ufldl_tutorial
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jatinshah
Stanford Unsupervised Feature Learning and Deep Learning Tutorial
In calculating the gradient, you only consider take derivatives to the z2, but not taking the sparsity loss into consideration.
In section [Recovering an Approximation of the Data](http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/#recovering_an_approximation_of_the_data), isn't  a vector? If so, why do we get:
when I run the train.py,I get the following error images = images.reshape((num_images[0], num_rows[0] * num_cols[0])).transpose() Traceback (most recent call last): File "/home/snow/Documents/study/ufldl_tutorial/train.py", line 42, in images = load_MNIST.load_MNIST_images('data/mnist/train-images-idx3-ubyte') File "/home/snow/Documents/study/ufldl_tutorial/load_MNIST.py",...
Hi guys,, have you the problem of terrified slow computation when run the line num_grad = gradient.compute_gradient(J, theta) % line 101 in train.py It has already taken more than 12...
File "........\load_MNIST.py", line 18, in load_MNIST_images images = images.reshape((num_images, num_rows \* num_cols)).transpose() ValueError: total size of new array must be unchanged
Matlab mnist images are stored column major order, whereas this version stores them in row major. This means that when transferring weights between Python and Matlab implementations, you need to...
You should add a np.sqrt above s+epsilon like below. (u, s, vt) = np.linalg.svd(sigma) zca_white = u.dot(np.diag(1 / np.sqrt(s + epsilon))).dot(u.transpose())
