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()
本文链接：http://task.lmcjl.com/news/5949.html