keras API的使用，神经网络层，优化器，损失函数，查看模型层数，compile和fit训练

layers介绍

Flatten和Dense介绍

 

优化器

 

损失函数

 

compile用法

 

第二个是onehot编码

 

模型训练 model.fit

 两种创建模型的方法

from tensorflow.python.keras.preprocessing.image import load_img,img_to_array
from tensorflow.python.keras.models import Sequential,Model
from tensorflow.python.keras.layers import Dense,Flatten,Input
import tensorflow as tf
from tensorflow.python.keras.losses import sparse_categorical_crossentropy


def main():

    #通过Sequential创建网络
    model = Sequential(
        [
            Flatten(input_shape=(28,28)),
            Dense(64,activation=tf.nn.relu),
            Dense(128,activation=tf.nn.relu),
            Dense(10,activation=tf.nn.softmax)
        ]
    )
    print(model)

   #通过Model创建模型
    data = Input(shape=(784,))
    out = Dense(64)(data)
    model_sec = Model(inputs=data,outputs=out)
    print(model_sec)
    print(model.layers,model_sec.layers)
    print(model.input,model.output)
    print(model.summary())
    print(model_sec.summary())

if __name__ == '__main__':
    main()