Keras是强大、易用的深度学习库，基于Theano和TensorFlow提供了高阶神经网络API，用于开发和评估深度学习模型。
示例
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
data = np.random.random((1000,100))
labels = np.random.randint(2,size=(1000,1))
model = Sequential()
model.add(Dense(32,activation='relu',
input_dim=100))
model.add(Dense(1,activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(data,labels,epochs=10,batch_size=32)
predictions = model.predict(data)
数据
数据要存为 NumPy 数组或数组列表，使 sklearn.cross_validation的 train_test_split 模块进行分割将数据分割为训练集与测试集。
Keras数据集
from keras.datasets import boston_housing,mnist,
cifar10,imdb
(x_train,y_train),(x_test,y_test) = mnist.load_data()
(x_train2,y_train2),(x_test2,y_test2) = boston_housing.load_data()
(x_train3,y_train3),(x_test3,y_test3) = cifar10.load_data()
(x_train4,y_train4),(x_test4,y_test4) = imdb.load_data(num_words=20000)
num_classes = 10
其它
from urllib.request import urlopen
data = np.loadtxt(urlopen("http://archive.ics.uci.edu/
ml/machine-learning-databases/pima-indians-diabetes/
pima-indians-diabetes.data"),delimiter=",")
X = data[:,0:8]
y = data [:,8]
预处理
序列填充
from keras.preprocessing import sequence
x_train4 = sequence.pad_sequences(x_train4,maxlen=80)
x_test4 = sequence.pad_sequences(x_test4,maxlen=80)
独热编码
from keras.utils import to_categorical
Y_train = to_categorical(y_train, num_classes)
Y_test = to_categorical(y_test, num_classes)
Y_train3 = to_categorical(y_train3, num_classes)
Y_test3 = to_categorical(y_test3, num_classes)
训练集与测试集
from sklearn.model_selection import train_test_split
X_train5,X_test5,y_train5,y_test5 = train_test_split(X, y,test_size=0.33,
random_state=42)
标准化/归一化
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(x_train2)
standardized_X = scaler.transform(x_train2)
standardized_X_test = scaler.transform(x_test2)
模型架构
序贯模型
from keras.models import Sequential
model = Sequential()
model2 = Sequential()
model3 = Sequential()
多层感知机(MLP)
#二进制分类
from keras.layers import Dense
model.add(Dense(12,input_dim=8,kernel_initializer='uniform',
activation='relu'))
model.add(Dense(8,kernel_initializer='uniform',activation='relu'))
model.add(Dense(1,kernel_initializer='uniform',activation='sigmoid'))
#多级分类
from keras.layers import Dropout
model.add(Dense(512,activation='relu',input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10,activation='softmax'))
#回归
model.add(Dense(64,activation='relu',input_dim=train_data.shape[1]))
model.add(Dense(1))
卷积神经网络（CNN）
from keras.layers import Activation,Conv2D,MaxPooling2D,Flatten
model2.add(Conv2D(32,(3,3),padding='same',input_shape=x_train.shape[1:]))
model2.add(Activation('relu'))
model2.add(Conv2D(32,(3,3)))
model2.add(Activation('relu'))
model2.add(MaxPooling2D(pool_size=(2,2)))
model2.add(Dropout(0.25))
model2.add(Conv2D(64,(3,3), padding='same'))
model2.add(Activation('relu'))
model2.add(Conv2D(64,(3,3)))
model2.add(Activation('relu'))
model2.add(MaxPooling2D(pool_size=(2,2)))
model2.add(Dropout(0.25))
model2.add(Flatten())
model2.add(Dense(512))
model2.add(Activation('relu'))
model2.add(Dropout(0.5))
model2.add(Dense(num_classes))
model2.add(Activation('softmax'))
递归神经网络(RNN)
from keras.klayers import Embedding,LSTM
model3.add(Embedding(20000,128))
model3.add(LSTM(128,dropout=0.2,recurrent_dropout=0.2))
model3.add(Dense(1,activation='sigmoid'))
审视模型
model.output_shape#模型输出形状
model.summary()#模型摘要展示
model.get_config()#模型配置
model.get_weights()#列出模型的所有权重张量
编译模型
多层感知机：二进制分类
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
多层感知机：多级分类
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
多层感知机：回归
model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
模型训练
model3.fit(x_train4,
y_train4,
batch_size=32,
epochs=15,
verbose=1,
validation_data=(x_test4,y_test4))
评估模型性能
score = model3.evaluate(x_test, y_test, batch_size=32)
预测
model3.predict(x_test4, batch_size=32)
model3.predict_classes(x_test4,batch_size=32)
保存/加载模型
from keras.models import load_model
model3.save('model_file.h5')
my_model = load_model('my_model.h5')
模型微调
参数优化
from keras.optimizers import RMSprop
opt = RMSprop(lr=0.0001, decay=1e-6)
model2.compile(loss='categorical_crossentropy', optimizer=opt,
metrics=['accuracy'])
早停法
from keras.callbacks import EarlyStopping
early_stopping_monitor = EarlyStopping(patience=2)
model3.fit(x_train4,y_train4,batch_size=32,epochs=15,validation_data=(x_test4,y_test4),callbacks=[early_stopping_monitor])
