deeplearning.ai 第四课第二周，keras导航

1、函数库导入：（案例是一个happyhouse案例）

import numpy as npfrom keras import layersfrom keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2Dfrom keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2Dfrom keras.models import Modelfrom keras.preprocessing import imagefrom keras.utils import layer_utilsfrom keras.utils.data_utils import get_filefrom keras.applications.imagenet_utils import preprocess_inputimport pydotfrom IPython.display import SVGfrom keras.utils.vis_utils import model_to_dotfrom keras.utils import plot_modelfrom keras import lossesfrom kt_utils import *import keras.backend as KK.set_image_data_format('channels_last')import matplotlib.pyplot as pltfrom matplotlib.pyplot import imshow%matplotlib inline#help(Model.compile)

2、训练数据导入：

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()# Normalize image vectorsX_train = X_train_orig/255.X_test = X_test_orig/255.# ReshapeY_train = Y_train_orig.TY_test = Y_test_orig.Tprint ("number of training examples = " + str(X_train.shape[0]))print ("number of test examples = " + str(X_test.shape[0]))print ("X_train shape: " + str(X_train.shape))print ("Y_train shape: " + str(Y_train.shape))print ("X_test shape: " + str(X_test.shape))print ("Y_test shape: " + str(Y_test.shape))

3、happymodel定义（本模型采用类似letnet-5卷积网络的设置，前面卷积和池化层和其相同，后面的全连接层采用神经元数为 100,20，及最后一层用的是logistic_regression的二分层）

# GRADED FUNCTION: HappyModeldef HappyModel(input_shape):    """    Implementation of the HappyModel.    Arguments:    input_shape -- shape of the images of the dataset    Returns:    model -- a Model() instance in Keras    """    ### START CODE HERE ###    # Feel free to use the suggested outline in the text above to get started, and run through the whole    # exercise (including the later portions of this notebook) once. The come back also try out other    # network architectures as well.     X_input = Input(input_shape)    # shape(None,68,68,3)    X = ZeroPadding2D((2,2))(X_input)    #first layer of model    # shape (None,64,64,6)    X = Conv2D(6,(5,5),strides=(1,1),name='conv1')(X)    X = BatchNormalization(axis=3,name='bn1')(X)    X = Activation('relu')(X)    # first layer pooling    # shape = (None,32,32,6)    X = MaxPooling2D((2,2),strides=(2,2),name='max_pool_1')(X)    # second layers of models    # shape = (None,28,28,16)    X = Conv2D(16,(5,5),strides=(1,1),name='conv2')(X)    X = BatchNormalization(axis=3,name='nb2')(X)    X = Activation('relu')(X)    # second pooling    # shape = (None,14,14,16)    X = MaxPooling2D((2,2),strides=(2,2),name='max_pool_2')(X)    # flatten the X values    # shape = (None, 14*14*16)    X = Flatten()(X)    # third layers models    # shape =  (None,100)    X = Dense(100,activation='relu',name='fc1')(X)    # fourth layer models    # shape = (None,20)    X = Dense(20,activation='relu',name='fc2')(X)    # fifth layer models    # shape = (None,1)    X = Dense(1,activation='sigmoid',name='fc3')(X)    #create this your keras model     model = Model(inputs = X_input, outputs = X, name='HappyModel')    ### END CODE HERE ###    return model

4、模型计算
4.1 实体模型定义：

### START CODE HERE ### (1 line)happyModel = HappyModel((64,64,3))### END CODE HERE ###

4.2 模型 compile 定一其学习过程及相应超参

### START CODE HERE ### (1 line)happyModel.compile(optimizer='Adam',loss='binary_crossentropy',metrics=["accuracy"])### END CODE HERE ###

4.3 模型训练过程：

### START CODE HERE ### (1 line)happyModel.fit(x = X_train,y=Y_train,epochs = 10,batch_size=32)### END CODE HERE ###

4.4 模型评估：

### START CODE HERE ### (1 line)preds = happyModel.evaluate(x=X_test,y=Y_test)### END CODE HERE ###print()print ("Loss = " + str(preds[0]))print ("Test Accuracy = " + str(preds[1]))