自定义层or网络
Outline
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keras.Sequential
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keras.layers.Layer
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keras.Model
keras.Sequential
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model.trainable_variables # 管理参数
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model.call()
network = Sequential([
layers.Dense(256, acitvaiton='relu'),
layers.Dense(128, acitvaiton='relu'),
layers.Dense(64, acitvaiton='relu'),
layers.Dense(32, acitvaiton='relu'),
layers.Dense(10)
])
network.build(input_shape=(None, 28 * 28))
network.summary()
Layer/Model
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Inherit from keras.layers.Layer/keras.Model
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__init__
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call
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Model:compile/fit/evaluate
MyDense
class MyDense(layers.Layer):
def __init__(self, inp_dim, outp_dim):
super(MyDense, self).__init__()
self.kernel = self.add_variable('w', [imp_dim, outp_dim])
self.bias = self.add_variable('b', [outp_dim])
def call(self, inputs, training=None):
out = input @ self.kernel + self.bias
return out
MyModel
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, iputs, 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
