homework/Day_080_HW.ipynb

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    "# 請結合前面的知識與程式碼，比較不同的 optimizer 與 learning rate 組合對訓練的結果與影響\n",
    "### 常見的 optimizer 包含\n",
    "* SGD\n",
    "* RMSprop\n",
    "* AdaGrad\n",
    "* Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import keras\n",
    "\n",
    "# Disable GPU\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = keras.datasets.cifar10.load_data()"
   ]
  },
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    "## 資料前處理\n",
    "def preproc_x(x, flatten=True):\n",
    "    x = x / 255.\n",
    "    if flatten:\n",
    "        x = x.reshape((len(x), -1))\n",
    "    return x\n",
    "\n",
    "def preproc_y(y, num_classes=10):\n",
    "    if y.shape[-1] == 1:\n",
    "        y = keras.utils.to_categorical(y, num_classes)\n",
    "    return y    "
   ]
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   "cell_type": "code",
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    "x_train, y_train = train\n",
    "x_test, y_test = test\n",
    "\n",
    "# Preproc the inputs\n",
    "x_train = preproc_x(x_train)\n",
    "x_test = preproc_x(x_test)\n",
    "\n",
    "# Preprc the outputs\n",
    "y_train = preproc_y(y_train)\n",
    "y_test = preproc_y(y_test)"
   ]
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    "def build_mlp():\n",
    "    \"\"\"\n",
    "    Build your own model\n",
    "    \"\"\"\n",
    "    input_layer = keras.layers.Input(input_shape)\n",
    "    \n",
    "    for i,units in enumerate(num_neurons_list):\n",
    "        if i == 0:\n",
    "            x = keras.layers.Dense(units = units,activation = 'relu', name = 'hidden_layer' + str(i + 1))(input_layer)\n",
    "            \n",
    "        else:\n",
    "            x = keras.layers.Dense(units = units,activation = 'relu', name = 'hidden_layer' + str(i + 1))(x)\n",
    "            \n",
    "    out = keras.layers.Dense(units = output, activation = 'softmax', name = 'Output_layers')(x)\n",
    " \n",
    "    model = keras.models.Model(inputs = [input_layer], outputs = [out]  )\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 超參數設定\n",
    "\"\"\"\n",
    "Set your required experiment parameters\n",
    "\"\"\"\n",
    "LEARNING_RATE = [0.1, 0.01, 0.001, 0.0001]\n",
    "EPOCHS = 50\n",
    "BATCH_SIZE = 256\n",
    "MOMENTUM = 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "results = {}\n",
    "\"\"\"\n",
    "建立你的訓練與實驗迴圈並蒐集資料\n",
    "\"\"\"\n",
    "for lr in LEARNING_RATE:\n",
    "    keras.backend.clear_session()\n",
    "    \n",
    "    print('lr = ', lr)\n",
    "    \n",
    "    model = build_mlp(input_shape = x_train.shape[1:])\n",
    "    optimizer = keras.optimizers.adam(lr = lr)\n",
    "    \n",
    "    model.compile(loss = 'categorical_crossentropy', optimizer  = optimizer, metrics = ['accuracy'])\n",
    "    model.fit(x_train,y_train,\n",
    "             batch_size = BATCH_SIZE,\n",
    "             epochs = EPOCHS,\n",
    "             validation_data = (x_test, y_test),\n",
    "             shuffle = True)\n",
    "    \n",
    "    train_loss = model.history.history['loss']\n",
    "    valid_loss = model.history.history['val_loss']\n",
    "    train_acc = model.history.history['acc']\n",
    "    valid_acc = model.history.history['val_acc']\n",
    "    \n",
    "    exp_name_tag = \"exp-lr-%s\" % str(lr)\n",
    "    results[exp_name_tag] = {'train-loss': train_loss,\n",
    "                             'valid-loss': valid_loss,\n",
    "                             'train-acc': train_acc,\n",
    "                             'valid-acc': valid_acc}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "將實驗結果繪出\n",
    "\"\"\"\n",
    "color_bar = ['r','g','b','y']\n",
    "plt.figure(figsize = (8, 6))\n",
    "\n",
    "for i, cond in enumerate(results.keys()):\n",
    "    plt.plot(range(len(results[cond]['train-loss'])), results[cond]['train-loss'], '-', label = cond, color = color_bar[i])\n",
    "    plt.plot(range(len(results[cond]['valid-loss'])), results[cond]['valid-loss'], '--', label = cond, color = color_bar[i])\n",
    "plt.title(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize = (8, 6))\n",
    "for i, cond in enumerate(results.keys()):\n",
    "    plt.plot(range(len(results[cond]['train-acc'])), results[cond]['train-acc'], '-', label = cond, color = color_bar[i])\n",
    "    plt.plot(range(len(results[cond]['valid-acc'])), results[cond]['valid-acc'], '--', label = cond, color = color_bar[i])\n",
    "plt.title(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
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