week08:autoenconders_torch make tensors from X for assert checking

NickVeld · web-flow · commit 48fd77e2f40b · 2019-11-20T10:56:10.000+03:00
diff --git a/week08_autoencoders/autoencoders_torch.ipynb b/week08_autoencoders/autoencoders_torch.ipynb
@@ -764,9 +764,9 @@
    "source": [
     "#noise tests\n",
     "theoretical_std = (X[:100].std()**2 + 0.5**2)**.5\n",
-    "our_std = apply_gaussian_noise(X[:100],sigma=0.5).std()\n",
+    "our_std = apply_gaussian_noise(torch.from_numpy(X[:100]),sigma=0.5).std()\n",
     "assert abs(theoretical_std - our_std) < 0.01, \"Standard deviation does not match it's required value. Make sure you use sigma as std.\"\n",
-    "assert abs(apply_gaussian_noise(X[:100],sigma=0.5).mean() - X[:100].mean()) < 0.01, \"Mean has changed. Please add zero-mean noise\""
+    "assert abs(apply_gaussian_noise(torch.from_numpy(X[:100]),sigma=0.5).mean() - torch.from_numpy(X[:100]).mean()) < 0.01, \"Mean has changed. Please add zero-mean noise\""
    ]
   },
   {
@@ -801,11 +801,11 @@
     "plt.subplot(1,4,1)\n",
     "plt.imshow(X[0].transpose([1,2,0]))\n",
     "plt.subplot(1,4,2)\n",
-    "plt.imshow(apply_gaussian_noise(X[:1],sigma=0.01).data.numpy()[0].transpose([1,2,0]).clip(0, 1))\n",
+    "plt.imshow(apply_gaussian_noise(torch.from_numpy(X[:1]),sigma=0.01).data.numpy()[0].transpose([1,2,0]).clip(0, 1))\n",
     "plt.subplot(1,4,3)\n",
-    "plt.imshow(apply_gaussian_noise(X[:1],sigma=0.1).data.numpy()[0].transpose([1,2,0]).clip(0, 1))\n",
+    "plt.imshow(apply_gaussian_noise(torch.from_numpy(X[:1]),sigma=0.1).data.numpy()[0].transpose([1,2,0]).clip(0, 1))\n",
     "plt.subplot(1,4,4)\n",
-    "plt.imshow(apply_gaussian_noise(X[:1],sigma=0.5).data.numpy()[0].transpose([1,2,0]).clip(0, 1))"
+    "plt.imshow(apply_gaussian_noise(torch.from_numpy(X[:1]),sigma=0.5).data.numpy()[0].transpose([1,2,0]).clip(0, 1))"
    ]
   },
   {
