tensorflow

This problem is caused by the following line: tf.nn.softmax_cross_entropy_with_logits(labels=activation, logits=Y) Based on documentation you should have labels: Each row labels[i] must be a valid probability distribution. logits: Unscaled log probabilities. So logits suppose to be your hypothesis and thus equal to activation and valid probability distribution is Y. So just change it with tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=activation)

You can take the Lagrangian approach and simply add a penalty for features of the variable you don’t want. e.g. To encourage theta to be non-negative, you could add the following to the optimizer’s objective function. added_loss = -tf.minimum( tf.reduce_min(theta),0) If any theta are negative, then add2loss will be positive, otherwise zero. Scaling that to … Read more