Is permute operation differentiable operation in PyTorch - machine-learning

For my project I am using permute operation to change the index order of the e tensor in encoder path but I am not sure if this is differentiable operation? If it is not, Is there any way to change tensor shape before the convolution.

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Understanding log-softmax gradient implementation for Policy Gradient RL

I am trying to understand how to compute the gradient of log-softmax function in matrix form for policy function parameter update in Policy Gradient RL.
I found one Python implementation online from here using the Cartpole environment as an example.
In the example, the author uses softmax policy. This is implemented as per below:
def policy(state, w):
"""Computes softmax policy, i.e. the decision probabilities
for each action in our action space.
:param state: state vector,
:param theta: parameters,
:return: vector of decision probabilities.
"""
z = state.dot(w)
exp = np.exp(z)
return exp/np.sum(exp)
In the article, instead of computing the gradient of log-softmax directly, we obtain it by (1) first computing the Jacobian-matrix of the softmax, (2) then by dividing the a "sliced-Jacobian" with the policy probability and finally (3) by multiplying this ratio from (1) and (2) with the state vector.
The Jacobian of the softmax is computed using the following code:
def softmax_grad(softmax):
"""Computes the Jacobian of the softmax function."""
s = softmax.reshape(-1,1)
return np.diagflat(s) — np.dot(s, s.T)
The derivation of the Jacobian for the softmax function is rather easy. However, it is the next part that is confusing me:
# Computation of the gradient for log-softmax using Jacobian of softmax.
dsoftmax = softmax_grad(probs)[action, :] # Why do we only take one row?
dlog = dsoftmax / probs[0,action]
grad = state.T.dot(dlog[None, :]) # Why do we multiply with the state?
In the above, the grad is the final gradient for log-softmax function. I just don't understand why.
My questions are the following:
What is the mathematical justification for this computation?
How can we derive this computation mathematically?
Why are we slicing the Jacobian?
Why are we multiplying the resulting probabilities with the state?
I'd appreciate a formal and mathematical derivation for this. Any ideas?
Thanks!

How do I perform a differentiable operation selection in TensorFlow?

I am trying to produce a mathematical operation selection nn model, which is based on the scalar input. The operation is selected based on the softmax result which is produce by the nn. Then this operation has to be applied to the scalar input in order to produce the final output. So far I’ve come up with applying argmax and onehot on the softmax output in order to produce a mask which then is applied on the concated values matrix from all the possible operations to be performed (as show in the pseudo code below). The issue is that neither argmax nor onehot appears to be differentiable. I am new to this, so any would be highly appreciated. Thanks in advance.
#perform softmax
logits = tf.matmul(current_input, W) + b
softmax = tf.nn.softmax(logits)
#perform all possible operations on the input
op_1_val = tf_op_1(current_input)
op_2_val = tf_op_2(current_input)
op_3_val = tf_op_2(current_input)
values = tf.concat([op_1_val, op_2_val, op_3_val], 1)
#create a mask
argmax = tf.argmax(softmax, 1)
mask = tf.one_hot(argmax, num_of_operations)
#produce the input, by masking out those operation results which have not been selected
output = values * mask
I believe that this is not possible. This is similar to Hard Attention described in this paper. Hard attention is used in Image captioning to allow the model to focus only on a certain part of the image at each step. Hard attention is not differentiable but there are 2 ways to go around this:
1- Use Reinforcement Learning (RL): RL is made to train models that makes decisions. Even though, the loss function won't back-propagate any gradients to the softmax used for the decision, you can use RL techniques to optimize the decision. For a simplified example, you can consider the loss as penalty, and send to the node, with the maximum value in the softmax layer, a policy gradient proportional to the penalty in order to decrease the score of the decision if it was bad (results in a high loss).
2- Use something like soft attention: instead of picking only one operation, mix them with weights based on the softmax. so instead of:
output = values * mask
Use:
output = values * softmax
Now, the operations will converge down to zero based on how much the softmax will not select them. This is easier to train compared to RL but it won't work if you must completely remove the non-selected operations from the final result (set them to zero completely).
This is another answer that talks about Hard and Soft attention that you may find helpful: https://stackoverflow.com/a/35852153/6938290

Cross Entropy Loss for Semantic Segmentation Keras

I'm pretty sure this is a silly question but I can't find it anywhere else so I'm going to ask it here.
I'm doing semantic image segmentation using a cnn (unet) in keras with 7 labels. So my label for each image is (7,n_rows,n_cols) using the theano backend. So across the 7 layers for each pixel, it's one-hot encoded. In this case, is the correct error function to use categorical cross-entropy? It seems that way to me but the network seems to learn better with binary cross-entropy loss. Can someone shed some light on why that would be and what the principled objective is?
Binary cross-entropy loss should be used with sigmod activation in the last layer and it severely penalizes opposite predictions. It does not take into account that the output is a one-hot coded and the sum of the predictions should be 1. But as mis-predictions are severely penalizing the model somewhat learns to classify properly.
Now to enforce the prior of one-hot code is to use softmax activation with categorical cross-entropy. This is what you should use.
Now the problem is using the softmax in your case as Keras don't support softmax on each pixel.
The easiest way to go about it is permute the dimensions to (n_rows,n_cols,7) using Permute layer and then reshape it to (n_rows*n_cols,7) using Reshape layer. Then you can added the softmax activation layer and use crossentopy loss. The data should also be reshaped accordingly.
The other way of doing so will be to implement depth-softmax :
def depth_softmax(matrix):
sigmoid = lambda x: 1 / (1 + K.exp(-x))
sigmoided_matrix = sigmoid(matrix)
softmax_matrix = sigmoided_matrix / K.sum(sigmoided_matrix, axis=0)
return softmax_matrix
and use it as a lambda layer:
model.add(Deconvolution2D(7, 1, 1, border_mode='same', output_shape=(7,n_rows,n_cols)))
model.add(Permute(2,3,1))
model.add(BatchNormalization())
model.add(Lambda(depth_softmax))
If tf image_dim_ordering is used then you can do way with the Permute layers.
For more reference check here.
I tested the solution of #indraforyou and think that the proposed method has some mistakes. As the commentsection does not allow for proper code segments, here is what I think would be the fixed version:
def depth_softmax(matrix):
from keras import backend as K
exp_matrix = K.exp(matrix)
softmax_matrix = exp_matrix / K.expand_dims(K.sum(exp_matrix, axis=-1), axis=-1)
return softmax_matrix
This method will expect the ordering of the matrix to be (height, width, channels).

How are the following types of neural network-like techniques called?

The neural network applications I've seen always learn the weights of their inputs and use fixed "hidden layers".
But I'm wondering about the following techniques:
1) fixed inputs, but the hidden layers are no longer fixed, in the sense that the functions of the input they compute can be tweaked (learned)
2) fixed inputs, but the hidden layers are no longer fixed, in the sense that although they have clusters which compute fixed functions (multiplication, addition, etc... just like ALUs in a CPU or GPU) of their inputs, the weights of the connections between them and between them and the input can be learned (this should in some ways be equivalent to 1) )
These could be used to model systems for which we know the inputs and the output but not how the input is turned into the output (figuring out what is inside a "black box"). Do such techniques exist and if so, what are they called?
For part (1) of your question, there are a couple of relatively recent techniques that come to mind.
The first one is a type of feedforward layer called "maxout" which computes a piecewise linear output function of its inputs.
Consider a traditional neural network unit with d inputs and a linear transfer function. We can describe the output of this unit as a function of its input z (a vector with d elements) as g(z) = w z, where w is a vector with d weight values.
In a maxout unit, the output of the unit is described as
g(z) = max_k w_k z
where w_k is a vector with d weight values, and there are k such weight vectors [w_1 ... w_k] per unit. Each of the weight vectors in the maxout unit computes some linear function of the input, and the max combines all of these linear functions into a single, convex, piecewise linear function. The individual weight vectors can be learned by the network, so that in effect each linear transform learns to model a specific part of the input (z) space.
You can read more about maxout networks at http://arxiv.org/abs/1302.4389.
The second technique that has recently been developed is the "parametric relu" unit. In this type of unit, all neurons in a network layer compute an output g(z) = max(0, w z) + a min(w z, 0), as compared to the more traditional rectified linear unit, which computes g(z) = max(0, w z). The parameter a is shared across all neurons in a layer in the network and is learned along with the weight vector w.
The prelu technique is described by http://arxiv.org/abs/1502.01852.
Maxout units have been shown to work well for a number of image classification tasks, particularly when combined with dropout to prevent overtraining. It's unclear whether the parametric relu units are extremely useful in modeling images, but the prelu paper gets really great results on what has for a while been considered the benchmark task in image classification.

The cost function and gradient of softmax classifier

When training a softmax classifier, I used minFunc function in Matlab, but it didn't work, the step size would reach TolX quickly and the accuracy is not even 5%. There must be something wrong but I just couldn't find it.
Here is my Matlab code about the cost function and gradient:
z=x*W; %x is the input data, it's an m*n matrix, m is the number of samples, n is the number of units in the input layer. W is an n*o matrix, o is the number of units in the output layer.
a=sigmoid(z)./repmat(sum(sigmoid(z),2),1,o); %a is the output of the classifier.
J=-mean(sum(target.*log(a),2))+l/2*sum(sum(W.^2)); %This is the cost function, target is the desired output, it's an m*n matrix. l is the weight decay parameter.
Wgrad=-x'*(target-a)/m+l*W;
the formula can be found here. Can anyone point out where my error is?
I found the error, I should not use the sigmoid function, it should simply be exp.

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