Keras入门与残差网络.ipynb

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 "cells": [
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   "source": [
    "import numpy as np\n",
    "from keras import layers\n",
    "from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D\n",
    "from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.models import Model\n",
    "from keras.preprocessing import image\n",
    "from keras.utils import layer_utils\n",
    "from keras.utils.data_utils import get_file\n",
    "from keras.applications.imagenet_utils import preprocess_input\n",
    "import pydot\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.utils import plot_model\n",
    "import kt_utils \n",
    "\n",
    "import keras.backend as K\n",
    "K.set_image_data_format('channels_last')\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 600\n",
      "Number of test examples: 150\n",
      "X_train's shape is: (600, 64, 64, 3)\n",
      "X_test's shape is: (150, 64, 64, 3)\n",
      "Y_train's shape is: (600, 1)\n",
      "Y_test's shape is: (150, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train_org,Y_train_org,X_test_org,Y_test_org,classed=kt_utils.load_dataset()\n",
    "#Normalize image vectors\n",
    "X_train = X_train_org/255\n",
    "X_test = X_test_org/255\n",
    "#reshape\n",
    "Y_train = Y_train_org.T\n",
    "Y_test = Y_test_org.T\n",
    "\n",
    "print(\"Number of training examples: \"+str(X_train.shape[0]))\n",
    "print(\"Number of test examples: \"+str(X_test.shape[0]))\n",
    "print(\"X_train's shape is: \"+str(X_train.shape))\n",
    "print(\"X_test's shape is: \"+str(X_test.shape))\n",
    "print(\"Y_train's shape is: \"+str(Y_train.shape))\n",
    "print(\"Y_test's shape is: \"+str(Y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def HappyModel(input_shape):\n",
    "    \"\"\"\n",
    "    实现一个检测笑容的模型\n",
    "    \n",
    "    参数：\n",
    "        input_shape - 输入的数据的维度\n",
    "    返回：\n",
    "        model - 创建的Keras的模型\n",
    "        \n",
    "    \"\"\"\n",
    "    X_input = Input(input_shape)\n",
    "    #使用0填充：X_input周围填充0\n",
    "    X = ZeroPadding2D((3,3))(X_input)\n",
    "    #对X使用CONV->BN->RELU块\n",
    "    X = Conv2D(32,(7,7),strides=(1,1),name='conv0')(X)\n",
    "    X = BatchNormalization(axis=3,name='bn0')(X)\n",
    "    X = Activation('relu')(X)\n",
    "    \n",
    "    #最大池化层\n",
    "    X = MaxPooling2D((2,2),name='max_pool')(X)\n",
    "    #降维 将矩阵转化为向量+全连接层\n",
    "    X = Flatten()(X)\n",
    "    X = Dense(1,activation='sigmoid',name='fc')(X)\n",
    "    \n",
    "    #创建模型 \n",
    "    model = Model(inputs=X_input,outputs=X,name='HappyModel')\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W1230 16:47:26.747794  6248 deprecation_wrapper.py:119] From C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "W1230 16:47:26.974189  6248 deprecation.py:323] From C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "W1230 16:47:27.900872  6248 deprecation_wrapper.py:119] From C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "600/600 [==============================] - 8s 13ms/step - loss: 2.4229 - accuracy: 0.5683\n",
      "Epoch 2/40\n",
      "600/600 [==============================] - 6s 10ms/step - loss: 0.6949 - accuracy: 0.7417\n",
      "Epoch 3/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.3492 - accuracy: 0.8583\n",
      "Epoch 4/40\n",
      "600/600 [==============================] - 6s 10ms/step - loss: 0.2187 - accuracy: 0.9183\n",
      "Epoch 5/40\n",
      "600/600 [==============================] - 7s 12ms/step - loss: 0.1618 - accuracy: 0.9317\n",
      "Epoch 6/40\n",
      "600/600 [==============================] - 8s 13ms/step - loss: 0.1190 - accuracy: 0.9600\n",
      "Epoch 7/40\n",
      "600/600 [==============================] - 9s 15ms/step - loss: 0.0949 - accuracy: 0.9717\n",
      "Epoch 8/40\n",
      "600/600 [==============================] - 6s 10ms/step - loss: 0.0950 - accuracy: 0.9733\n",
      "Epoch 9/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0843 - accuracy: 0.9833\n",
      "Epoch 10/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0702 - accuracy: 0.9867\n",
      "Epoch 11/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.0650 - accuracy: 0.9817\n",
      "Epoch 12/40\n",
      "600/600 [==============================] - 8s 13ms/step - loss: 0.0641 - accuracy: 0.9850\n",
      "Epoch 13/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0593 - accuracy: 0.9817\n",
      "Epoch 14/40\n",
      "600/600 [==============================] - 7s 12ms/step - loss: 0.0602 - accuracy: 0.9867\n",
      "Epoch 15/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0708 - accuracy: 0.9783\n",
      "Epoch 16/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.0549 - accuracy: 0.9800\n",
      "Epoch 17/40\n",
      "600/600 [==============================] - 7s 12ms/step - loss: 0.0378 - accuracy: 0.9900\n",
      "Epoch 18/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0468 - accuracy: 0.9867\n",
      "Epoch 19/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0369 - accuracy: 0.9917\n",
      "Epoch 20/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0321 - accuracy: 0.9933\n",
      "Epoch 21/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.0308 - accuracy: 0.9917\n",
      "Epoch 22/40\n",
      "600/600 [==============================] - 7s 12ms/step - loss: 0.0304 - accuracy: 0.9933\n",
      "Epoch 23/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.0292 - accuracy: 0.9933\n",
      "Epoch 24/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.0259 - accuracy: 0.9950\n",
      "Epoch 25/40\n",
      "600/600 [==============================] - 6s 10ms/step - loss: 0.0331 - accuracy: 0.9917\n",
      "Epoch 26/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.0331 - accuracy: 0.9883\n",
      "Epoch 27/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0307 - accuracy: 0.9917\n",
      "Epoch 28/40\n",
      "600/600 [==============================] - 7s 12ms/step - loss: 0.0252 - accuracy: 0.9933\n",
      "Epoch 29/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0209 - accuracy: 0.9933\n",
      "Epoch 30/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.0218 - accuracy: 0.9967\n",
      "Epoch 31/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.0160 - accuracy: 0.9950\n",
      "Epoch 32/40\n",
      "600/600 [==============================] - 6s 10ms/step - loss: 0.0147 - accuracy: 0.9983\n",
      "Epoch 33/40\n",
      "600/600 [==============================] - 6s 10ms/step - loss: 0.0183 - accuracy: 0.9967\n",
      "Epoch 34/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.0279 - accuracy: 0.9917\n",
      "Epoch 35/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.0239 - accuracy: 0.9967\n",
      "Epoch 36/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0161 - accuracy: 0.9967\n",
      "Epoch 37/40\n",
      "600/600 [==============================] - 6s 11ms/step - loss: 0.0148 - accuracy: 0.9983\n",
      "Epoch 38/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0127 - accuracy: 0.9983\n",
      "Epoch 39/40\n",
      "600/600 [==============================] - 7s 11ms/step - loss: 0.0144 - accuracy: 0.9967\n",
      "Epoch 40/40\n",
      "600/600 [==============================] - 6s 10ms/step - loss: 0.0100 - accuracy: 0.9967\n",
      "150/150 [==============================] - 1s 4ms/step\n",
      "误差值 = 0.08072789231936137\n",
      "准确度 = 0.9599999785423279\n"
     ]
    }
   ],
   "source": [
    "#创建一个模型实体\n",
    "happy_model = HappyModel(X_train.shape[1:])\n",
    "#编译模型\n",
    "happy_model.compile(\"adam\",\"binary_crossentropy\",metrics=['accuracy'])\n",
    "#训练模型（此操作会花费几分钟）\n",
    "happy_model.fit(X_train,Y_train,epochs=40,batch_size=50)\n",
    "#评估模型\n",
    "preds = happy_model.evaluate(X_test,Y_test,batch_size=32,verbose=1,sample_weight=None)\n",
    "print(\"误差值 = \"+str(preds[0]))\n",
    "print(\"准确度 = \"+str(preds[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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