02. tensorflow读取numpy数据训练DNN模型.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TfRdquslKbO3"
   },
   "source": [
    "##  Python使用tensorflow读取numpy数据训练DNN模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-0tqX8qkXZEj"
   },
   "source": [
    "演示步骤：\n",
    "1. 从文件加载NumPy数组\n",
    "2. 将Numpy数组加载到tf.data.Dataset对象\n",
    "3. 构建DNN模型执行训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-Ze5IBx9clLB"
   },
   "source": [
    "### 1. 导入库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k6J3JzK5NxQ6"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G0yWiN8-cpDb"
   },
   "source": [
    "### 2. 从npz 文件读取numpy数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy文件地址\n",
    "filename = \"./datas/mnist/mnist.npz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with np.load(filename) as data:\n",
    "    train_examples = data['x_train']\n",
    "    train_labels = data['y_train']\n",
    "    test_examples = data['x_test']\n",
    "    test_labels = data['y_test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(train_examples), type(train_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 1\n"
     ]
    }
   ],
   "source": [
    "print(train_examples.ndim, train_labels.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (60000,)\n"
     ]
    }
   ],
   "source": [
    "print(train_examples.shape, train_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8 uint8\n"
     ]
    }
   ],
   "source": [
    "print(train_examples.dtype, train_labels.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_examples[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_examples[0].shape"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
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       "5"
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LJX1U0jpJmyT9l6SeLurtPyQ9JelJDQVrdod6W6ihQ/QnJW2o/Z3b6feu0Fdb3jculwWS4AQdkARhB5Ig7EAShB1IgrADSRB2IAnCDiTx/65XcTNOWsh5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(train_examples[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cCeCkvrDgCMM"
   },
   "source": [
    "### 3. 加载 NumPy 数组到tf.data.Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tslB0tJPgB-2"
   },
   "source": [
    "tf.data.Dataset.from_tensor_slices可以接收元组，特征矩阵、标签向量，要求它们行数（样本数）相等，会按行匹配组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QN_8wwc5R7Qm"
   },
   "outputs": [],
   "source": [
    "train_dataset = tf.data.Dataset.from_tensor_slices((train_examples, train_labels))\n",
    "test_dataset = tf.data.Dataset.from_tensor_slices((test_examples, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   3,\n",
       "          18,  18,  18, 126, 136, 175,  26, 166, 255, 247, 127,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,  30,  36,  94, 154, 170,\n",
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       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,  49, 238, 253, 253, 253, 253,\n",
       "         253, 253, 253, 253, 251,  93,  82,  82,  56,  39,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,  18, 219, 253, 253, 253, 253,\n",
       "         253, 198, 182, 247, 241,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,  80, 156, 107, 253, 253,\n",
       "         205,  11,   0,  43, 154,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,  14,   1, 154, 253,\n",
       "          90,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 139, 253,\n",
       "         190,   2,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  11, 190,\n",
       "         253,  70,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  35,\n",
       "         241, 225, 160, 108,   1,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          81, 240, 253, 253, 119,  25,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,  45, 186, 253, 253, 150,  27,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0,  16,  93, 252, 253, 187,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0,   0,   0, 249, 253, 249,  64,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,  46, 130, 183, 253, 253, 207,   2,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  39,\n",
       "         148, 229, 253, 253, 253, 250, 182,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  24, 114, 221,\n",
       "         253, 253, 253, 253, 201,  78,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,  23,  66, 213, 253, 253,\n",
       "         253, 253, 198,  81,   2,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,  18, 171, 219, 253, 253, 253, 253,\n",
       "         195,  80,   9,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,  55, 172, 226, 253, 253, 253, 253, 244, 133,\n",
       "          11,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0, 136, 253, 253, 253, 212, 135, 132,  16,   0,\n",
       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0],\n",
       "        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "           0,   0]], dtype=uint8),\n",
       " 5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据集的一个样本（这时包含了所有特征列、标签列）\n",
    "train_dataset.as_numpy_iterator().next()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0dvl1uUukc4K"
   },
   "source": [
    "### 4. 打乱和批次化数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GTXdRMPcSXZj"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "SHUFFLE_BUFFER_SIZE = 100\n",
    "\n",
    "shuffle_ds = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE)\n",
    "\n",
    "train_dataset = shuffle_ds.batch(BATCH_SIZE)\n",
    "test_dataset = test_dataset.batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w69Jl8k6lilg"
   },
   "source": [
    "### 5. 建立和训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input_shape要省略输入数据的第一维度，(60000, 28, 28)，只需要输入(28,28)\n",
    "# 这里的input_shape其实就等于train_examples.shape[1:]\n",
    "first_layer = tf.keras.layers.Flatten(input_shape=(28, 28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Uhxr8py4DkDN"
   },
   "outputs": [],
   "source": [
    "# 搭建模型\n",
    "model = tf.keras.Sequential([\n",
    "    first_layer,\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型编译\n",
    "model.compile(optimizer=tf.keras.optimizers.RMSprop(),\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "              metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 查看模型信息\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. 训练和评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XLDzlPGgOHBx"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "938/938 [==============================] - 2s 2ms/step - loss: 3.4164 - sparse_categorical_accuracy: 0.8756\n",
      "Epoch 2/10\n",
      "938/938 [==============================] - 2s 2ms/step - loss: 0.5543 - sparse_categorical_accuracy: 0.9274\n",
      "Epoch 3/10\n",
      "938/938 [==============================] - 2s 2ms/step - loss: 0.4046 - sparse_categorical_accuracy: 0.9465\n",
      "Epoch 4/10\n",
      "938/938 [==============================] - 2s 2ms/step - loss: 0.3261 - sparse_categorical_accuracy: 0.9553\n",
      "Epoch 5/10\n",
      "938/938 [==============================] - 2s 2ms/step - loss: 0.2856 - sparse_categorical_accuracy: 0.9601\n",
      "Epoch 6/10\n",
      "938/938 [==============================] - 2s 2ms/step - loss: 0.2705 - sparse_categorical_accuracy: 0.9647\n",
      "Epoch 7/10\n",
      "938/938 [==============================] - 2s 2ms/step - loss: 0.2419 - sparse_categorical_accuracy: 0.9685\n",
      "Epoch 8/10\n",
      "938/938 [==============================] - 2s 2ms/step - loss: 0.2307 - sparse_categorical_accuracy: 0.9706\n",
      "Epoch 9/10\n",
      "938/938 [==============================] - 2s 2ms/step - loss: 0.2052 - sparse_categorical_accuracy: 0.9734\n",
      "Epoch 10/10\n",
      "938/938 [==============================] - 2s 2ms/step - loss: 0.1947 - sparse_categorical_accuracy: 0.9752\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f554417fa10>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_dataset, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2q82yN8mmKIE"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 1ms/step - loss: 0.5924 - sparse_categorical_accuracy: 0.9581\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5923562049865723, 0.9581000208854675]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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