MNIST test.ipynb

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   "execution_count": 2,
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     "data": {
      "text/plain": [
       "'2.1.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch MNIST DATA set\n",
    "mnist = tf.keras.datasets.mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test = x_train / 255.0, x_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Neural Network Model\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose the optimizer and loss function \n",
    "model.compile(optimizer='adam',\n",
    "             loss='sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 6s 99us/sample - loss: 0.2998 - accuracy: 0.9135\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 5s 87us/sample - loss: 0.1428 - accuracy: 0.9579\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 5s 81us/sample - loss: 0.1082 - accuracy: 0.9675\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 5s 78us/sample - loss: 0.0891 - accuracy: 0.9725\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 4s 73us/sample - loss: 0.0746 - accuracy: 0.9770\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2b19525f2e10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model with training set for 5 times\n",
    "model.fit(x_train,y_train,epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 81us/sample - loss: 0.0687 - accuracy: 0.9794\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0686913245390635, 0.9794]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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