Noise simulation

Author: jangsoopark

README.md

@@ -159,6 +159,7 @@ $ python3 train.py
 
 - Noise Simulation [1]
     - i.i.d samples from a uniform distribution
+    - This simulation does not fix the random seed
 
 | Noise | 1% | 5% | 10% | 15%|
 | :---: | :---: | :---: | :---: | :---: |

notebook/experiments-SOC.ipynb

@@ -288,45 +288,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from skimage import util"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(614.0, 7744)"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "util.random_noise(np.zeros((88, 88)), mode='s&p', amount=0.15).sum(), 88 * 88"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from skimage import util\n",
+    "\n",
+    "\n",
     "def generate_noise(_images, amount):\n",
     "    \n",
     "    n, _, h, w = _images.shape\n",
     "        \n",
-    "    noise = np.random.uniform(size=(n, 1, h, w))\n",
-    "    portions = util.random_noise(np.zeros((n, 1, 88, 88)), mode='s&p', amount=amount)\n",
+    "    noise = np.array([np.random.uniform(size=(1, h, w)) for _ in range(n)])\n",
+    "    portions = np.array([\n",
+    "        util.random_noise(np.zeros((1, 88, 88)), mode='s&p', amount=amount)\n",
+    "        for _ in range(n)\n",
+    "    ])\n",
     "    noise = noise * portions\n",
     "    \n",
     "    return _images + noise.astype(np.float32)\n",
@@ -352,22 +329,23 @@
     "        corrects += (predictions == labels.to(m.device)).sum().item()\n",
     "\n",
     "    accuracy = 100 * corrects / num_data\n",
+    "    \n",
     "    return accuracy"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ratio = 0.01, accuracy = 98.56\n",
-      "ratio = 0.05, accuracy = 94.39\n",
-      "ratio = 0.10, accuracy = 85.03\n",
-      "ratio = 0.15, accuracy = 73.65\n"
+      "ratio = 0.01, accuracy = 98.23\n",
+      "ratio = 0.05, accuracy = 94.19\n",
+      "ratio = 0.10, accuracy = 85.28\n",
+      "ratio = 0.15, accuracy = 73.40\n"
      ]
     }
    ],