Updated model training and running to CPU

Week1.ipynb

@@ -25,8 +25,12 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
+    "import tensorflow as tf\n",
     "from tensorflow import keras\n",
-    "from tensorflow.keras import layers"
+    "from tensorflow.keras import layers\n",
+    "\n",
+    "# Hide GPU from visible devices\n",
+    "tf.config.set_visible_devices([], 'GPU')"
    ]
   },
   {
@@ -73,7 +77,8 @@
     "\n",
     "# convert class vectors to binary class matrices\n",
     "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
-    "y_test = keras.utils.to_categorical(y_test, num_classes)"
+    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
+    "# [1, 2, 3, 4] -> [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]\n"
    ]
   },
   {
@@ -166,41 +171,41 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/15\n",
-      "422/422 [==============================] - 4s 3ms/step - loss: 0.3724 - accuracy: 0.8837 - val_loss: 0.0810 - val_accuracy: 0.9785\n",
+      "422/422 [==============================] - 9s 20ms/step - loss: 0.3573 - accuracy: 0.8919 - val_loss: 0.0857 - val_accuracy: 0.9777\n",
       "Epoch 2/15\n",
-      "422/422 [==============================] - 1s 3ms/step - loss: 0.1118 - accuracy: 0.9671 - val_loss: 0.0562 - val_accuracy: 0.9852\n",
+      "422/422 [==============================] - 8s 19ms/step - loss: 0.1184 - accuracy: 0.9636 - val_loss: 0.0608 - val_accuracy: 0.9825\n",
       "Epoch 3/15\n",
-      "422/422 [==============================] - 1s 2ms/step - loss: 0.0835 - accuracy: 0.9739 - val_loss: 0.0481 - val_accuracy: 0.9872\n",
+      "422/422 [==============================] - 8s 19ms/step - loss: 0.0862 - accuracy: 0.9733 - val_loss: 0.0496 - val_accuracy: 0.9868\n",
       "Epoch 4/15\n",
-      "422/422 [==============================] - 1s 2ms/step - loss: 0.0700 - accuracy: 0.9787 - val_loss: 0.0404 - val_accuracy: 0.9898\n",
+      "422/422 [==============================] - 8s 20ms/step - loss: 0.0724 - accuracy: 0.9778 - val_loss: 0.0424 - val_accuracy: 0.9883\n",
       "Epoch 5/15\n",
-      "422/422 [==============================] - 1s 3ms/step - loss: 0.0604 - accuracy: 0.9815 - val_loss: 0.0381 - val_accuracy: 0.9910\n",
+      "422/422 [==============================] - 8s 19ms/step - loss: 0.0656 - accuracy: 0.9793 - val_loss: 0.0398 - val_accuracy: 0.9895\n",
       "Epoch 6/15\n",
-      "422/422 [==============================] - 1s 2ms/step - loss: 0.0559 - accuracy: 0.9826 - val_loss: 0.0354 - val_accuracy: 0.9903\n",
+      "422/422 [==============================] - 8s 20ms/step - loss: 0.0591 - accuracy: 0.9816 - val_loss: 0.0361 - val_accuracy: 0.9912\n",
       "Epoch 7/15\n",
-      "422/422 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.9829 - val_loss: 0.0338 - val_accuracy: 0.9903\n",
+      "422/422 [==============================] - 8s 20ms/step - loss: 0.0522 - accuracy: 0.9833 - val_loss: 0.0315 - val_accuracy: 0.9922\n",
       "Epoch 8/15\n",
-      "422/422 [==============================] - 1s 2ms/step - loss: 0.0484 - accuracy: 0.9847 - val_loss: 0.0296 - val_accuracy: 0.9923\n",
+      "422/422 [==============================] - 8s 20ms/step - loss: 0.0485 - accuracy: 0.9846 - val_loss: 0.0319 - val_accuracy: 0.9910\n",
       "Epoch 9/15\n",
-      "422/422 [==============================] - 1s 2ms/step - loss: 0.0443 - accuracy: 0.9860 - val_loss: 0.0333 - val_accuracy: 0.9912\n",
+      "422/422 [==============================] - 9s 20ms/step - loss: 0.0447 - accuracy: 0.9858 - val_loss: 0.0331 - val_accuracy: 0.9917\n",
       "Epoch 10/15\n",
-      "422/422 [==============================] - 1s 2ms/step - loss: 0.0422 - accuracy: 0.9869 - val_loss: 0.0308 - val_accuracy: 0.9927\n",
+      "422/422 [==============================] - 9s 21ms/step - loss: 0.0416 - accuracy: 0.9871 - val_loss: 0.0309 - val_accuracy: 0.9922\n",
       "Epoch 11/15\n",
-      "422/422 [==============================] - 1s 2ms/step - loss: 0.0397 - accuracy: 0.9874 - val_loss: 0.0321 - val_accuracy: 0.9922\n",
+      "422/422 [==============================] - 8s 20ms/step - loss: 0.0397 - accuracy: 0.9877 - val_loss: 0.0281 - val_accuracy: 0.9932\n",
       "Epoch 12/15\n",
-      "422/422 [==============================] - 1s 2ms/step - loss: 0.0375 - accuracy: 0.9875 - val_loss: 0.0269 - val_accuracy: 0.9928\n",
+      "422/422 [==============================] - 9s 20ms/step - loss: 0.0393 - accuracy: 0.9874 - val_loss: 0.0308 - val_accuracy: 0.9908\n",
       "Epoch 13/15\n",
-      "422/422 [==============================] - 1s 2ms/step - loss: 0.0355 - accuracy: 0.9886 - val_loss: 0.0296 - val_accuracy: 0.9925\n",
+      "422/422 [==============================] - 8s 20ms/step - loss: 0.0373 - accuracy: 0.9882 - val_loss: 0.0276 - val_accuracy: 0.9928\n",
       "Epoch 14/15\n",
-      "422/422 [==============================] - 1s 2ms/step - loss: 0.0340 - accuracy: 0.9888 - val_loss: 0.0291 - val_accuracy: 0.9928\n",
+      "422/422 [==============================] - 8s 19ms/step - loss: 0.0357 - accuracy: 0.9879 - val_loss: 0.0265 - val_accuracy: 0.9935\n",
       "Epoch 15/15\n",
-      "422/422 [==============================] - 1s 3ms/step - loss: 0.0321 - accuracy: 0.9894 - val_loss: 0.0277 - val_accuracy: 0.9927\n"
+      "422/422 [==============================] - 8s 19ms/step - loss: 0.0334 - accuracy: 0.9886 - val_loss: 0.0298 - val_accuracy: 0.9927\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x14a08dfc3d0>"
+       "<keras.callbacks.History at 0x2242fa4c220>"
       ]
      },
      "execution_count": 5,
@@ -230,8 +235,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test loss: 0.02324853651225567\n",
-      "Test accuracy: 0.9922000169754028\n"
+      "Test loss: 0.02596166729927063\n",
+      "Test accuracy: 0.9919000267982483\n"
      ]
     }
    ],
@@ -250,12 +255,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
     "model.save(\"mnist.h5\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

mnist.h5

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6b9d7830249bb932ad3715ba3d277c9415cd8a8a4c6d100eb6a1f8568f58fa8f
+oid sha256:f86dd360a2a044d621faffa7d748a1a8154395656af6b76b7b687272c04cac35
 size 455304