模型集成共享图层

Question

在keras中,我想训练一组共享某些图层的模型.它们具有以下形式:

x ---> f(x)---> g_1(f(x))

x ---> f(x)---> g_2(f(x))

...

x ---> f(x)---> g_n(f(x))

这里f(x)是一些非平凡的共享层.g_1到g_n有其特定参数.

在每个训练阶段,数据x被馈送到n个网络中的一个,例如,第i个.然后通过基于梯度的优化器最小化/减小g_i(f(x))上的损失.我怎样才能定义和训练这样的模型？

提前致谢!

Answer 1

您可以使用功能模型轻松完成此操作.

一个小例子..你可以建立它:

import numpy as np
from keras.models import Model
from keras.layers import Dense, Input

X = np.empty(shape=(1000,100))
Y1 = np.empty(shape=(1000))
Y2 = np.empty(shape=(1000,2))
Y3 = np.empty(shape=(1000,3))

inp = Input(shape=(100,))
dense_f1 = Dense(50)
dense_f2 = Dense(20)

f = dense_f2(dense_f1(inp))

dense_g1 = Dense(1)
g1 = dense_g1(f)

dense_g2 = Dense(2)
g2 = dense_g2(f)

dense_g3 = Dense(3)
g3 = dense_g3(f)


model = Model([inp], [g1, g2, g3])
model.compile(loss=['mse', 'binary_crossentropy', 'categorical_crossentropy'], optimizer='rmsprop')

model.summary()

model.fit([X], [Y1, Y2, Y3], nb_epoch=10)

编辑:

根据您的意见,您可以随时根据您的培训需求制作不同的模型并自行编写培训循环.您可以在model.summary()所有模型中看到共享初始图层.这是示例的扩展

model1 = Model(inp, g1)
model1.compile(loss=['mse'], optimizer='rmsprop')
model2 = Model(inp, g2)
model2.compile(loss=['binary_crossentropy'], optimizer='rmsprop')
model3 = Model(inp, g3)
model3.compile(loss=['categorical_crossentropy'], optimizer='rmsprop')
model1.summary()
model2.summary()
model3.summary()

batch_size = 10
nb_epoch=10
n_batches = X.shape[0]/batch_size


for iepoch in range(nb_epoch):
    for ibatch in range(n_batches):
        x_batch = X[ibatch*batch_size:(ibatch+1)*batch_size]
        if ibatch%3==0:
            y_batch = Y1[ibatch*batch_size:(ibatch+1)*batch_size]
            model1.train_on_batch(x_batch, y_batch)      
        elif ibatch%3==1:
            y_batch = Y2[ibatch*batch_size:(ibatch+1)*batch_size]
            model2.train_on_batch(x_batch, y_batch)      
        else:
            y_batch = Y3[ibatch*batch_size:(ibatch+1)*batch_size]
            model3.train_on_batch(x_batch, y_batch)