keras 自定义层中的持久变量 [英] Persistent Variable in keras Custom Layer
问题描述
我想写一个自定义层,我可以在运行之间在内存中保留一个变量.例如,
I want write a custom layer, where I can keep a variable in memory between runs. For example,
class MyLayer(Layer):
def __init__(self, out_dim = 51, **kwargs):
self.out_dim = out_dim
super(MyLayer, self).__init__(**kwargs)
def build(self, input_shape):
a = 0.0
self.persistent_variable = K.variable(a)
self.built = True
def get_output_shape_for(self, input_shape):
return (input_shape[0], 1)
def call(self, x, mask=None):
a = K.eval(self.persistent_variable) + 1
K.set_value(self.persistent_variable, a)
return self.persistent_variable
m = Sequential()
m.add(MyLayer(input_shape=(1,)))
当我运行 m.predict
时,我希望 persistent_variable
得到更新,并打印增加的值.但它看起来总是打印 0
When I run m.predict
, I expect the persistent_variable
to get updated, and print the incremented value.
But it looks like it always prints 0
# Dummy input
x = np.zeros(1)
m.predict(x, batch_size=1)
我的问题是,如何在每次运行 m.predict
My question is, how do I make the persistent_variable
increment and save after every run of m.predict
谢谢,内文
推荐答案
诀窍是你必须在你的调用函数中调用 self.add_update(...)
来注册一个函数每次评估模型时都会调用它(我通过深入研究有状态 rnns 的源代码发现了这一点).如果您执行 self.stateful = True
,它将为每个训练和预测调用调用您的自定义更新函数,否则它只会在训练期间调用它.例如:
The trick is that you have to call self.add_update(...)
in your call function to register a function that will be called every time your model is evaluated (I found this by digging into the source code of the stateful rnns). If you do self.stateful = True
it will call your custom update function for every training and prediction call, otherwise it will only call it during training. For example:
import keras.backend as K
import numpy as np
from keras.engine.topology import Layer
class CounterLayer(Layer):
def __init__(self, stateful=False,**kwargs):
self.stateful = stateful # True means it will increment counter on predict and train, false means it will only increment counter on train
super(CounterLayer, self).__init__(**kwargs)
def build(self, input_shape):
# Define variables in build
self.count = K.variable(0, name="count")
super(CounterLayer, self).build(input_shape)
def call(self, x, mask=None):
updates = []
# The format is (variable, value setting to)
# So this says
# self.pos = self.pos + 1
updates.append((self.count, self.count+1))
# You can append more updates to this list or call add_update more
# times if you want
# Add our custom update
# We stick x here so it calls our update function every time our layer
# is given a new x
self.add_update(updates, x)
# This will be an identity layer but keras gets mad for some reason
# if you just output x so we'll multiply it by 1 so it thinks it is a
# "new variable"
return self.count
# in newer keras versions you might need to name this compute_output_shape instead
def get_output_shape_for(self, input_shape):
# We will just return our count as an array ([[count]])
return (1,1)
def reset_states(self):
self.count.set_value(0)
示例用法:
from keras.layers import Input
from keras.models import Model
from keras.optimizers import RMSprop
inputLayer = Input(shape=(10,))
counter = CounterLayer() # Don't update on predict
# counter = CounterLayer(stateful=True) # This will update each time you call predict
counterLayer = counter(inputLayer)
model = Model(input=inputLayer, output=counterLayer)
optimizer = RMSprop(lr=0.001)
model.compile(loss="mse", optimizer=optimizer)
# See the value of our counter
print counter.count.get_value()
# This won't actually train anything but each epoch will update our counter
# Note that if you say have a batch size of 5, update will be called 5 times per epoch
model.fit(np.zeros([1, 10]), np.array([0]), batch_size=1, nb_epoch=5)
# The value of our counter has now changed
print counter.count.get_value()
model.predict(np.zeros([1, 10]))
# If we did stateful=False, this didn't change, otherwise it did
print counter.count.get_value()
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