python - 将张量流梯度应用于特定输入
问题描述
我正在尝试针对 keras 模型中的特定输入特征为某些输出变量创建雅可比矩阵。例如,如果我有一个具有 100 个输入特征和 10 个输出变量的模型,并且我想针对输出 50-70 创建输出 2、3 和 4 的雅可比,我可以像这样创建雅可比:
from keras.models import Model
from keras.layers import Dense, Input
import tensorflow as tf
import keras.backend as K
import numpy as np
input_ = Input(shape=(100,))
output_ = Dense(10)(input_)
model = Model(input_,output_)
x_indices = np.arange(50,70)
y_indices = [2,3,4]
y_list = tf.unstack(model.output[0])
x = np.random.random((1,100))
jacobian_matrix = []
for i in y_indices:
J = tf.gradients(y_list[i], model.input)
jacobian_func = K.function([model.input, K.learning_phase()], J)
jac = jacobian_func([x, False])[0][0,x_indices]
jacobian_matrix.append(jac)
jacobian_matrix = np.array(jacobian_matrix)
但是对于更复杂的模型,这非常慢。我只想针对感兴趣的输入创建上面的雅可比函数。我试过这样的事情:
from keras.models import Model
from keras.layers import Dense, Input
import tensorflow as tf
import keras.backend as K
import numpy as np
input_ = Input(shape=(100,))
output_ = Dense(10)(input_)
model = Model(input_,output_)
x_indices = np.arange(50,60)
y_indices = [2,3,4]
y_list = tf.unstack(model.output[0])
x_list = tf.unstack(model.input[0])
x = np.random.random((1,100))
jacobian_matrix = []
for i in y_indices:
jacobian_row = []
for j in x_indices:
J = tf.gradients(y_list[i], x_list[j])
jacobian_func = K.function([model.input, K.learning_phase()], J)
jac = jacobian_func([x, False])[0][0,:]
jacobian_row.append(jac)
jacobian_matrix.append(jacobian_row)
jacobian_matrix = np.array(jacobian_matrix)
并得到错误:
TypeErrorTraceback (most recent call last)
<ipython-input-33-d0d524ad0e40> in <module>()
23 for j in x_indices:
24 J = tf.gradients(y_list[i], x_list[j])
---> 25 jacobian_func = K.function([model.input, K.learning_phase()], J)
26 jac = jacobian_func([x, False])[0][0,:]
27 jacobian_row.append(jac)
/opt/conda/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in function(inputs, outputs, updates, **kwargs)
2500 msg = 'Invalid argument "%s" passed to K.function with TensorFlow backend' % key
2501 raise ValueError(msg)
-> 2502 return Function(inputs, outputs, updates=updates, **kwargs)
2503
2504
/opt/conda/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in __init__(self, inputs, outputs, updates, name, **session_kwargs)
2443 self.inputs = list(inputs)
2444 self.outputs = list(outputs)
-> 2445 with tf.control_dependencies(self.outputs):
2446 updates_ops = []
2447 for update in updates:
/opt/conda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in control_dependencies(control_inputs)
4302 """
4303 if context.in_graph_mode():
-> 4304 return get_default_graph().control_dependencies(control_inputs)
4305 else:
4306 return _NullContextmanager()
/opt/conda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in control_dependencies(self, control_inputs)
4015 if isinstance(c, IndexedSlices):
4016 c = c.op
-> 4017 c = self.as_graph_element(c)
4018 if isinstance(c, Tensor):
4019 c = c.op
/opt/conda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in as_graph_element(self, obj, allow_tensor, allow_operation)
3033
3034 with self._lock:
-> 3035 return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
3036
3037 def _as_graph_element_locked(self, obj, allow_tensor, allow_operation):
/opt/conda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in _as_graph_element_locked(self, obj, allow_tensor, allow_operation)
3122 # We give up!
3123 raise TypeError("Can not convert a %s into a %s." % (type(obj).__name__,
-> 3124 types_str))
3125
3126 def get_operations(self):
TypeError: Can not convert a NoneType into a Tensor or Operation.
有任何想法吗?谢谢。
解决方案
问题在于线路J = tf.gradients(y_list[i], x_list[j])
。x_list[j]
派生自model.input[0]
,但没有从x_list[j]
到的定向路径model.output[0]
。您需要取消堆叠模型输入,重新堆叠然后运行模型,或者创建关于整个输入的导数,然后j
从那里选择第 th 行。
第一种方式:
inputs = tf.keras.Inputs((100,))
uninteresting, interesting, more_uninteresting = tf.split(inputs, [50, 10, 40], axis=1)
inputs = tf.concat([uninteresting, interesting, more_uninteresting], axis=1)
model = Model(inputs)
...
J, = tf.gradients(y_list[i], interesting)
第二种方式:
J, = tf.gradients(y_list[i], model.input[0])
J = J[:, 50:60]
话虽如此,对于大量y
索引来说,这仍然会很慢,所以我强烈建议你绝对确定你需要雅可比本身(而不是,例如,雅可比向量积的结果) .
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