WebThis repository contains the programming assignments for Deep Learning specialization courses by Andrew Ng. It deals with the following concepts. DNNs, Hyperparameter … WebThe add_loss() API. Loss functions applied to the output of a model aren't the only way to create losses. When writing the call method of a custom layer or a subclassed model, …

Tensorflow Adjusting Cost Function for Imbalanced Data

WebComputes the mean of elements across dimensions of a tensor. WebFeb 4, 2024 · You use this function to set the cost value: cost = compute_cost (Z5, Y) So cost is None here, which you pass into the .minimize () method. The fix is to use return; presumably you wanted to return the tf.reduce_mean () result: def compute_cost (Z5, Y): return tf.reduce_mean (tf.nn.softmax_cross_entropy_with_logits (logits=Z5, labels=Y)) … heather gibbs facebook

Understand tf.reduce_mean with Examples for Beginners - Tutorial …

WebMay 28, 2024 · Sorted by: 2. When your hypothesis is equal to 1, your second part of the loss becomes Y * log (0), hence the nan output. I suggest you to add a small constant inside the logarithm and it should work. Try this. cost = -tf.reduce_mean (Y* (tf.log (hypothesis+1e-4))+ (1-Y)* (tf.log (1-hypothesis+1e-4))) Share. Follow. WebOct 20, 2016 · cost = tf.reduce_mean(tf.pow(pred - Y, 2)) / 2 This is the original code cost = tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * n_samples) But I found the result is different. … WebAug 22, 2024 · If a callable, loss should take no arguments and return the value to minimize. If a Tensor, the tape argument must be passed. The first piece of code takes tensor as the input of minimize (), and it requires the gradient tape, but I don't know how. The second piece of code takes callable function as the input of minimize (), which is easy. movie deadly blessing 1981