Uniform variable names to match the slides

guydav · Nov 21, 2018 · 8631437 · 8631437
1 parent a9a8827
commit 8631437
Showing 1 changed file with 21 additions and 21 deletions.
diff --git a/11-VAE.ipynb b/11-VAE.ipynb
@@ -107,7 +107,7 @@
     "            nn.Sigmoid(),\n",
     "        )\n",
     "\n",
-    "    def reparameterize(self, mu, logvar):\n",
+    "    def reparameterise(self, mu, logvar):\n",
     "        if self.training:\n",
     "            std = logvar.mul(0.5).exp_()\n",
     "            eps = std.data.new(std.size()).normal_()\n",
@@ -119,7 +119,7 @@
     "        mu_logvar = self.encoder(x.view(-1, 784)).view(-1, 2, d)\n",
     "        mu = mu_logvar[:, 0, :]\n",
     "        logvar = mu_logvar[:, 1, :]\n",
-    "        z = self.reparameterize(mu, logvar)\n",
+    "        z = self.reparameterise(mu, logvar)\n",
     "        return self.decoder(z), mu, logvar\n",
     "\n",
     "model = VAE().to(device)"
@@ -147,9 +147,9 @@
    "source": [
     "# Reconstruction + KL divergence losses summed over all elements and batch\n",
     "\n",
-    "def loss_function(recon_x, x, mu, logvar):\n",
+    "def loss_function(x_hat, x, mu, logvar):\n",
     "    BCE = nn.functional.binary_cross_entropy(\n",
-    "        recon_x, x.view(-1, 784), size_average=False\n",
+    "        x_hat, x.view(-1, 784), reduction='sum'\n",
     "    )\n",
     "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
     "\n",
@@ -171,11 +171,11 @@
     "    # Training\n",
     "    model.train()\n",
     "    train_loss = 0\n",
-    "    for data, _ in train_loader:\n",
-    "        data = data.to(device)\n",
+    "    for x, _ in train_loader:\n",
+    "        x = x.to(device)\n",
     "        # ===================forward=====================\n",
-    "        recon_batch, mu, logvar = model(data)\n",
-    "        loss = loss_function(recon_batch, data, mu, logvar)\n",
+    "        x_hat, mu, logvar = model(x)\n",
+    "        loss = loss_function(x_hat, x, mu, logvar)\n",
     "        train_loss += loss.item()\n",
     "        # ===================backward====================\n",
     "        optimizer.zero_grad()\n",
@@ -189,15 +189,15 @@
     "    with torch.no_grad():\n",
     "        model.eval()\n",
     "        test_loss = 0\n",
-    "        for data, _ in test_loader:\n",
-    "            data = data.to(device)\n",
+    "        for x, _ in test_loader:\n",
+    "            x = x.to(device)\n",
     "            # ===================forward=====================\n",
-    "            recon_batch, mu, logvar = model(data)\n",
-    "            test_loss += loss_function(recon_batch, data, mu, logvar).item()\n",
+    "            x_hat, mu, logvar = model(x)\n",
+    "            test_loss += loss_function(x_hat, x, mu, logvar).item()\n",
     "    # ===================log========================\n",
     "    test_loss /= len(test_loader.dataset)\n",
     "    print(f'====> Test set loss: {test_loss:.4f}')\n",
-    "    display_images(data, recon_batch, 1, f'Epoch {epoch}')"
+    "    display_images(x, x_hat, 1, f'Epoch {epoch}')"
    ]
   },
   {
@@ -209,7 +209,7 @@
     "# Generating a few samples\n",
     "\n",
     "N = 16\n",
-    "sample = torch.randn((N, 20), requires_grad=False).to(device)\n",
+    "sample = torch.randn((N, d)).to(device)\n",
     "sample = model.decoder(sample)\n",
     "display_images(None, sample, N // 4, count=True)"
    ]
@@ -222,7 +222,7 @@
    "source": [
     "# Display last test batch\n",
     "\n",
-    "display_images(None, data, 4, count=True)"
+    "display_images(None, x, 4, count=True)"
    ]
   },
   {
@@ -233,11 +233,11 @@
    "source": [
     "# Choose starting and ending point for the interpolation -> shows original and reconstructed\n",
     "\n",
-    "A, B = 5, 14\n",
+    "A, B = 3, 8\n",
     "sample = model.decoder(torch.stack((mu[A].data, mu[B].data), 0))\n",
     "display_images(None, torch.stack(((\n",
-    "    data[A].data.view(-1),\n",
-    "    data[B].data.view(-1),\n",
+    "    x[A].data.view(-1),\n",
+    "    x[B].data.view(-1),\n",
     "    sample.data[0],\n",
     "    sample.data[1]\n",
     ")), 0))"
@@ -254,7 +254,7 @@
     "N = 16\n",
     "code = torch.Tensor(N, 20).to(device)\n",
     "for i in range(N):\n",
-    "    code[i] = i / N * mu[B].data + (1 - i / N) * mu[A].data\n",
+    "    code[i] = i / (N - 1) * mu[B].data + (1 - i / (N - 1) ) * mu[A].data\n",
     "code = torch.tensor(code, requires_grad=True)\n",
     "sample = model.decoder(code)\n",
     "display_images(None, sample, N // 4, count=True)"
@@ -263,9 +263,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "latest",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "latest"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {