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Update week14.ipynb
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‎doc/pub/week14/ipynb/week14.ipynb‎

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"cell_type": "code",
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{
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"text": [
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"PyTorch 2.3.0.post100 | device: cpu\n",
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"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
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"Failed to download (trying next):\n",
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"HTTP Error 404: Not Found\n",
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"\n",
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"Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
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"Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz\n"
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"100%|███████████████████████████████████████████████████████████████████████████████████████████████| 9912422/9912422 [00:01<00:00, 8740496.78it/s]\n"
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"Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw\n",
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"\n",
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"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
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"Failed to download (trying next):\n",
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"HTTP Error 404: Not Found\n",
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"\n",
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"Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
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"Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
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"Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw\n",
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"\n",
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"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
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"\n",
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"Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
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"Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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"Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw\n",
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"\n",
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"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
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"Failed to download (trying next):\n",
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"\n",
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"Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
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"Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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"Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
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"\n",
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"Train: 60000 | Test: 10000\n",
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"Image shape: torch.Size([1, 28, 28])\n"
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]
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"# ── Standard imports ──────────────────────────────────────────────────────\n",
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"import math, time, os\n",
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"cell_type": "code",
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"outputs": [
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{
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"name": "stdout",
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"text": [
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"VAE parameters: 258,025\n",
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"VAE(\n",
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" (encoder): Sequential(\n",
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" (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
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" (1): LeakyReLU(negative_slope=0.2)\n",
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" (2): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
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" (3): LeakyReLU(negative_slope=0.2)\n",
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" (4): Flatten(start_dim=1, end_dim=-1)\n",
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" )\n",
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" (fc_mu): Linear(in_features=3136, out_features=20, bias=True)\n",
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" (fc_log_var): Linear(in_features=3136, out_features=20, bias=True)\n",
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" (fc_dec): Linear(in_features=20, out_features=3136, bias=True)\n",
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" (decoder): Sequential(\n",
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" (0): Unflatten(dim=1, unflattened_size=(64, 7, 7))\n",
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" (1): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
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" (2): LeakyReLU(negative_slope=0.2)\n",
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" (3): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
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" )\n",
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")\n"
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]
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}
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],
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"source": [
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"# ── VAE Model ─────────────────────────────────────────────────────────────\n",
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"class VAE(nn.Module):\n",
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"id": "6e511305",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training VAE on MNIST (20 epochs)...\n",
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"(Uses [0,1]-normalised pixels and Bernoulli decoder)\n"
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]
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}
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],
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"source": [
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"def train_vae(model, loader, epochs=20, lr=1e-3, beta=1.0):\n",
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" \"\"\"\n",

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