搭建一个简易网络模型：

import tensorflow as tf
model=tf.keras.Sequential()
model.add(tf.keras.layers.Dense(64,input_shape=(20,),activation='relu'))
model.add(tf.keras.layers.Dense(32,activation='relu'))
model.add(tf.keras.layers.Dense(5,activation='softmax'))
model.summary()

 

 

#方法二

import tensorflow as tf
model=tf.keras.Sequential([
    tf.keras.layers.Dense(64,input_shape=(20,),activation='relu'),
    tf.keras.layers.Dense(32,activation='relu'),
    tf.keras.layers.Dense(5,activation='softmax')
])
model.summary()

---

简单的神经网络实例

import tensorflow as tf
import numpy as np

x_train=np.random.random((10000,15))
y_train=tf.keras.utils.to_categorical(np.random.randint(10,size=(10000,1)),num_classes=10)
x_test=np.random.random((10000,15))
y_test=tf.keras.utils.to_categorical(np.random.randint(10,size=(10000,1)),num_classes=10)

model=tf.keras.Sequential([
    tf.keras.layers.Dense(512,activation=tf.nn.relu),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(128,activation=tf.nn.relu),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(10,activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy']
)
model.fit(x_train,y_train,epochs=5,batch_size=128)

——2019.11.12