我试图用keras（作为诊断工具）计算相对于神经网络权重的梯度范数。最后，我想为此创建一个回调，但在那里，我一直在努力创建一个可以计算渐变的函数，并以numpy数组/标量值的形式返回实际值（而不仅仅是张量流张量）。代码如下：
import numpy as np
import keras.backend as K
from keras.layers import Dense
from keras.models import Sequential
def get_gradient_norm_func(model):
grads = K.gradients(model.total_loss, model.trainable_weights)
summed_squares = [K.sum(K.square(g)) for g in grads]
norm = K.sqrt(sum(summed_squares))
func = K.function([model.input], [norm])
return func
def main():
x = np.random.random((128,)).reshape((-1, 1))
y = 2 * x
model = Sequential(layers=[Dense(2, input_shape=(1,)),
Dense(1)])
model.compile(loss='mse', optimizer='RMSprop')
get_gradient = get_gradient_norm_func(model)
history = model.fit(x, y, epochs=1)
print(get_gradient([x]))
if __name__ == '__main__':
main()
代码在调用get_gradient()时失败。追溯是漫长的，涉及很多形状，但很少有关于什么是正确形状的信息。我怎么能纠正这个？
理想情况下，我想要一个与后端无关的解决方案，但基于张量流的解决方案也是一种选择。
2017-08-15 15:39:14.914388: W tensorflow/core/framework/op_kernel.cc:1148] Invalid argument: Shape [-1,-1] has negative dimensions
2017-08-15 15:39:14.914414: E tensorflow/core/common_runtime/executor.cc:644] Executor failed to create kernel. Invalid argument: Shape [-1,-1] has negative dimensions
[[Node: dense_2_target = Placeholder[dtype=DT_FLOAT, shape=[?,?], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
2017-08-15 15:39:14.915026: W tensorflow/core/framework/op_kernel.cc:1148] Invalid argument: Shape [-1,-1] has negative dimensions
2017-08-15 15:39:14.915038: E tensorflow/core/common_runtime/executor.cc:644] Executor failed to create kernel. Invalid argument: Shape [-1,-1] has negative dimensions
[[Node: dense_2_target = Placeholder[dtype=DT_FLOAT, shape=[?,?], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
2017-08-15 15:39:14.915310: W tensorflow/core/framework/op_kernel.cc:1148] Invalid argument: Shape [-1] has negative dimensions
2017-08-15 15:39:14.915321: E tensorflow/core/common_runtime/executor.cc:644] Executor failed to create kernel. Invalid argument: Shape [-1] has negative dimensions
[[Node: dense_2_sample_weights = Placeholder[dtype=DT_FLOAT, shape=[?], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Traceback (most recent call last):
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1139, in _do_call
return fn(*args)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1121, in _run_fn
status, run_metadata)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/contextlib.py", line 89, in __exit__
next(self.gen)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape [-1] has negative dimensions
[[Node: dense_2_sample_weights = Placeholder[dtype=DT_FLOAT, shape=[?], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "gradientlog.py", line 45, in <module>
main()
File "gradientlog.py", line 42, in main
print(get_gradient([x]))
File "/home/josteb/sandbox/keras/keras/backend/tensorflow_backend.py", line 2251, in __call__
**self.session_kwargs)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 997, in _run
feed_dict_string, options, run_metadata)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1132, in _do_run
target_list, options, run_metadata)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1152, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape [-1] has negative dimensions
[[Node: dense_2_sample_weights = Placeholder[dtype=DT_FLOAT, shape=[?], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'dense_2_sample_weights', defined at:
File "gradientlog.py", line 45, in <module>
main()
File "gradientlog.py", line 39, in main
model.compile(loss='mse', optimizer='RMSprop')
File "/home/josteb/sandbox/keras/keras/models.py", line 783, in compile
**kwargs)
File "/home/josteb/sandbox/keras/keras/engine/training.py", line 799, in compile
name=name + '_sample_weights'))
File "/home/josteb/sandbox/keras/keras/backend/tensorflow_backend.py", line 435, in placeholder
x = tf.placeholder(dtype, shape=shape, name=name)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 1530, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1954, in _placeholder
name=name)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/josteb/.local/opt/anaconda3/envs/timeseries/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Shape [-1] has negative dimensions
[[Node: dense_2_sample_weights = Placeholder[dtype=DT_FLOAT, shape=[?], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
答案 0 :(得分：4)
在Keras中有几个与梯度计算过程相关的占位符：
x y model.fit()中提供权重，Keras仍会为样本权重生成占位符，并在培训期间将np.ones((y.shape[0],), dtype=K.floatx())提供给图表。Dropout）。因此，在您提供的示例中，为了计算渐变，您需要将x，y和sample_weights提供给图表。这是错误的根本原因。
在Model._make_train_function()内，the following lines显示了在这种情况下如何构建K.function()的必要输入：
inputs = self._feed_inputs + self._feed_targets + self._feed_sample_weights
if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
inputs += [K.learning_phase()]
with K.name_scope('training'):
...
self.train_function = K.function(inputs,
[self.total_loss] + self.metrics_tensors,
updates=updates,
name='train_function',
**self._function_kwargs)
通过模仿此功能，您应该能够获得标准值：
def get_gradient_norm_func(model):
grads = K.gradients(model.total_loss, model.trainable_weights)
summed_squares = [K.sum(K.square(g)) for g in grads]
norm = K.sqrt(sum(summed_squares))
inputs = model.model._feed_inputs + model.model._feed_targets + model.model._feed_sample_weights
func = K.function(inputs, [norm])
return func
def main():
x = np.random.random((128,)).reshape((-1, 1))
y = 2 * x
model = Sequential(layers=[Dense(2, input_shape=(1,)),
Dense(1)])
model.compile(loss='mse', optimizer='rmsprop')
get_gradient = get_gradient_norm_func(model)
history = model.fit(x, y, epochs=1)
print(get_gradient([x, y, np.ones(len(y))]))
执行输出：
Epoch 1/1
128/128 [==============================] - 0s - loss: 2.0073
[4.4091368]
请注意，由于您使用的是Sequential而不是Model，因此需要model.model._feed_*而不是model._feed_*。
答案 1 :(得分：1)
扩展josteinb's comment，我要分享使用的版本。
与the previous answer基本相同，但是此版本将范数计算集成到常规训练例程中。
import keras.backend as K
# Get a "l2 norm of gradients" tensor
def get_gradient_norm(model):
with K.name_scope('gradient_norm'):
grads = K.gradients(model.total_loss, model.trainable_weights)
norm = K.sqrt(sum([K.sum(K.square(g)) for g in grads]))
return norm
# Build a model
model = Model(...)
# Compile the model
model.compile(
loss="categorical_crossentropy",
optimizer="adam",
metrics=["categorical_accuracy"],
)
# Append the "l2 norm of gradients" tensor as a metric
model.metrics_names.append("gradient_norm")
model.metrics_tensors.append(get_gradient_norm(model))
# You can compute the norm within the usual training routine
loss, acc, gradient_norm = model.train_on_batch(batch, label)