fix bug #17: randn -> rand

code/chapter03_DL-basics/3.13_dropout.ipynb

@@ -9,7 +9,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.4.1\n"
+      "1.2.0\n"
      ]
     }
    ],
@@ -40,7 +40,7 @@
     "    # 这种情况下把全部元素都丢弃\n",
     "    if keep_prob == 0:\n",
     "        return torch.zeros_like(X)\n",
-    "    mask = (torch.randn(X.shape) < keep_prob).float()\n",
+    "    mask = (torch.rand(X.shape) < keep_prob).float()\n",
     "    \n",
     "    return mask * X / keep_prob"
    ]
@@ -54,7 +54,7 @@
      "data": {
       "text/plain": [
        "tensor([[ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.],\n",
-       "        [ 8.,  9., 10., 11., 12.,  0., 14., 15.]])"
+       "        [ 8.,  9., 10., 11., 12., 13., 14., 15.]])"
       ]
      },
      "execution_count": 3,
@@ -75,8 +75,8 @@
     {
      "data": {
       "text/plain": [
-       "tensor([[ 0.,  0.,  4.,  0.,  8.,  0., 12., 14.],\n",
-       "        [ 0., 18., 20.,  0., 24.,  0.,  0., 30.]])"
+       "tensor([[ 0.,  0.,  4.,  6.,  0.,  0., 12., 14.],\n",
+       "        [ 0., 18., 20., 22.,  0.,  0., 28.,  0.]])"
       ]
      },
      "execution_count": 4,
@@ -184,11 +184,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch 1, loss 0.0044, train acc 0.574, test acc 0.648\n",
-      "epoch 2, loss 0.0023, train acc 0.786, test acc 0.786\n",
-      "epoch 3, loss 0.0019, train acc 0.826, test acc 0.825\n",
-      "epoch 4, loss 0.0017, train acc 0.839, test acc 0.831\n",
-      "epoch 5, loss 0.0016, train acc 0.849, test acc 0.850\n"
+      "epoch 1, loss 0.0045, train acc 0.561, test acc 0.662\n",
+      "epoch 2, loss 0.0023, train acc 0.783, test acc 0.786\n",
+      "epoch 3, loss 0.0019, train acc 0.823, test acc 0.773\n",
+      "epoch 4, loss 0.0017, train acc 0.838, test acc 0.847\n",
+      "epoch 5, loss 0.0016, train acc 0.848, test acc 0.809\n"
      ]
     }
    ],
@@ -231,11 +231,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch 1, loss 0.0045, train acc 0.553, test acc 0.715\n",
-      "epoch 2, loss 0.0023, train acc 0.784, test acc 0.793\n",
-      "epoch 3, loss 0.0019, train acc 0.822, test acc 0.817\n",
-      "epoch 4, loss 0.0018, train acc 0.837, test acc 0.830\n",
-      "epoch 5, loss 0.0016, train acc 0.848, test acc 0.839\n"
+      "epoch 1, loss 0.0048, train acc 0.526, test acc 0.743\n",
+      "epoch 2, loss 0.0023, train acc 0.779, test acc 0.764\n",
+      "epoch 3, loss 0.0020, train acc 0.815, test acc 0.819\n",
+      "epoch 4, loss 0.0018, train acc 0.836, test acc 0.814\n",
+      "epoch 5, loss 0.0016, train acc 0.848, test acc 0.842\n"
      ]
     }
    ],
@@ -256,7 +256,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -270,7 +270,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

docs/chapter03_DL-basics/3.13_dropout.md

@@ -49,7 +49,7 @@ def dropout(X, drop_prob):
     # 这种情况下把全部元素都丢弃
     if keep_prob == 0:
         return torch.zeros_like(X)
-    mask = (torch.randn(X.shape) < keep_prob).float()
+    mask = (torch.rand(X.shape) < keep_prob).float()
     
     return mask * X / keep_prob
 ```