Day12_Strides and Padding_HW.ipynb

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   "source": [
    "## 『作業內容』\n",
    "####   依照指示，透過調整Padding、Strides參數控制輸出Feature map大小\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## 『目標』\n",
    "####   了解輸出feature map尺寸變化原理"
   ]
  },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
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     "name": "stdout",
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     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 13, 13, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 13, 13, 32)        1184      \n",
      "=================================================================\n",
      "Total params: 1,184\n",
      "Trainable params: 1,184\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 13, 13, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 7, 7, 32)          1184      \n",
      "=================================================================\n",
      "Total params: 1,184\n",
      "Trainable params: 1,184\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 13, 13, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 8, 32)          1184      \n",
      "=================================================================\n",
      "Total params: 1,184\n",
      "Trainable params: 1,184\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_4 (InputLayer)         (None, 13, 13, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 4, 4, 32)          1184      \n",
      "=================================================================\n",
      "Total params: 1,184\n",
      "Trainable params: 1,184\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Convolution2D\n",
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "\n",
    "\n",
    "##kernel size=(6,6)\n",
    "##kernel數量：32\n",
    "\n",
    "## Same padding、strides=(1,1)\n",
    "classifier=Sequential()\n",
    "inputs = Input(shape=(13,13,1))\n",
    "x=Convolution2D(32,(6,6),strides=(1,1),padding='same')(inputs)\n",
    "model = Model(inputs=inputs, outputs=x)\n",
    "model.summary()\n",
    "## Same padding、strides=(2,2)\n",
    "classifier=Sequential()\n",
    "inputs = Input(shape=(13,13,1))\n",
    "x=Convolution2D(32,(6,6),strides=(2,2),padding='same')(inputs)\n",
    "model = Model(inputs=inputs, outputs=x)\n",
    "model.summary()\n",
    "## Valid padding、strides=(1,1)\n",
    "classifier=Sequential()\n",
    "inputs = Input(shape=(13,13,1))\n",
    "x=Convolution2D(32,(6,6),strides=(1,1),padding='valid')(inputs)\n",
    "model = Model(inputs=inputs, outputs=x)\n",
    "model.summary()\n",
    "## Valid padding、strides=(2,2)\n",
    "classifier=Sequential()\n",
    "inputs = Input(shape=(13,13,1))\n",
    "x=Convolution2D(32,(6,6),strides=(2,2),padding='valid')(inputs)\n",
    "model = Model(inputs=inputs, outputs=x)\n",
    "model.summary()\n"
   ]
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   "cell_type": "code",
   "execution_count": null,
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