标签：获取 super code class layer put 管理 color com
使用到的API有：keras.Sequential、Layers/Model
1.keras.Sequential
以前的代码已经很多次用到了这个接口，这里直接给出代码：
model = Sequential([ layers.Dense(256,activation=tf.nn.relu), # [b,784] ==>[b,256] layers.Dense(128,activation=tf.nn.relu), layers.Dense(64,activation=tf.nn.relu), layers.Dense(32,activation=tf.nn.relu), layers.Dense(10) ]) model.build(input_shape=[None,28*28]) model.summary()
Sequential还可以通过一些API去管理参数，如：model.trainable_variables、model.call()，前者是用来获取网络中所有的可训练参数，后者则是相当于逐层调model方法
2.Layer/Model
Layer的全路径为keras.layers.Layer，Model的全路径为keras.Model（包含compile,fit,evaluate功能）
class MyDense(keras.layers.Layer): def __init__(self,inp_dim,outp_dim): super(MyDense, self).__init__() self.kernel = self.add_variable(‘w‘,[inp_dim,outp_dim]) self.bias = self.add_variable(‘b‘,[outp_dim]) def call(self,inputs,training=None): out = inputs @ self.kernel + self.bias return out class MyModel(keras.Model): def __init__(self): super(MyModel, self).__init__() self.fc1 = MyDense(28*28,256) self.fc2 = MyDense(256, 128) self.fc3 = MyDense(128, 64) self.fc4 = MyDense(64, 32) self.fc5 = MyDense(32, 10) def call(self,inputs,training=None): 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) return x
标签：获取 super code class layer put 管理 color com
原文地址：https://www.cnblogs.com/zdm-code/p/12245906.html
