I need to translate this code to pytorch. The code given below use np.vectorize. I am looking for a pytorch equivalent for this.
class SimplexPotentialProjection(object):
def __init__(self, potential, inversePotential, strong_convexity_const, precision = 1e-10):
self.inversePotential = inversePotential
self.gradPsi = np.vectorize(potential)
self.gradPsiInverse = np.vectorize(inversePotential)
self.precision = precision
self.strong_convexity_const = strong_convexity_const
The doc for numpy.vectorize clearly states that:
The vectorize function is provided primarily for convenience, not for performance. The implementation is essentially a for loop.
Therefore, in order to convert your numpy code to pytorch you'll simply need apply potential and inversePotential in a loop over their tensor arguments.
However, that might be very inefficient. You would better re-implement your functions to act "natively" in a vectorized manner on tensors.
Related
I want to implement a new operation similar to matrix multiplication in tensorflow. For example, If there are two matrices input, W and C = Op(input,W), then, C[i,j] = tf.math.reduce_prod(input[i,:] - W[:,j]). For implementing this operation I am planning to use two "for-loops" iterating over rows & columns, however, I believe that the for-loop implementation will decrease the training performance. I tried looking into the implementation of tf.lingalg.matmul() but the function seems to call C++ binaries which are not editable. Is there any way to speed up this computation? The following is the function I have written to implement Op(input,w):
def difference(input,w):
result = tf.Variable(tf.zeros((input.shape[0],w.shape[1]),dtype=tf.float16))
for i in tf.range(0,input.shape[0]):
for j in tf.range(0,w.shape[1]):
result[i,j].assign(tf.math.reduce_prod(input[i,:] - W[:,j]))
return result
I'm a beginner in pytorch, and I have some functions that are needed to implement in network.
My question is: is there any way like tf.function, or should I use "class(nn.Module)" with variable?
For example, let X be a 10x2 matrix . In pseudo-code:
a = Variable(1.0)
b = Variable(1.0)
Y = a*X[:,0]**2 + b*X[:,1]
In PyTorch you don't need things like tf.function, you just use normal Python code (because of the dynamic graph).
Please give more detailed example (with code) of what you're trying to do if the above doesn't answer your question.
I'm trying to run a simple convolution but with complex numbers:
r = np.random.random([1,10,10,10])
i = np.random.random([1,10,10,10])
x = tf.complex(r,i)
conv_layer = tf.layers.conv2d(
inputs=x,
filters=10,
kernel_size=[3,3],
kernel_initializer=utils.truncated_normal_complex(),
activation=tf.nn.sigmoid)
However I get this error:
TypeError: Value passed to parameter 'input' has DataType complex128 not in list of allowed values: float16, float32
Does anyone know how to implement such a convolution in Tensorflow?
Will I need to implement a custom op, or is there some better option here?
Frustratingly, complex matrix multiplication is possible, e.g. the following runs fine:
def r():
return np.random.random([10,10])
A = tf.complex(r(),r())
B = tf.complex(r(),r())
C = tf.multiply(A,B)
sess.run(C)
So there's no real reason convolution shouldn't work, I would think (as convolution is essentially just matrix multiplication).
Thanks
Probably too late but for anyone who still is interested: applying convolutions to complex valued data is not as straightforward as your usual data types, like float32. There are studies that investigat different network structures for this purpose (for example see this link for "Deep Complex U-Net"). There are implementations of these structures in pytorch and tensorflow.
All complex-valued features are split into either Cartesian (real, imaginary) or polar (modulus, angle) representations. Nobody is really trying to use a single feature that is purely complex; I would love to be proven wrong!
Two parts to this question:
(1) What is the best way to update a subset of a tensor in tensorflow? I've seen several related questions:
Adjust Single Value within Tensor -- TensorFlow
and
How to update a subset of 2D tensor in Tensorflow?
and I'm aware that Variable objects can be assigned using Variable.assign() (and/or scatter_update, etc.), but it seems very strange to me that tensorflow does not have a more intuitive way to update a part of a Tensor object. I have searched through the tensorflow api docs and stackoverflow for quite some time now and can't seem to find a simpler solution than what is presented in the links above. This seems particularly odd, especially given that Theano has an equivalent version with Tensor.set_subtensor(). Am I missing something or is there no simple way to do this through the tensorflow api at this point?
(2) If there is a simpler way, is it differentiable?
Thanks!
I suppose the immutability of Tensors is required for the construction of a computation graph; you can't have a Tensor update some of its values without becoming another Tensor or there will be nothing to put in the graph before it. The same issue comes up in Autograd.
It's possible to do this (but ugly) using boolean masks (make them variables and use assign, or even define them prior in numpy). That would be differentiable, but in practice I'd avoid having to update subtensors.
If you really have to, and I really hope there is a better way to do this, but here is a way to do it in 1D using tf.dynamic_stitch and tf.setdiff1d:
def set_subtensor1d(a, b, slice_a, slice_b):
# a[slice_a] = b[slice_b]
a_range = tf.range(a.shape[0])
_, a_from = tf.setdiff1d(a_range, a_range[slice_a])
a_to = a_from
b_from, b_to = tf.range(b.shape[0])[slice_b], a_range[slice_a]
return tf.dynamic_stitch([a_to, b_to],
[tf.gather(a, a_from),tf.gather(b, b_from)])
For higher dimensions this could be generalised by abusing reshape (where nd_slice could be implemented like this but there is probably a better way):
def set_subtensornd(a, b, slice_tuple_a, slice_tuple_b):
# a[*slice_tuple_a] = b[*slice_tuple_b]
a_range = tf.range(tf.reduce_prod(tf.shape(a)))
a_idxed = tf.reshape(a_range, tf.shape(a))
a_dropped = tf.reshape(nd_slice(a_idxed, slice_tuple_a), [-1])
_, a_from = tf.setdiff1d(a_range, a_dropped)
a_to = a_from
b_range = tf.range(tf.reduce_prod(tf.shape(b)))
b_idxed = tf.reshape(b_range, tf.shape(b))
b_from = tf.reshape(nd_slice(b_idxed, slice_tuple_b), [-1])
b_to = a_dropped
a_flat, b_flat = tf.reshape(a, [-1]), tf.reshape(b, [-1])
stitched = tf.dynamic_stitch([a_to, b_to],
[tf.gather(a_flat, a_from),tf.gather(b_flat, b_from)])
return tf.reshape(stitched, tf.shape(a))
I have no idea how slow this will be. I'd guess quite slow. And, I haven't tested it much beyond running it on a couple of tensors.
This question and answer demonstrate that when feature selection is performed using one of scikit-learn's dedicated feature selection routines, then the names of the selected features can be retrieved as follows:
np.asarray(vectorizer.get_feature_names())[featureSelector.get_support()]
For example, in the above code, featureSelector might be an instance of sklearn.feature_selection.SelectKBest or sklearn.feature_selection.SelectPercentile, since these classes implement the get_support method which returns a boolean mask or integer indices of the selected features.
When one performs feature selection via linear models penalized with the L1 norm, it's unclear how to accomplish this. sklearn.svm.LinearSVC has no get_support method and the documentation doesn't make clear how to retrieve the feature indices after using its transform method to eliminate features from a collection of samples. Am I missing something here?
For sparse estimators you can generally find the support by checking where the non-zero entries are in the coefficients vector (provided the coefficients vector exists, which is the case for e.g. linear models)
support = np.flatnonzero(estimator.coef_)
For your LinearSVC with l1 penalty it would accordingly be
from sklearn.svm import LinearSVC
svc = LinearSVC(C=1., penalty='l1', dual=False)
svc.fit(X, y)
selected_feature_names = np.asarray(vectorizer.get_feature_names())[np.flatnonzero(svc.coef_)]
I've been using sklearn 15.2, and according to LinearSVC documentation , coef_ is an array, shape = [n_features] if n_classes == 2 else [n_classes, n_features].
So first, np.flatnonzero doesn't work for multi-class. You'll have index out of range error. Second, it should be np.where(svc.coef_ != 0)[1] instead of np.where(svc.coef_ != 0)[0] . 0 is index of classes, not features. I ended up with using np.asarray(vectorizer.get_feature_names())[list(set(np.where(svc.coef_ != 0)[1]))]