homework/Day014_Batch+Normalization_HW.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 『本次練習內容』\n",
    "#### 搭建 Conv2D-BN-Activation層\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 『本次練習目的』\n",
    "  #### 了解如何搭建CNN基礎架構，Conv2D-BN-Activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential  #用來啟動 NN\n",
    "from keras.layers import Conv2D  # Convolution Operation\n",
    "from keras.layers import MaxPooling2D # Pooling\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense # Fully Connected Networks\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.layers import Activation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 依照指示建立模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
      "=================================================================\n",
      "Total params: 10,400\n",
      "Trainable params: 10,272\n",
      "Non-trainable params: 128\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_shape = (32, 32, 3)\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "##  Conv2D-BN-Activation('sigmoid') \n",
    "\n",
    "#BatchNormalization主要參數：\n",
    "#momentum: Momentum for the moving mean and the moving variance.\n",
    "#epsilon: Small float added to variance to avoid dividing by zero.\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same', input_shape=input_shape))\n",
    "model.add(BatchNormalization(momentum=0.99, epsilon=0.001)) \n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "##、 Conv2D-BN-Activation('relu')\n",
    "model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same', input_shape=input_shape))\n",
    "model.add(BatchNormalization(momentum=0.99, epsilon=0.001)) \n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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