Day_070_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業:\n",
    "[練習]請嘗試新增一層或兩層看看最後預測結果是否有差異\n",
    "model.add(Dense(units=256, input_dim=784, kernel_initializer='normal', activation='relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zfI5zCjRPlnc"
   },
   "source": [
    "# 資料預處理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 20601,
     "status": "ok",
     "timestamp": 1533441753568,
     "user": {
      "displayName": "Edward Cheng",
      "photoUrl": "//lh4.googleusercontent.com/-ANnwjGu3IBk/AAAAAAAAAAI/AAAAAAAAAAc/qXXg6Jek9xw/s50-c-k-no/photo.jpg",
      "userId": "112554017642991017343"
     },
     "user_tz": -480
    },
    "id": "GyIQ7tpGPlnf",
    "outputId": "f3642b99-3ef2-43fe-ee42-e17b1ef50cbb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "import numpy as np\n",
    "np.random.seed(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5937,
     "status": "ok",
     "timestamp": 1533441759521,
     "user": {
      "displayName": "Edward Cheng",
      "photoUrl": "//lh4.googleusercontent.com/-ANnwjGu3IBk/AAAAAAAAAAI/AAAAAAAAAAc/qXXg6Jek9xw/s50-c-k-no/photo.jpg",
      "userId": "112554017642991017343"
     },
     "user_tz": -480
    },
    "id": "OLzoKlJRPlno",
    "outputId": "c75f10a4-951d-4b3b-ad5f-c8fc4dff0e96"
   },
   "outputs": [],
   "source": [
    "#載入手寫辨識的資料集\n",
    "from keras.datasets import mnist\n",
    "(x_train_image,y_train_label),\\\n",
    "(x_test_image,y_test_label)= mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "QEuQwa4nPlnu"
   },
   "outputs": [],
   "source": [
    "#指定測試集與訓練資料集\n",
    "x_Train =x_train_image.reshape(60000, 784).astype('float32')\n",
    "x_Test = x_test_image.reshape(10000, 784).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "YisRq-SxPln0"
   },
   "outputs": [],
   "source": [
    "# normalize inputs from 0-255 to 0-1\n",
    "x_Train_normalize = x_Train / 255\n",
    "x_Test_normalize = x_Test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "AHtfmzpfPln4"
   },
   "outputs": [],
   "source": [
    "#把LABEL轉成NUMERICAL Categorical \n",
    "y_Train_OneHot = np_utils.to_categorical(y_train_label)\n",
    "y_Test_OneHot = np_utils.to_categorical(y_test_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0s8PjvqJPln7"
   },
   "source": [
    "# 建立模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "N10v1eX2Pln8"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "jmaxrsXwPln-"
   },
   "outputs": [],
   "source": [
    "#建立模型\n",
    "model = Sequential()\n",
    "model.add(Dense(units=256, \n",
    "                input_dim=784, \n",
    "                kernel_initializer='normal', \n",
    "                activation='relu'))\n",
    "model.add(Dense(512))\n",
    "model.add(Dense(128))\n",
    "model.add(Dense(units=10, \n",
    "                kernel_initializer='normal', \n",
    "                activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jdoAZgv1PloK"
   },
   "source": [
    "# 訓練模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "5d5wrEL3PloK"
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', \n",
    "              optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 15426,
     "status": "ok",
     "timestamp": 1533441786624,
     "user": {
      "displayName": "Edward Cheng",
      "photoUrl": "//lh4.googleusercontent.com/-ANnwjGu3IBk/AAAAAAAAAAI/AAAAAAAAAAc/qXXg6Jek9xw/s50-c-k-no/photo.jpg",
      "userId": "112554017642991017343"
     },
     "user_tz": -480
    },
    "id": "VlpjoqQ9PloM",
    "outputId": "b91454c9-af9a-40fe-9736-b4a1c97462cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/10\n",
      "48000/48000 [==============================] - 6s 123us/step - loss: 0.2262 - acc: 0.9310 - val_loss: 0.1108 - val_acc: 0.9658\n",
      "Epoch 2/10\n",
      "48000/48000 [==============================] - 5s 113us/step - loss: 0.1083 - acc: 0.9666 - val_loss: 0.1046 - val_acc: 0.9689\n",
      "Epoch 3/10\n",
      "48000/48000 [==============================] - 5s 107us/step - loss: 0.0803 - acc: 0.9756 - val_loss: 0.1038 - val_acc: 0.9688\n",
      "Epoch 4/10\n",
      "48000/48000 [==============================] - 6s 115us/step - loss: 0.0618 - acc: 0.9803 - val_loss: 0.1287 - val_acc: 0.9673\n",
      "Epoch 5/10\n",
      "48000/48000 [==============================] - 6s 120us/step - loss: 0.0551 - acc: 0.9826 - val_loss: 0.1568 - val_acc: 0.9613\n",
      "Epoch 6/10\n",
      "48000/48000 [==============================] - 6s 125us/step - loss: 0.0502 - acc: 0.9844 - val_loss: 0.1220 - val_acc: 0.9703\n",
      "Epoch 7/10\n",
      "48000/48000 [==============================] - 6s 116us/step - loss: 0.0399 - acc: 0.9876 - val_loss: 0.1222 - val_acc: 0.9709\n",
      "Epoch 8/10\n",
      "48000/48000 [==============================] - 5s 112us/step - loss: 0.0363 - acc: 0.9887 - val_loss: 0.1375 - val_acc: 0.9699\n",
      "Epoch 9/10\n",
      "48000/48000 [==============================] - 6s 115us/step - loss: 0.0362 - acc: 0.9889 - val_loss: 0.1352 - val_acc: 0.9698\n",
      "Epoch 10/10\n",
      "48000/48000 [==============================] - 6s 116us/step - loss: 0.0296 - acc: 0.9910 - val_loss: 0.1248 - val_acc: 0.9752\n"
     ]
    }
   ],
   "source": [
    "train_history =model.fit(x=x_Train_normalize,\n",
    "                         y=y_Train_OneHot,validation_split=0.2, \n",
    "                         epochs=10, batch_size=32,verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pV1Ftdb0PloP"
   },
   "source": [
    "# 以圖形顯示訓練過程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "EEmuoa1dPloQ"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def show_train_history(train_history,train,validation):\n",
    "    plt.plot(train_history.history[train])\n",
    "    plt.plot(train_history.history[validation])\n",
    "    plt.title('Train History')\n",
    "    plt.ylabel(train)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['train', 'validation'], loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1451,
     "status": "ok",
     "timestamp": 1533441789030,
     "user": {
      "displayName": "Edward Cheng",
      "photoUrl": "//lh4.googleusercontent.com/-ANnwjGu3IBk/AAAAAAAAAAI/AAAAAAAAAAc/qXXg6Jek9xw/s50-c-k-no/photo.jpg",
      "userId": "112554017642991017343"
     },
     "user_tz": -480
    },
    "id": "fvE2WsejPloT",
    "outputId": "bb56e170-8850-409e-bbb2-eb6b5b89e528"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_train_history(train_history,'acc','val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1937,
     "status": "ok",
     "timestamp": 1533441791049,
     "user": {
      "displayName": "Edward Cheng",
      "photoUrl": "//lh4.googleusercontent.com/-ANnwjGu3IBk/AAAAAAAAAAI/AAAAAAAAAAc/qXXg6Jek9xw/s50-c-k-no/photo.jpg",
      "userId": "112554017642991017343"
     },
     "user_tz": -480
    },
    "id": "zIYyrnFWPloW",
    "outputId": "4bdbd1fa-6f25-49af-88d0-8d35f6e2ecaa"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_train_history(train_history,'loss','val_loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h1z2y0g-PloZ"
   },
   "source": [
    "# 評估模型準確率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1425,
     "status": "ok",
     "timestamp": 1533441792584,
     "user": {
      "displayName": "Edward Cheng",
      "photoUrl": "//lh4.googleusercontent.com/-ANnwjGu3IBk/AAAAAAAAAAI/AAAAAAAAAAc/qXXg6Jek9xw/s50-c-k-no/photo.jpg",
      "userId": "112554017642991017343"
     },
     "user_tz": -480
    },
    "id": "vMawurJqPloZ",
    "outputId": "31465f8d-cfad-49b8-dc9e-90d643e6b4a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 23us/step\n",
      "\n",
      "accuracy= 0.9766\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(x_Test_normalize, y_Test_OneHot)\n",
    "print()\n",
    "print('accuracy=',scores[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "default_view": {},
   "name": "Keras_Mnist_MLP_h256.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}