, MSEDistLoss_train: 7.0290e-07, WeightedLoss_train: 6.6909e-04\n","INFO:root:Encoder_Decoder: Epoch 9/10 - ReconMSELoss_train: 5.4353e-04, LatentCovLoss_train: 5.2800e-05, MSEDistLoss_train: 6.2335e-07, WeightedLoss_train: 5.9695e-04\n","INFO:root:Encoder_Decoder: Epoch 10/10 - ReconMSELoss_train: 4.6981e-04, LatentCovLoss_train: 4.3592e-05, MSEDistLoss_train: 5.4320e-07, WeightedLoss_train: 5.1395e-04\n"]}],"execution_count":5,"source":["from torch.optim import Adam\n","from torch.cuda import is_available\n","from torch.utils.data import DataLoader\n","from diresa_torch.training.trainer import train_diresa\n","from diresa_torch.losses.loss_funcs import MSEDistLoss, LatentCovLoss\n","import logging\n","\n","logging.basicConfig(level=logging.INFO, force = True)\n","\n","device = \"cuda\" if is_available() else \"cpu\"\n","diresa = diresa.to(device)\n","\n","hist = train_diresa(model=diresa,\n"," train_loader=DataLoader(train, batch_size=512, shuffle=True),\n"," #val_loader=DataLoader(val, batch_size=512, shuffle=True),\n"," criteria=[nn.MSELoss(), LatentCovLoss(), MSEDistLoss()],\n"," loss_weights=[1., 1., 1.],\n"," optimizer=Adam(diresa.parameters(), lr=0.001),\n"," num_epochs=10,\n"," staged_training=False,\n"," )"],"id":"aba3dfc9ba34fcd3"},{"metadata":{"id":"e61f04da08035f81"},"cell_type":"markdown","source":["### 5. Evaluate the DIRESA model\n","We evaluate the DIRESA model with the `evaluate_diresa` function. The bigger the batch size, the more accurate the covariance and distance losses will be.\n"],"id":"e61f04da08035f81"},{"metadata":{"id":"837b6531ac2dd10","executionInfo":{"status":"ok","timestamp":1758700878377,"user_tz":-120,"elapsed":22,"user":{"displayName":"Geert De Paepe","userId":""}},"outputId":"f727a315-fcca-4425-a915-662002827625","colab":{"base_uri":"https://localhost:8080/"}},"cell_type":"code","outputs":[{"output_type":"stream","name":"stderr","text":["INFO:root:ReconMSELoss_eval: 5.1149e-04, LatentCovLoss_eval: 8.1919e-06, MSEDistLoss_eval: 5.5916e-09, WeightedLoss_eval: 5.1969e-04\n"]}],"execution_count":6,"source":["from diresa_torch.training.trainer import evaluate_diresa\n","\n","hist = evaluate_diresa(model=diresa,\n"," test_loader=DataLoader(test, batch_size=5000),\n"," criteria=[nn.MSELoss(), LatentCovLoss(), MSEDistLoss()],\n"," loss_weights=[1., 1., 1.],\n"," )"],"id":"837b6531ac2dd10"},{"metadata":{"id":"1e82c2372672a4fa"},"cell_type":"markdown","source":["### 6. Order the latent components\n","The ordering of the latent components is done by calculating the R2 values for each of them for a given dataset.\n","This is done with the `order_diresa` function.\n","Again, the bigger the batch size, the more accurate the R2 calculation will be."],"id":"1e82c2372672a4fa"},{"cell_type":"code","source":["from diresa_torch.training.trainer import order_diresa\n","\n","order_diresa(model=diresa, data_loader=DataLoader(test, batch_size=5000))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IAb71jq3qcgD","executionInfo":{"status":"ok","timestamp":1758700874988,"user_tz":-120,"elapsed":41,"user":{"displayName":"Geert De Paepe","userId":""}},"outputId":"91f63985-b5a6-4b9a-e259-1ef68ac64155"},"id":"IAb71jq3qcgD","execution_count":7,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO:root:Batch size for ordering is 5000\n","INFO:root:Ordered R2 scores are: [0.5434688329696655, 0.41254401206970215]\n"]}]},{"metadata":{"id":"c662123b934540b"},"cell_type":"markdown","source":["### 7. Show latent space\n","We plot the 2D latent space. As we ordered the latent components, the biggest variance should be in the x-axes direction."],"id":"c662123b934540b"},{"metadata":{"id":"111c6a62c22ba780","executionInfo":{"status":"ok","timestamp":1758700875281,"user_tz":-120,"elapsed":294,"user":{"displayName":"Geert De Paepe","userId":""}},"outputId":"325f213e-730b-4591-b27f-bdfc099e16bd","colab":{"base_uri":"https://localhost:8080/","height":452}},"cell_type":"code","outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"execution_count":8,"source":["import matplotlib.pyplot as plt\n","from torch import from_numpy\n","\n","latent = diresa.encode(from_numpy(test).to(device)).cpu().detach().numpy()\n","plt.figure()\n","plt.title(\"Latent space\")\n","plt.scatter(latent[:, 0], latent[:, 1], marker='.', s=0.1, color='C2')\n","m, b = np.polyfit(latent[:, 0], latent[:, 1], deg=1)\n","plt.axline(xy1=(0, b), slope=m, color='r', label=f'$y = {m:.2f}x {b:+.2f}$')\n","plt.legend()\n","plt.show()"],"id":"111c6a62c22ba780"},{"metadata":{"id":"4c3dac3c248dc9a"},"cell_type":"markdown","source":["### 8. Original versus decoded datset\n","We compair the origonal dataset with the decoded one."],"id":"4c3dac3c248dc9a"},{"metadata":{"id":"27c6a9af783fc77","executionInfo":{"status":"ok","timestamp":1758700875819,"user_tz":-120,"elapsed":535,"user":{"displayName":"Geert De Paepe","userId":""}},"colab":{"base_uri":"https://localhost:8080/","height":414},"outputId":"313fa10b-8aff-47e9-f3cc-a1476c6e83d0"},"cell_type":"code","outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"execution_count":9,"source":["predict = diresa.decode(from_numpy(latent).to(device)).cpu().detach().numpy()\n","\n","fig = plt.figure()\n","ax = fig.add_subplot(projection='3d')\n","ax.scatter(val[:, 0], val[:, 1], val[:, 2], marker='.', s=0.1)\n","ax.scatter(predict[:, 0], predict[:, 1], predict[:, 2], marker='.', s=0.1, color='C1')\n","plt.show()"],"id":"27c6a9af783fc77"},{"metadata":{"id":"daab82d274d6226"},"cell_type":"markdown","source":["### 9. A convolutional example\n","\n","If your dataset consists of a number of variables (e.g. temperature and pressure, so 2 variables) over a 2 dimensional grid,\n","convolutional layers can be used in the encoder/decoder. Here is an example for a grid (y, x) = (32, 64).\n","The dataset would then have a shape of (nbr_of_samples, 2, 32, 64). We will use a stack of 3 convolutional/maxpooling blocks\n","in the encoder (the decoder mirrors the encoder). The first block uses 3 Conv2D layers, the second bock 2 and the third block 1,\n","followed by a MaxPooling2D layer (*stack=(3, 2, 1)*). The number of filters in the first block is 32, in the second 16 and\n","in the third 8 (*stack_filters=(32, 16, 8)*)."],"id":"daab82d274d6226"},{"metadata":{"id":"1da3f06606242653","executionInfo":{"status":"ok","timestamp":1758700875843,"user_tz":-120,"elapsed":14,"user":{"displayName":"Geert De Paepe","userId":""}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"b090e5e8-9c0f-4dc8-b66d-ad635ccc48a5"},"cell_type":"code","outputs":[{"output_type":"stream","name":"stdout","text":["Diresa(\n"," (base_encoder): Encoder(\n"," (cnn): CNNLayer(\n"," (cnn): Sequential(\n"," (0): _CNNBlock(\n"," (block): Sequential(\n"," (0): Conv2d(2, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (1): ReLU()\n"," (2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (3): ReLU()\n"," (4): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (5): ReLU()\n"," (6): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n"," )\n"," )\n"," (1): _CNNBlock(\n"," (block): Sequential(\n"," (0): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (1): ReLU()\n"," (2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (3): ReLU()\n"," (4): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n"," )\n"," )\n"," (2): _CNNBlock(\n"," (block): Sequential(\n"," (0): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (1): ReLU()\n"," (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n"," )\n"," )\n"," )\n"," )\n"," (flatten): Flatten(start_dim=1, end_dim=-1)\n"," (network): Sequential(\n"," (0): CNNLayer(\n"," (cnn): Sequential(\n"," (0): _CNNBlock(\n"," (block): Sequential(\n"," (0): Conv2d(2, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (1): ReLU()\n"," (2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (3): ReLU()\n"," (4): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (5): ReLU()\n"," (6): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n"," )\n"," )\n"," (1): _CNNBlock(\n"," (block): Sequential(\n"," (0): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (1): ReLU()\n"," (2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (3): ReLU()\n"," (4): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n"," )\n"," )\n"," (2): _CNNBlock(\n"," (block): Sequential(\n"," (0): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (1): ReLU()\n"," (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n"," )\n"," )\n"," )\n"," )\n"," (1): Flatten(start_dim=1, end_dim=-1)\n"," )\n"," )\n"," (dist_layer): DistanceLayer()\n"," (base_decoder): Decoder(\n"," (unflatten): Unflatten(dim=1, unflattened_size=(8, 4, 8))\n"," (cnn): CNNLayer(\n"," (cnn): Sequential(\n"," (0): _CNNTransposeBlock(\n"," (block): Sequential(\n"," (0): Upsample(scale_factor=2.0, mode='nearest')\n"," (1): ConvTranspose2d(8, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (2): ReLU()\n"," )\n"," )\n"," (1): _CNNTransposeBlock(\n"," (block): Sequential(\n"," (0): Upsample(scale_factor=2.0, mode='nearest')\n"," (1): ConvTranspose2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (2): ReLU()\n"," (3): ConvTranspose2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (4): ReLU()\n"," )\n"," )\n"," (2): _CNNTransposeBlock(\n"," (block): Sequential(\n"," (0): Upsample(scale_factor=2.0, mode='nearest')\n"," (1): ConvTranspose2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (2): ReLU()\n"," (3): ConvTranspose2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (4): ReLU()\n"," (5): ConvTranspose2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (6): ReLU()\n"," )\n"," )\n"," )\n"," )\n"," (network): Sequential(\n"," (0): Unflatten(dim=1, unflattened_size=(8, 4, 8))\n"," (1): CNNLayer(\n"," (cnn): Sequential(\n"," (0): _CNNTransposeBlock(\n"," (block): Sequential(\n"," (0): Upsample(scale_factor=2.0, mode='nearest')\n"," (1): ConvTranspose2d(8, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (2): ReLU()\n"," )\n"," )\n"," (1): _CNNTransposeBlock(\n"," (block): Sequential(\n"," (0): Upsample(scale_factor=2.0, mode='nearest')\n"," (1): ConvTranspose2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (2): ReLU()\n"," (3): ConvTranspose2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (4): ReLU()\n"," )\n"," )\n"," (2): _CNNTransposeBlock(\n"," (block): Sequential(\n"," (0): Upsample(scale_factor=2.0, mode='nearest')\n"," (1): ConvTranspose2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (2): ReLU()\n"," (3): ConvTranspose2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (4): ReLU()\n"," (5): ConvTranspose2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (6): ReLU()\n"," )\n"," )\n"," )\n"," )\n"," )\n"," )\n"," (ordering_layer): OrderingLayer()\n",")\n"]}],"execution_count":10,"source":["diresa_conv = Diresa.from_hyper_param(input_shape=(2, 32, 64),\n"," stack=(3, 2, 1),\n"," stack_filters=(32, 16, 8),\n"," )\n","print(diresa_conv)"],"id":"1da3f06606242653"},{"metadata":{"id":"c3d06b52-991b-478f-a599-ec0a947bd6c8"},"cell_type":"markdown","source":["### 10. Build DIRESA with custom encoder and decoder\n","We can also build DIRESA models with custom encoder and decoder (reconstruction) models. To show the API, we use the encoder and decoder of the previous model to create a DIRESA model."],"id":"c3d06b52-991b-478f-a599-ec0a947bd6c8"},{"metadata":{"id":"3fb3e14d-75d7-4aab-9ef3-794c9387e7a5","executionInfo":{"status":"ok","timestamp":1758700875857,"user_tz":-120,"elapsed":12,"user":{"displayName":"Geert De Paepe","userId":""}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"65a01b3b-b936-467e-c58d-d47401a573e1"},"cell_type":"code","outputs":[{"output_type":"stream","name":"stdout","text":["Diresa(\n"," (base_encoder): Encoder(\n"," (network): DenseLayer(\n"," (network): Sequential(\n"," (0): Linear(in_features=3, out_features=40, bias=True)\n"," (1): ReLU()\n"," (2): Linear(in_features=40, out_features=20, bias=True)\n"," (3): ReLU()\n"," (4): Linear(in_features=20, out_features=2, bias=True)\n"," )\n"," )\n"," )\n"," (dist_layer): DistanceLayer()\n"," (base_decoder): Decoder(\n"," (network): DenseLayer(\n"," (network): Sequential(\n"," (0): Linear(in_features=2, out_features=20, bias=True)\n"," (1): ReLU()\n"," (2): Linear(in_features=20, out_features=40, bias=True)\n"," (3): ReLU()\n"," (4): Linear(in_features=40, out_features=3, bias=True)\n"," )\n"," )\n"," )\n"," (ordering_layer): OrderingLayer()\n",")\n"]}],"execution_count":11,"source":["diresa_custom = Diresa.from_custom(diresa.base_encoder, diresa.base_decoder)\n","print(diresa_custom)"],"id":"3fb3e14d-75d7-4aab-9ef3-794c9387e7a5"}],"metadata":{"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.7"},"colab":{"provenance":[{"file_id":"https://github.com/gdepaepe/diresa/blob/main/diresa.ipynb","timestamp":1755873525771}]}},"nbformat":4,"nbformat_minor":5}