keras_API汇总积累（熟读手册）一，快速开始

一，快速开始

建立(sequential和add两种方式)

1.1sequential

from keras.models import Sequential

from keras.layers  import Dense,Activation,Dropout,Flatten,Conv2D,MaxPooling2D,Embedding,LSTM,Conv1D,GlobalAveragePooling1D,MaxPooling1D

from keras.optimizers import SGD

model=Sequential([Dense(32,input_shape=(784,)),Activation('relu'),Dsense(10),Activation('softmax')])#在一个sequential模型中添加了两个全连接层和两个激活函数

1.2add

model.Sequential()

model.add(Dense(32,input_dim=784))#3D时，还需要input_length

model.add(Activation('relu'))

1.3other

model.add(Embedding(max_features,output_dim=256))

model.add(LSTM(128,return_sequences=True, stateful=True))

model.add(Conv2D(32,(3,3),activation='relu',input_shape=(100,100,3)))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())

model.add(Dropout(0.5))

model.add(Conv1D(64,3,activation='relu',input_shape=(64,100)))

model.add(MaxPooling1D(3)

model.add(GlobalAveragePooling1D())

编译（三个参数：optimizer,loss,metrics(可以自定义评估函数eg:mean_pred)）

自己设置优化器：sgd=SGD(lr=0.01,decay=1e-6,momentum=0.9,nesrerov=True)

model.compile(loss='categorical_crossentropy',optimizer='rmsprop',metrics=['accuracy'，mean_pred])

 compile(optimizer, loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None)

训练

model.fit(x,y,batch_size=32,epochs=10)

fit(x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None)

train_on_batch(x, y, sample_weight=None, class_weight=None)

test_on_batch(x, y, sample_weight=None)

评估

score=model.evaluate(x_test,y_test,batch_size=32)

evaluate(x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None)

预测

predict(x, batch_size=None, verbose=0, steps=None)

a.打印出网络每一层输入输出详情的png图片：先装graphviz，链接：https://pan.baidu.com/s/1u64HriYy4KQ_BhJE8MMTkA ，提取码：zqxa》》》》安装到电脑后，打开Graphviz2.38in，复制目录添加到系统环境变量的path中》》》》cmd：pip install pydot=1.2.3》》》》cmd: dot -version查看安装成功》》》》keras.utils.plot_model(model, 'model.png', show_shapes=True) 》》》就会把你的model保存到文件夹下，名字model.png。

b.标签转换为one-hot：one_hot_labels=keras.utils.to_categorical(y,num_classes=10)