实例13.6-Sequential实现手写数字识别-加载整个模型.ipynb

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   "source": [
    "# 实例13.6-Sequential实现手写数字识别-加载整个模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten (Flatten)           (None, 784)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               100480    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "313/313 - 0s - loss: 0.0927 - sparse_categorical_accuracy: 0.9720 - 387ms/epoch - 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.09265123307704926, 0.972000002861023]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist=tf.keras.datasets.mnist\n",
    "(train_x,train_y),(test_x,test_y)=mnist.load_data()\n",
    "\n",
    "X_train,X_test=tf.cast(train_x/255.0,tf.float32),tf.cast(test_x/255.0,tf.float32)\n",
    "y_train,y_test=tf.cast(train_y,tf.int16),tf.cast(test_y,tf.int16)\n",
    "\n",
    "model=tf.keras.models.load_model(\"mnist_model.h5\")\n",
    "model.summary()\n",
    "\n",
    "model.evaluate(X_test,y_test,verbose=2)"
   ]
  }
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