自定義層函式需要繼承layers.layer，自定義網路需要繼承keras.model。
其內部需要定義兩個函式：
1、__init__初始化函式，內部需要定義構造形式；
2、call函式，內部需要定義計算形式及返回值。
#self def layer
class mydense(layers.layer):#inherit layers.layer
def __init__(self,input_dim,output_dim):#init
super(mydense,self).__init__()
self.kernal = self.add_variable('w',[input_dim,output_dim])
self.bias = self.add_variable('b',[output_dim])
def call(self,inputs,training=none):#compute
out = inputs @ self.kernal + self.bias
return out
#self def network
class mymodel(keras.model):#inherit keras.model
def __init__(self):#init
super(mymodel,self).__init__()
self.fc1 = mydense(input_dim=28*28,output_dim=512)
self.fc2 = mydense(input_dim=512, output_dim=256)
self.fc3 = mydense(input_dim=256, output_dim=128)
self.fc4 = mydense(input_dim=128, output_dim=64)
self.fc5 = mydense(input_dim=64, output_dim=32)
self.fc6 = mydense(input_dim=32, output_dim=10)
def call(self,inputs,training=none):#compute inputs.shape = [b,28*28]
x = self.fc1(inputs)
x = tf.nn.relu(x)
x = self.fc2(x)
x = tf.nn.relu(x)
x = self.fc3(x)
x = tf.nn.relu(x)
x = self.fc4(x)
x = tf.nn.relu(x)
x = self.fc5(x)
x = tf.nn.relu(x)
x = self.fc6(x)
return x
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers,sequential,optimizers,datasets,metrics
def preprocess(x,y):
x = tf.cast(tf.reshape(x,[-1]),dtype=tf.float32)/255.
y = tf.cast(tf.one_hot(y,depth=10),dtype=tf.int32)
return x,y
#load_data
(x_train,y_train),(x_val,y_val) = datasets.mnist.load_data()
print('data: ',x_train.shape,y_train.shape,x_val.shape,y_val.shape)
db = tf.data.dataset.from_tensor_slices((x_train,y_train))
db = db.map(preprocess).shuffle(60000).batch(128)
db_val = tf.data.dataset.from_tensor_slices((x_val,y_val))
db_val = db_val.map(preprocess).batch(128)
#self def layer
class mydense(layers.layer):#inherit layers.layer
def __init__(self,input_dim,output_dim):#init
super(mydense,self).__init__()
self.kernal = self.add_variable('w',[input_dim,output_dim])
self.bias = self.add_variable('b',[output_dim])
def call(self,inputs,training=none):#compute
out = inputs @ self.kernal + self.bias
return out
#self def network
class mymodel(keras.model):#inherit keras.model
def __init__(self):#init
super(mymodel,self).__init__()
self.fc1 = mydense(input_dim=28*28,output_dim=512)
self.fc2 = mydense(input_dim=512, output_dim=256)
self.fc3 = mydense(input_dim=256, output_dim=128)
self.fc4 = mydense(input_dim=128, output_dim=64)
self.fc5 = mydense(input_dim=64, output_dim=32)
self.fc6 = mydense(input_dim=32, output_dim=10)
def call(self,inputs,training=none):#compute inputs.shape = [b,28*28]
x = self.fc1(inputs)
x = tf.nn.relu(x)
x = self.fc2(x)
x = tf.nn.relu(x)
x = self.fc3(x)
x = tf.nn.relu(x)
x = self.fc4(x)
x = tf.nn.relu(x)
x = self.fc5(x)
x = tf.nn.relu(x)
x = self.fc6(x)
return x
network = mymodel()
network.build(input_shape=[none,28*28])
network.summary()
#build network
network = sequential([
layers.dense(512,activation=tf.nn.relu),
layers.dense(256,activation=tf.nn.relu),
layers.dense(128,activation=tf.nn.relu),
layers.dense(64,activation=tf.nn.relu),
layers.dense(32,activation=tf.nn.relu),
layers.dense(10)
])network.build(input_shape=[none,28*28])
network.summary()
#input para
network.compile(optimizer=optimizers.adam(lr=1e-2),
loss = tf.losses.categoricalcrossentropy(from_logits=true),
metrics = ['accuracy'])
#run network
network.fit(db,epochs=20,validation_data=db_val,validation_freq=1)
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