43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import tensorflow as tf
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def identify_dependent_variables(tensor, candidate_variables):
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"""
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identify the variables that `tensor` depends on
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:param tensor: A Tensor.
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:param candidate_variables: A list of Variables.
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:return: A list of variables in `candidate variables` that has effect on `tensor`
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"""
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grads = tf.gradients(tensor, candidate_variables)
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return [var for var, grad in zip(candidate_variables, grads) if grad is not None]
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def get_soft_update_op(update_fraction, including_nets, excluding_nets=None):
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"""
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:param including_nets:
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:param excluding_nets:
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:return:
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"""
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assert 0 < update_fraction < 1, 'Unrecommended update_fraction <=0 or >=1!'
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variables = []
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variables_old = []
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for net in including_nets:
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for var, var_old in zip(net.network_weights, net.network_old_weights):
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if var not in variables:
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variables.append(var)
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variables_old.append(var_old)
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if excluding_nets:
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excluding_variables = []
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for net in excluding_nets:
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excluding_variables += net.network_weights
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for var, var_old in zip(variables, variables_old):
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if var in excluding_variables:
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variables.remove(var)
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variables_old.remove(var_old)
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return [tf.assign(var_old, update_fraction * var + (1 - update_fraction) * var_old)
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for var_old, var in zip(variables_old, variables)]
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