import numpy as np
#import tensorflow as tf
from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
import pydot
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from kt_utils import *import keras.backend as K
K.set_image_data_format('channels_last')
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow%matplotlib inline

def mean_pred(y_true, y_pred):return K.mean(y_pred)def load_dataset():train_dataset = h5py.File('datasets/train_happy.h5', "r")  # h5py 是一种数据存储格式train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set featurestrain_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
test_dataset = h5py.File('datasets/test_happy.h5', "r")test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set featurestest_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes

X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()# Normalize image vectors
X_train = X_train_orig/255.
X_test = X_test_orig/255.# Reshape
Y_train = Y_train_orig.T
Y_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))

根据keras的快速原型：这些方法都是在导入keras时候导入的
def model(input_shape):X_input = Input(input_shape) # 底层是tensorflow ，其实是创建placeholder存放变量X=ZeroPadding2D((3,3))(X_input) # 建立padding画布，主要适配不同图片尺寸或者避免边缘特征磨损的情况X=Conv2D(32，(7,7)),strides=(1,1),name=’conv0’)(x)  ##32 是过滤核的数量；（7,7）是过滤核的宽度和高度，strides是卷积的部长，name是对其进行命名X = BatchNormalization(axis = 3, name = 'bn0')(X) #规范化，控制过拟合X=Activation(‘relu’)(x) #激活函数X=MaxPooling2D((2,2),name=’max_pool’)(x) #池化
X = Flatten()(X) #全连接，多维转为一维 X=Dense(1,activation=’sigmoid’,name=’fc’)(x) #激活函数，和全连接model=Model(input=X_input,outputs=X,name=’HappyModel’)   return model

def HappyModel(input_shape):X_input = Input(shape=input_shape)X = ZeroPadding2D(padding=(1,1))(X_input)X=Conv2D(8,kernel_size=(3,3),strides=(1,1))(x)X=BatchNormalization(axis=3)(x)X=Activation(‘relu’)(X)X=MaxPooling2D(pool_size=(2,2),strides=(2,2),padding=’valid’)(x) #FCX=Flatten()(X)Y=Dense(1,activation=’sigmoid’)(x)Model = Model(inputs=X_input,outputs=Y,name=’HappyModel’)Return model

happyModel = HappyModel((64, 64, 3))

import kerashappyModel.compile(optimizer=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0), loss='binary_crossentropy', metrics=['accuracy'])

happyModel.fit(x=X_train, y=Y_train, batch_size=16, epochs=20)

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

mg_path = 'images/my_image.jpg'
### END CODE HERE ###
img = image.load_img(img_path, target_size=(64, 64))
imshow(img)x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
result = happyModel.predict(x) ###预测结果
print(result)