1.1-keras-functional-api.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 如何使用Keras函數式API進行深度學習"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras使得創建深度學習模型變得快速而簡單。\n",
    "\n",
    "序貫(sequential)API允許您為大多數問題逐層堆疊創建模型。雖然說對很多的應用來說, 這樣的一個手法很簡單也解決\n",
    "了很多深度學習網絡結構的構建，但是它也有限制 - 它不允許你創建模型有共享層或有多個輸入或輸出的網絡。\n",
    "\n",
    "Keras中的函數式(functional)API是創建網絡模型的另一種方式，它提供了更多的靈活性，包括創建更複雜的模型。\n",
    "\n",
    "在這個文章中，您將了解如何使用Keras中更靈活的函數式(functional)API來定義深度學習模型。\n",
    "\n",
    "完成這個文章的相關範例, 您將知道：\n",
    "* Sequential和Functional API之間的區別。\n",
    "* 如何使用功能性(functional)API定義簡單的多層感知器(MLP)，卷積神經網絡(CNN)和遞歸神經網絡(RNN)模型。\n",
    "* 如何用共享層和多個輸入輸出來定義更複雜的模型。\n",
    "\n",
    "![network topology](https://www.codeproject.com/KB/AI/1215045/Popular-Neural-Network-Architecture.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform: Windows-7-6.1.7601-SP1\n",
      "Tensorflow version: 1.4.0\n",
      "Keras version: 2.1.1\n"
     ]
    }
   ],
   "source": [
    "# 這個Jupyter Notebook的環境\n",
    "import platform\n",
    "import tensorflow\n",
    "import keras\n",
    "print(\"Platform: {}\".format(platform.platform()))\n",
    "print(\"Tensorflow version: {}\".format(tensorflow.__version__))\n",
    "print(\"Keras version: {}\".format(keras.__version__))\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import numpy as np\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Keras 序貫模型 (Sequential Models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras提供了一個Sequential模型API。\n",
    "\n",
    "它是創建深度學習模型的一種相對簡單的方法，我們透過創建Kears的Sequential類別實例(instance), 然後創建模型圖層\n",
    "並添加到其中。\n",
    "\n",
    "例如，可以定義多個圖層並將以陣列的方式一次做為參數傳遞給Sequential："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "# 構建模型\n",
    "model = Sequential([Dense(2, input_shape=(1,)), Dense(1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "當然我們也可以一層一層也分段添加上去："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "# 構建模型\n",
    "model = Sequential()\n",
    "model.add(Dense(2, input_shape=(1,)))\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sequential模型API對於在大多數情況下非常有用與方便，但也有一些局限性。\n",
    "例如，網絡拓撲結構可能具有多個不同輸入，產生多個輸出或重複使用共享圖層的複雜模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Keras 函數式(functional)API構建模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras函數式(functional)API為構建網絡模型提供了更為靈活的方式。\n",
    "\n",
    "它允許您定義多個輸入或輸出模型以及共享圖層的模型。除此之外，它允許您定義動態(ad-hoc)的非週期性(acyclic)網絡圖。\n",
    "\n",
    "模型是通過創建層的實例(layer instances)並將它們直接相互連接成對來定義的，然後定義一個模型(model)來指定那些層是要作為\n",
    "這個模型的輸入和輸出。\n",
    "\n",
    "讓我們依次看看Keras功能(functional)API的三個獨特特性："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 定義輸入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "與Sequential模型不同，您必須創建獨立的Input層物件的instance並定義輸入數據張量的維度形狀(tensor shape)。\n",
    "\n",
    "輸入層採用一個張量形狀參數(tensor shape)，它是一個tuple，用於宣吿輸入張量的維度。\n",
    "\n",
    "例如: 我們要把MNIST的每張圖像(28x28)打平成一個一維(784)的張量做為一個多層感知器(MLP)的Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import Input\n",
    "\n",
    "mnist_input = Input(shape=(784,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 連接不同的網絡層"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型中的神經層是成對連接的,就像是一個樂高積木一樣有一面是凸一面是凹, 一個神經層的輸出會接到另一個神經層的輸入。\n",
    "\n",
    "這是通過在定義每個新神經層時指定輸入的來源來完成的。使用括號表示法，以便在創建圖層之後，指定作為輸入的神經層。\n",
    "\n",
    "我們用一個簡短的例子來說明這一點。我們可以像上面那樣創建輸入層，然後創建一個隱藏層作為密集層，它接收來自輸入層的輸入。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import Input\n",
    "from keras.layers import Dense\n",
    "\n",
    "mnist_input = Input(shape=(784,))\n",
    "hidden = Dense(512)(mnist_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正是這種逐層連接的方式賦予功能性(functional)API靈活性。您可以看到開始一些動態的神經網絡是多麼容易。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 創建模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在創建所有模型圖層並將它們連接在一起之後，您必須定義一個模型(Model)物件的instance。\n",
    "\n",
    "與Sequential API一樣，這個模型是您可以用於總結(summarize)，擬合(fit)，評估(evaluate)和預測(predict)。\n",
    "\n",
    "Keras提供了一個Model類別，您可以使用它從創建的圖層創建模型的instance。它會要求您只指定整個模型的第一個輸入層和最後一個的輸出層。例如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import Input\n",
    "from keras.layers import Dense\n",
    "from keras.models import Model\n",
    "\n",
    "mnist_input = Input(shape=(784,))\n",
    "hidden = Dense(512)(mnist_input)\n",
    "\n",
    "model = Model(inputs=mnist_input, outputs=hidden)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "現在我們已經知道了Keras函數式API的所有關鍵部分，讓我們通過定義一系列不同的模型來開展工作。\n",
    "\n",
    "每個範例都是可以執行的，並打印網絡結構及產生網絡圖表。我建議你為自己的模型做這個事情，以明確你所定義的是什麼樣的網絡結構。\n",
    "\n",
    "我希望這些範例能夠在將來使用函數式API定義自己的模型時為您提供模板。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.標準網絡模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在開始使用函數式API時，最好先看一些標準的神經網絡模型是如何定義的。\n",
    "\n",
    "在本節中，我們將著眼於定義一個簡單的多層感知器(MLP)，卷積神經網絡(CNN)和遞歸神經網絡(RNN)。\n",
    "\n",
    "這些範例將為以後了解更複雜的網絡構建提供基礎。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 多層感知器(Multilayer Perceptron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "讓我們來定義了一個多類別分類(multi-class classification)的多層感知器(MLP)模型。\n",
    "\n",
    "該模型有784個輸入，3個隱藏層，512,216和128個隱藏神經元，輸出層有10個輸出。\n",
    "\n",
    "在每個隱藏層中使用`relu`激活函數，並且在輸出層中使用`softmax`激活函數進行多類別分類。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "hidden1 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "hidden2 (Dense)              (None, 216)               110808    \n",
      "_________________________________________________________________\n",
      "hidden3 (Dense)              (None, 128)               27776     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 541,794\n",
      "Trainable params: 541,794\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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aT7tt+pdFjxd1jDi6FcCOv6Dy+vXrOHnyZF99N4x+4bWfatpoDMPAtWvXvOlU\nUgP9pzgafBsugP6VNf/Y3/82XAB37twZ+jP1pw333zI57xssG+4ISIOFASRRDCCJYgBJFANIohhA\nEsUAkigGkEQxgCSKASRRDCCJYgBJFANIorr2bpjN9v9yqTs6DuDevXsBAMeOHeu4GBos+rnvRMef\nCSHqBOeAJIoBJFEMIIliAEnU/wH9UjU/svSt/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 多層感知器(MLP)模型\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.utils import plot_model\n",
    "\n",
    "mnist_input = Input(shape=(784,), name='input')\n",
    "hidden1 = Dense(512, activation='relu', name='hidden1')(mnist_input)\n",
    "hidden2 = Dense(216, activation='relu', name='hidden2')(hidden1)\n",
    "hidden3 = Dense(128, activation='relu', name='hidden3')(hidden2)\n",
    "output = Dense(10, activation='softmax', name='output')(hidden3)\n",
    "\n",
    "model = Model(inputs=mnist_input, outputs=output)\n",
    "\n",
    "# 打印網絡結構\n",
    "model.summary()\n",
    "\n",
    "# 產生網絡拓撲圖\n",
    "plot_model(model, to_file='multilayer_perceptron_graph.png')\n",
    "\n",
    "# 秀出網絡拓撲圖\n",
    "Image('multilayer_perceptron_graph.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 3.2 卷積神經網絡(CNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們將定義一個用於圖像分類的卷積神經網絡(convolutional neural network)。\n",
    "\n",
    "該模型接收灰階的28×28圖像作為輸入，然後有一個作為特徵提取器的兩個卷積和池化層的序列，\n",
    "然後是一個完全連接層來解釋特徵，並且具有用於10類預測的`softmax`激活的輸出層。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv1 (Conv2D)               (None, 25, 25, 128)       2176      \n",
      "_________________________________________________________________\n",
      "pool1 (MaxPooling2D)         (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2 (Conv2D)               (None, 9, 9, 64)          131136    \n",
      "_________________________________________________________________\n",
      "pool2 (MaxPooling2D)         (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "hidden1 (Dense)              (None, 4, 4, 64)          4160      \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 4, 4, 10)          650       \n",
      "=================================================================\n",
      "Total params: 138,122\n",
      "Trainable params: 138,122\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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+Pg4AyGazsCwLMzMzWFtb2/T6ASCVSiGVSrW97cPDw3juuedQLBZr\nrkSVSgWzs7OwLAvj4+NYXFz02+fn5/3tKhaL/jLr6+uhdejX630qh3/1+tjSVB9JJBIqkUi0/Drb\ntlUmk1FKKeW6rrJtW9m2rTzP89sAqODmlsvlmrZ6XwNQS0tLSimlPM9TyWRSAVCrq6ubWr9SSjmO\noxzHabqNUa/VPM9TAFQymfTb9H7I5/NKKaUWFhYUAFUqlZRt2zXbpesNriOdTqtyuez34ThOqIZG\nfcSVy+XqbpcpfVVNO6HQB8J1Xb9taWlJAfAPllLRJ1WckzaqrVQqKQAqnU5vev1xNXutfD6fz0fW\nowMYt97gftXhj9tHHAxFE+2EQr9rB+l3Ttu2/bZOhqLd1/YyFMGrgXzErVfv23w+7191g5r1EQdD\n0UQ7oej2SbsVQqHfBILv0K2GKKptdXU1dOIHr4xx+oijH0Ox5Sfatm0DQOQtyWQy2dW+u73+uK5c\nuQIAOHToUM1zwRsCrdq3bx8KhQJKpRKSySSef/55zM7OdrSPfrTlQ5FIJAAAb731lt+mb0126zcN\n6pPgySef7Mr6W1GpVPDKK6/Atm0cPnzYb89kMgCACxcu+PtD3ymKy7IsVKtVjI6O4ty5cyiVSnj+\n+ec72kdfMn2pCmpn+OR5nn+3SU8K8/l86C6KUqrmjpGejCNwx0UPFVzX9YcKehk9add3YYLzlc2s\nP87dJz08AhAa2+s7ScFt14J3xIKPcrkcek6vL9iHXhc+HJLpO1Dlcjk0hGrUR1z9OHzqq2ravSXr\nuq7KZDKhE1hODMvlsn9SFgoFpZTybyfqk0DfVXIcJ3RiAOFbmZlMpmPrbxaKqJNOP9LptH9LNUq5\nXPZvoyaTSf9kletp1KYDrPuL20dc/RiKvvsFywD66nfJ6g+r+mg33Vb075Ltp/275ecURJ3GUDQQ\nvKPFb7jbPhiKBkZGRiL/T7e3vvv7FP2kn8a51Du8UhAJDAWRwFAQCQwFkcBQEAkMBZHAUBAJDAWR\nwFAQCQwFkcBQEAkMBZHAUBAJffddsnNzc9jY2DBdBvXIxYsXTZdQo69CMTk5yUBsMxMTE9izZ4/p\nMkL66me0ifoB5xREAkNBJDAURAJDQST8P1Rtf5S8r87GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 卷積神經網絡(CNN)\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.pooling import MaxPool2D\n",
    "from keras.utils import plot_model\n",
    "\n",
    "mnist_input = Input(shape=(28, 28, 1), name='input')\n",
    "\n",
    "conv1 = Conv2D(128, kernel_size=4, activation='relu', name='conv1')(mnist_input)\n",
    "pool1 = MaxPool2D(pool_size=(2, 2), name='pool1')(conv1)\n",
    "\n",
    "conv2 = Conv2D(64, kernel_size=4, activation='relu', name='conv2')(pool1)\n",
    "pool2 = MaxPool2D(pool_size=(2, 2), name='pool2')(conv2)\n",
    "\n",
    "hidden1 = Dense(64, activation='relu', name='hidden1')(pool2)\n",
    "output = Dense(10, activation='softmax', name='output')(hidden1)\n",
    "model = Model(inputs=mnist_input, outputs=output)\n",
    "\n",
    "# 打印網絡結構\n",
    "model.summary()\n",
    "\n",
    "# 產生網絡拓撲圖\n",
    "plot_model(model, to_file='convolutional_neural_network.png')\n",
    "\n",
    "# 秀出網絡拓撲圖\n",
    "Image('convolutional_neural_network.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 遞歸神經網絡(RNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們將定義一個長期短期記憶(LSTM)遞歸神經網絡用於圖像分類。\n",
    "\n",
    "該模型預期一個特徵的784個時間步驟作為輸入。該模型具有單個LSTM隱藏層以從序列中提取特徵，\n",
    "接著是完全連接的層來解釋LSTM輸出，接著是用於進行10類別預測的輸出層。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           (None, 784, 1)            0         \n",
      "_________________________________________________________________\n",
      "lstm1 (LSTM)                 (None, 128)               66560     \n",
      "_________________________________________________________________\n",
      "hidden1 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 84,362\n",
      "Trainable params: 84,362\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 遞歸神經網絡(RNN)\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.utils import plot_model\n",
    "\n",
    "mnist_input = Input(shape=(784, 1), name='input') # 把每一個像素想成是一序列有前後關係的time_steps\n",
    "lstm1 = LSTM(128, name='lstm1')(mnist_input)\n",
    "hidden1 = Dense(128, activation='relu', name='hidden1')(lstm1)\n",
    "output = Dense(10, activation='softmax', name='output')(hidden1)\n",
    "model = Model(inputs=mnist_input, outputs=output)\n",
    "\n",
    "# 打印網絡結構\n",
    "model.summary()\n",
    "\n",
    "# 產生網絡拓撲圖\n",
    "plot_model(model, to_file='recurrent_neural_network.png')\n",
    "\n",
    "# 秀出網絡拓撲圖\n",
    "Image('recurrent_neural_network.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.共享層模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "多個神經層可以共享一個神經層的輸出來當成輸入。\n",
    "\n",
    "例如，一個輸入可能可以有多個不同的特徵提取層，或者多個神經層用於解釋特徵提取層的輸出。\n",
    "\n",
    "我們來看這兩個例子。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 共享輸入層 (Shared Input Layer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們定義具有不同大小的內核的多個卷積層來解釋圖像輸入。\n",
    "\n",
    "該模型使用28×28像素的灰階圖像。有兩個CNN特徵提取子模型共享這個輸入;第一個具有4的內核大小和第二個8的內核大小。\n",
    "這些特徵提取子模型的輸出被平坦化(flatten)為向量(vector)，並且被串連成一個長向量, 然後被傳遞到完全連接的層以\n",
    "用於在最終輸出層之前進行10類別預測。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input (InputLayer)              (None, 28, 28, 1)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 25, 25, 32)   544         input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (Conv2D)                  (None, 21, 21, 16)   1040        input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "pool1 (MaxPooling2D)            (None, 12, 12, 32)   0           conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "pool2 (MaxPooling2D)            (None, 10, 10, 16)   0           conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "flatten_9 (Flatten)             (None, 4608)         0           pool1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "flatten_10 (Flatten)            (None, 1600)         0           pool2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 6208)         0           flatten_9[0][0]                  \n",
      "                                                                 flatten_10[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "hidden1 (Dense)                 (None, 64)           397376      concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "output (Dense)                  (None, 10)           650         hidden1[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 399,610\n",
      "Trainable params: 399,610\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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HPR9rPWiahtLSUtTW1sLr9UqvM91F2+YdbZt48FjisdTmJ5MrKipElMGGhG9+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\nF1+MY8eOhQ23WCwQQmD48OFYsWIFpk2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5E/zb5/MBAL744ovgsMzMTJx33nkpr43IiDQh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\nYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 共享輸入層 \n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, Flatten\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.pooling import MaxPool2D\n",
    "from keras.layers.merge import concatenate\n",
    "from keras.utils import plot_model\n",
    "\n",
    "# 輸入層\n",
    "mnist_input = Input(shape=(28, 28, 1), name='input')\n",
    "\n",
    "# 第一個特徵提取層\n",
    "conv1 = Conv2D(32, kernel_size=4, activation='relu', name='conv1')(mnist_input) # <-- 看這裡\n",
    "pool1 = MaxPool2D(pool_size=(2, 2), name='pool1')(conv1)\n",
    "flat1 = Flatten()(pool1)\n",
    "\n",
    "# 第二個特徵提取層\n",
    "conv2 = Conv2D(16, kernel_size=8, activation='relu', name='conv2')(mnist_input) # <-- 看這裡\n",
    "pool2 = MaxPool2D(pool_size=(2, 2), name='pool2')(conv2)\n",
    "flat2 = Flatten()(pool2)\n",
    "\n",
    "# 把兩個特徵提取層的結果併起來\n",
    "merge = concatenate([flat1, flat2])\n",
    "\n",
    "# 進行全連結層\n",
    "hidden1 = Dense(64, activation='relu', name='hidden1')(merge)\n",
    "\n",
    "# 輸出層\n",
    "output = Dense(10, activation='softmax', name='output')(hidden1)\n",
    "\n",
    "# 以Model來組合整個網絡\n",
    "model = Model(inputs=mnist_input, outputs=output)\n",
    "\n",
    "# 打印網絡結構\n",
    "model.summary()\n",
    "\n",
    "# plot graph\n",
    "plot_model(model, to_file='shared_input_layer.png')\n",
    "\n",
    "# 秀出網絡拓撲圖\n",
    "Image('shared_input_layer.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 4.2 共享特徵提取層 (Shared Feature Extraction Layer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "我們將使用兩個並行子模型來解釋用於序列分類的LSTM特徵提取器的輸出。\n",
    "\n",
    "該模型的輸入是1個特徵的784個時間步長。具有10個存儲單元的LSTM層解釋這個序列。第一種解釋模型是淺層單連通層，\n",
    "第二層是深層3層模型。兩個解釋模型的輸出連接成一個長向量，傳遞給用於進行10類別分類預測的輸出層。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input (InputLayer)              (None, 784, 1)       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lstm1 (LSTM)                    (None, 128)          66560       input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "interp21 (Dense)                (None, 64)           8256        lstm1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "interp22 (Dense)                (None, 32)           2080        interp21[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "interp1 (Dense)                 (None, 10)           1290        lstm1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "interp23 (Dense)                (None, 16)           528         interp22[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "merge (Concatenate)             (None, 26)           0           interp1[0][0]                    \n",
      "                                                                 interp23[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "output (Dense)                  (None, 10)           270         merge[0][0]                      \n",
      "==================================================================================================\n",
      "Total params: 78,984\n",
      "Trainable params: 78,984\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZFajg6quvxpYtW3DkyBFzWnt7O/bu3Yu9e/ciJycHR44cwQUXXIBXX31VYqWU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\n44wz4loHJR+GFgVYsmQJ5s+fD5/PZ/Z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QIiKlMLSISCkMLSJSCkOLiJTC0CIipTC0iEgp\nDC0iUgq/5UGyG2+8UXYJREphT0uSSZMmYebMmbLLoASbOXNmwC8JUfT4HfFEpBJ+RzwRqYWhRURK\nYWgRkVIYWkSklP8PsfsSZnX0efAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.layers.merge import concatenate\n",
    "from keras.utils import plot_model\n",
    "# 輸入層\n",
    "mnist_input = Input(shape=(784, 1), name='input') # 把每一個像素想成是一序列有前後關係的time_steps\n",
    "\n",
    "# 特徵提取層\n",
    "extract1 = LSTM(128, name='lstm1')(mnist_input)\n",
    "\n",
    "# 第一個解釋層\n",
    "interp1 = Dense(10, activation='relu', name='interp1')(extract1) # <-- 看這裡\n",
    "\n",
    "# 第二個解釋層\n",
    "interp21 = Dense(64, activation='relu', name='interp21')(extract1) # <-- 看這裡\n",
    "interp22 = Dense(32, activation='relu', name='interp22')(interp21)\n",
    "interp23 = Dense(16, activation='relu', name='interp23')(interp22)\n",
    "\n",
    "# 把兩個特徵提取層的結果併起來\n",
    "merge = concatenate([interp1, interp23], name='merge')\n",
    "\n",
    "# 輸出層\n",
    "output = Dense(10, activation='softmax', name='output')(merge)\n",
    "\n",
    "# 以Model來組合整個網絡\n",
    "model = Model(inputs=mnist_input, outputs=output)\n",
    "\n",
    "# 打印網絡結構\n",
    "model.summary()\n",
    "\n",
    "# plot graph\n",
    "plot_model(model, to_file='shared_feature_extractor.png')\n",
    "\n",
    "# 秀出網絡拓撲圖\n",
    "Image('shared_feature_extractor.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.多種輸入和輸出模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "函數式(functional)API也可用於開發具有多個輸入或多個輸出的模型的更複雜的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 多輸入模型\n",
    "\n",
    "我們將開發一個圖像分類模型，將圖像的兩個版本作為輸入，每個圖像的大小不同。特別是一個灰階的64×64版本和一個32×32的彩色版本。分離的特徵提取CNN模型對每個模型進行操作，然後將兩個模型的結果連接起來進行解釋和最終預測。\n",
    "\n",
    "請注意，在創建Model（）實例(instance)時，我們將兩個輸入圖層定義為一個數組(array)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "img_gray_bigsize (InputLayer)   (None, 64, 64, 1)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "img_rgb_smallsize (InputLayer)  (None, 32, 32, 3)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv11 (Conv2D)                 (None, 61, 61, 32)   544         img_gray_bigsize[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv21 (Conv2D)                 (None, 29, 29, 32)   1568        img_rgb_smallsize[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "pool11 (MaxPooling2D)           (None, 30, 30, 32)   0           conv11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "pool21 (MaxPooling2D)           (None, 14, 14, 32)   0           conv21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv12 (Conv2D)                 (None, 27, 27, 16)   8208        pool11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv22 (Conv2D)                 (None, 11, 11, 16)   8208        pool21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "pool12 (MaxPooling2D)           (None, 13, 13, 16)   0           conv12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "pool22 (MaxPooling2D)           (None, 5, 5, 16)     0           conv22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "flatten_11 (Flatten)            (None, 2704)         0           pool12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "flatten_12 (Flatten)            (None, 400)          0           pool22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_6 (Concatenate)     (None, 3104)         0           flatten_11[0][0]                 \n",
      "                                                                 flatten_12[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "hidden1 (Dense)                 (None, 128)          397440      concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "hidden2 (Dense)                 (None, 64)           8256        hidden1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "output (Dense)                  (None, 10)           650         hidden2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 424,874\n",
      "Trainable params: 424,874\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
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mdN999xmsBkFGgIAx1113nW655RZVVFQwgRIIiLq6Ovet1mvXrtWGDRsMV4Sg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\nODjndul02riuawYHB40xxoyMjBhJZmxsLK9/r8ZUKpVX43STk5NGkhk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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 多輸入模型\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, Flatten\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.pooling import MaxPool2D\n",
    "from keras.layers.merge import concatenate\n",
    "from keras.utils import plot_model\n",
    "\n",
    "# 第一個輸入層\n",
    "img_gray_bigsize = Input(shape=(64, 64, 1), name='img_gray_bigsize')\n",
    "conv11 = Conv2D(32, kernel_size=4, activation='relu', name='conv11')(img_gray_bigsize)\n",
    "pool11 = MaxPool2D(pool_size=(2, 2), name='pool11')(conv11)\n",
    "conv12 = Conv2D(16, kernel_size=4, activation='relu', name='conv12')(pool11)\n",
    "pool12 = MaxPool2D(pool_size=(2, 2), name='pool12')(conv12)\n",
    "flat1 = Flatten()(pool12)\n",
    "\n",
    "# 第二個輸入層\n",
    "img_rgb_smallsize = Input(shape=(32, 32, 3), name='img_rgb_smallsize')\n",
    "conv21 = Conv2D(32, kernel_size=4, activation='relu', name='conv21')(img_rgb_smallsize)\n",
    "pool21 = MaxPool2D(pool_size=(2, 2), name='pool21')(conv21)\n",
    "conv22 = Conv2D(16, kernel_size=4, activation='relu', name='conv22')(pool21)\n",
    "pool22 = MaxPool2D(pool_size=(2, 2), name='pool22')(conv22)\n",
    "flat2 = Flatten()(pool22)\n",
    "\n",
    "# 把兩個特徵提取層的結果併起來\n",
    "merge = concatenate([flat1, flat2])\n",
    "\n",
    "# 用隱藏的全連結層來解釋特徵\n",
    "hidden1 = Dense(128, activation='relu', name='hidden1')(merge)\n",
    "hidden2 = Dense(64, activation='relu', name='hidden2')(hidden1)\n",
    "\n",
    "# 輸出層\n",
    "output = Dense(10, activation='softmax', name='output')(hidden2)\n",
    "\n",
    "# 以Model來組合整個網絡\n",
    "model = Model(inputs=[img_gray_bigsize, img_rgb_smallsize], outputs=output)\n",
    "\n",
    "# 打印網絡結構\n",
    "model.summary()\n",
    "\n",
    "# plot graph\n",
    "plot_model(model, to_file='multiple_inputs.png')\n",
    "\n",
    "# 秀出網絡拓撲圖\n",
    "Image('multiple_inputs.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 5.2 多輸出模型\n",
    "\n",
    "我們將開發一個模型，進行兩種不同類型的預測。給定一個特徵的784個時間步長的輸入序列，該模型將對該序列進行分類並輸出具有相同長度的新序列。\n",
    "\n",
    "LSTM層解釋輸入序列並返回每個時間步的隱藏狀態。第一個輸出模型創建一個堆疊的LSTM，解釋這些特徵，並進行多類別預測。第二個輸出模型使用相同的輸出層對每個輸入時間步進行多類別預測。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input (InputLayer)              (None, 784, 1)       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "extract (LSTM)                  (None, 784, 64)      16896       input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "class11 (LSTM)                  (None, 32)           12416       extract[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "class12 (Dense)                 (None, 32)           1056        class11[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "output1 (Dense)                 (None, 10)           330         class12[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "output2 (TimeDistributed)       (None, 784, 10)      650         extract[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 31,348\n",
      "Trainable params: 31,348\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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OtFSJRML4/X4zMDBQsJ9K72+jqdj9sqOjw10m2zobGBgwfr+/4D6ZaST7XqXk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\nM2WM0bp163TeeedpaGhIzc3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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 多輸出模型\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.layers.wrappers import TimeDistributed\n",
    "from keras.utils import plot_model\n",
    "\n",
    "# 輸入層\n",
    "mnist_input = Input(shape=(784, 1), name='input') # 把每一個像素想成是一序列有前後關係的time_steps\n",
    "\n",
    "# 特徵擷取層\n",
    "extract = LSTM(64, return_sequences=True, name='extract')(mnist_input)\n",
    "\n",
    "# 分類輸出\n",
    "class11 = LSTM(32, name='class11')(extract)\n",
    "class12 = Dense(32, activation='relu', name='class12')(class11)\n",
    "output1 = Dense(10, activation='softmax', name='output1')(class12)\n",
    "\n",
    "# 序列輸出\n",
    "output2 = TimeDistributed(Dense(10, activation='softmax'), name='output2')(extract)\n",
    "\n",
    "# 以Model來組合整個網絡\n",
    "model = Model(inputs=mnist_input, outputs=[output1, output2])\n",
    "\n",
    "# 打印網絡結構\n",
    "model.summary()\n",
    "\n",
    "# plot graph\n",
    "plot_model(model, to_file='multiple_outputs.png')\n",
    "\n",
    "# 秀出網絡拓撲圖\n",
    "Image('multiple_outputs.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.最佳實踐\n",
    "以上有一些小技巧可以幫助你充分利用函數式API定義自己的模型。\n",
    "* **一致性的變量名稱命名** 對輸入（可見）和輸出神經層（輸出）使用相同的變量名，甚至可以使用隱藏層（hidden1，hidden2）。這將有助於正確地將許多的神經層連接在一起。\n",
    "* **檢查圖層摘要** 始終打印模型摘要並查看圖層輸出，以確保模型如您所期望的那樣連接在一起。\n",
    "* **查看網絡拓樸圖像** 總是儘可能地創建網絡拓樸圖像，並審查它，以確保一切按照你的意圖連接在一起。\n",
    "* **命名圖層** 您可以為圖層指定名稱,這些名稱可以讓你的模型圖形摘要和網絡拓樸圖像更容易被解讀。例如：Dense（1，name ='hidden1'）。\n",
    "* **獨立子模型** 考慮分離出子模型的發展，並最終將子模型結合在一起。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 總結 (Conclusion)\n",
    "\n",
    "在這篇文章中有一些個人學習到的一些有趣的重點:\n",
    "    \n",
    "* 使用Keras也可以很靈活地來建構複雜的深度學習網絡\n",
    "* 每一種深度學習網絡拓樸基本上都可以找的到一篇論文\n",
    "* 了解每種深度學習網絡拓樸架構的原理與應用的方向是強化內力的不二法門"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "參考: \n",
    "* [How to Use the Keras Functional API for Deep Learning](https://machinelearningmastery.com/keras-functional-api-deep-learning/)\n",
    "* [Keras官網](http://keras.io/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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