笔记|李宏毅老师机器学习课程，视频15Keras Demo

import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers import Conv2D, MaxPooling2D, Flatten
from keras.optimizers import SGD, Adam
from keras.utils import np_utils
from keras.datasets import mnist


def load_data():  # categorical_crossentropy
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    number = 10000
    x_train = x_train[0:number]
    y_train = y_train[0:number]
    x_train = x_train.reshape(number, 28 * 28)
    x_test = x_test.reshape(x_test.shape[0], 28 * 28)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    # convert class vectors to binary class matrices
    y_train = np_utils.to_categorical(y_train, 10)
    y_test = np_utils.to_categorical(y_test, 10)
    x_train = x_train
    x_test = x_test
    x_test = np.random.normal(x_test)  # 加噪声
    x_train = x_train / 255
    x_test = x_test / 255

    return (x_train, y_train), (x_test, y_test)


if __name__ == '__main__':
    '''
    注意事项如下：
    1、batch_size=100,epochs=20为宜，batch_size过大会导致loss下降曲线过于平滑而卡在local minima、saddle point或plateau处，batch_size过小会导致update次数过多，运算量太大，速度缓慢，但可以带来一定程度的准确率提高
    2、hidden layer数量不要太多，不然可能会发生vanishing gradient(梯度消失)，一般两到三层为宜
    3、如果layer数量太多，则千万不要使用sigmoid等缩减input影响的激活函数，应当选择ReLU、Maxout等近似线性的activation function(layer数量不多也应该选这两个)
    4、每一个hidden layer所包含的neuron数量，五六百为宜
    5、对于分类问题，loss function一定要使用cross entropy(categorical_crossentropy)，而不是mean square error(mse)
    6、优化器optimizer一般选择adam，它综合了RMSProp和Momentum，同时考虑了过去的gradient、现在的gradient，以及上一次的惯性
    7、如果testing data上准确率很低，training data上准确率比较高，可以考虑使用dropout，Keras的使用方式是在每一层hidden layer的后面加上一句model.add(Dropout(0.5))，其中0.5这个参数你自己定；注意，加了dropout之后在training set上的准确率会降低，但是在testing set上的准确率会提高，这是正常的
    8、如果input是图片的pixel，注意对灰度值进行归一化，即除以255，使之处于0～1之间
    9、最后的output最好同时输出在training set和testing set上的准确率，以便于对症下药
    '''
    # load training data and testing data
    (x_train, y_train), (x_test, y_test) = load_data()

    # define network structure
    model = Sequential()

    model.add(Dense(input_dim=28 * 28, units=500, activation='relu'))
    # model.add(Dropout(0.5))
    model.add(Dense(units=500, activation='relu'))
    # model.add(Dropout(0.5))
    model.add(Dense(units=10, activation='softmax'))

    # set configurations
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam', metrics=['accuracy'])

    # train model
    model.fit(x_train, y_train, batch_size=100, epochs=20)

    # evaluate the model and output the accuracy
    result_train = model.evaluate(x_train, y_train)
    result_test = model.evaluate(x_test, y_test)
    print('Train Acc:', result_train[1])
    print('Test Acc:', result_test[1])