在TensorFlow中更新权重的子集 [英] Update a subset of weights in TensorFlow

问题描述

有人知道如何更新前向传播中使用的权重的子集(即仅某些索引)吗?

Does anyone know how to update a subset (i.e. only some indices) of the weights that are used in the forward propagation?

我的猜测是，在按如下所示应用compute_gradients之后，我可能能够做到这一点:

My guess is that I might be able to do that after applying compute_gradients as follows:

optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
grads_vars = optimizer.compute_gradients(loss, var_list=[weights, bias_h, bias_v])

...然后对grads_vars中的元组列表进行处理.

...and then do something with the list of tuples in grads_vars.

推荐答案

您可以使用gather和scatter_update的组合.这是一个示例，该示例将位置0和2

You could use a combination of gather and scatter_update. Here's an example that doubles the values at position 0 and 2

indices = tf.constant([0,2])
data = tf.Variable([1,2,3])
data_subset = tf.gather(data, indices)
updated_data_subset = 2*data_subset
sparse_update = tf.scatter_update(data, indices, updated_data_subset)
init_op = tf.initialize_all_variables()

sess = tf.Session()
sess.run([init_op])
print "Values before:", sess.run([data])
sess.run([sparse_update])
print "Values after:", sess.run([data])

您应该看到

Values before: [array([1, 2, 3], dtype=int32)]
Values after: [array([2, 2, 6], dtype=int32)]

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