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()
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