05

SmBito · Oct 4, 2019 · 487569a · 487569a
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diff --git a/.DS_Store b/.DS_Store
diff --git a/05_Convolutional_Neural_Networks.ipynb b/05_Convolutional_Neural_Networks.ipynb
@@ -116,6 +116,125 @@
     "    dogsvcats.make_training_data()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_data = np.load(\"training_data.npy\", allow_pickle = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24946\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(training_data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([[167, 173, 179, ..., 165, 255, 255],\n",
+      "       [184, 192, 180, ..., 147, 255, 255],\n",
+      "       [199, 200, 196, ..., 179, 255, 255],\n",
+      "       ...,\n",
+      "       [255, 255, 255, ..., 255, 255, 255],\n",
+      "       [255, 255, 255, ..., 255, 255, 255],\n",
+      "       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)\n",
+      " array([1., 0.])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(training_data[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([[23, 14, 17, ..., 36, 20, 22],\n",
+      "       [16, 24, 19, ..., 27, 40, 29],\n",
+      "       [25, 24, 23, ..., 28, 22, 32],\n",
+      "       ...,\n",
+      "       [69, 69, 78, ..., 96, 92, 84],\n",
+      "       [75, 77, 70, ..., 90, 87, 67],\n",
+      "       [59, 61, 73, ..., 85, 82, 77]], dtype=uint8)\n",
+      " array([1., 0.])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(training_data[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.imshow(training_data[1][0], cmap = 'gray')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 0.])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_data[1][1]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,