hwnr.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = keras.datasets.mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (60000,)\n",
      "(10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(train_images.shape, train_labels.shape)\n",
    "print(test_images.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 2.5016 - accuracy: 0.8641\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.3681 - accuracy: 0.9156\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2967 - accuracy: 0.9277\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2470 - accuracy: 0.9383\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2367 - accuracy: 0.9409\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2095 - accuracy: 0.9479\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.2049 - accuracy: 0.9501\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.1902 - accuracy: 0.9523\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1818 - accuracy: 0.9547\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1827 - accuracy: 0.9553\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fb2383eed90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 0s - loss: 0.3060 - accuracy: 0.9470\n",
      "\n",
      "Test accuracy: 0.9470000267028809\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  }
 ],
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