gradient-descent

I've been following an online tutorial on deep learning. It has a practical question on gradient descent and cost calculations where I been struggling to get the given answers once it was converted to python code. Hope you can kindly help me get the correct answer please Please see the following link for the equations used Click here to see the equations used for the calculations Following is the function given to calculate the gradient descent,cost etc. The values need to be found without using for loops but using matrix manipulation operations import numpy as np def propagate(w, b, X, Y): ""

My question is how can I convert a constant tensor loaded from a pre-trained Vgg16 model to a tf.Variable tensor? The motivation is that I need to compute the gradient of a specific loss with respect to the Conv4_3 layers' kernel, however, the kernel were seems set to a tf.Constant type and it is not accepted by tf.Optimizer.compute_gradients method. F = vgg.graph.get_tensor_by_name('pretrained_vgg16/conv4_3/filter:0') G = optimizer.compute_gradients(losses, var_list=[F]) # TypeError: Argument is not a tf.Variable: Tensor("pretrained_vgg16/conv4_3/filter:0", shape=(3, 3, 512, 512), dtype

I'm doing gradient descent in matlab for mutiple variables, and the code is not getting the expected thetas I got with the normal eq. that are: theta = 1.0e+05 * 3.4041 1.1063 -0.0665 With the Normal eq. I have implemented. And with the GDM the results I get are: theta = 1.0e+05 * 2.6618 -2.6718 -0.5954 And I don't understand why is this, maybe some one can help me and tell me where is the mistake in the code. Code: function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters) m = length(y); % number of training examples J_history = zeros(num_iters, 1); thetas = size(theta

I have a question similar to this one . Because I have limited resources and I work with a deep model (VGG-16) - used to train a triplet network - I want to accumulate gradients for 128 batches of size one training example, and then propagate the error and update the weights. It's not clear to me how do I do this. I work with tensorflow but any implementation/pseudocode is welcome. Let's walk through the code proposed in one of the answers you liked to: ## Optimizer definition - nothing different from any classical example opt = tf.train.AdamOptimizer() ## Retrieve all trainable variables you

I have just started to learn deep learning. I found myself stuck when it came to gradient descent. I know how to implement batch gradient descent. I know how it works as well how mini-batch and stochastic gradient descent works in theory. But really can't understand how to implement in code. import numpy as np X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ]) y = np.array([[0,1,1,0]]).T alpha,hidden_dim = (0.5,4) synapse_0 = 2*np.random.random((3,hidden_dim)) - 1 synapse_1 = 2*np.random.random((hidden_dim,1)) - 1 for j in xrange(60000): layer_1 = 1/(1+np.exp(-(np.dot(X,synapse_0)))) layer_2 =

I'm working on machine learning problem and want to use linear regression as learning algorithm. I have implemented 2 different methods to find parameters theta of linear regression model: Gradient (steepest) descent and Normal equation. On the same data they should both give approximately equal theta vector. However they do not. Both theta vectors are very similar on all elements but the first one. That is the one used to multiply vector of all 1 added to the data. Here is how the theta s look like (fist column is output of Gradient descent, second output of Normal equation): Grad desc Norm