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()