TL;DR Your input data is not normalized. use x_data = (x_data – x_data.mean()) / x_data.std() increase the learning rate optimizer = torch.optim.Adam(model.parameters(), lr=0.01) You’ll get convergence in only 1000 iterations. More details The key difference between the two examples you have is that the data x in the first example is centered around (0, 0) … Read more
Your NetActor does not directly store any nn.Parameter. Moreover, all other layers it eventually uses in forward are stored as a simple list in self.nn_layers. If you want self.actor_nn.parameters() to know that the items stored in the list self.nn_layers may contain trainable parameters, you should work with containers. Specifically, making self.nn_layers to be a nn.ModuleList … Read more
Based on what you said it sounds like you need a larger batch_size, and of course there are implications with that which could impact the steps_per_epoch and number of epochs. To solve for jumping-around A larger batch size will give you a better gradient and will help to prevent jumping around You may also want … Read more
You can use TensorBoard’s Python classes or script to extract the data: How can I export data from TensorBoard? If you’d like to export data to visualize elsewhere (e.g. iPython Notebook), that’s possible too. You can directly depend on the underlying classes that TensorBoard uses for loading data: python/summary/event_accumulator.py (for loading data from a single … Read more
Try saving your model after you train on the first batch. Then, on successive runs, provide the xgb.train method with the filepath of the saved model. Here’s a small experiment that I ran to convince myself that it works: First, split the boston dataset into training and testing sets. Then split the training set into … Read more
If you have this error after an upgrade to TensorFlow 2.0, you can still use 1.X API by replacing: import tensorflow as tf by import tensorflow.compat.v1 as tf tf.disable_v2_behavior()
So to prevent another dozen of answers suggesting invalid in the context of this thread suggestion to !kill -9 -1, let’s close this thread: The answer is simple: As of this writing Google simply gives only 5% of GPU to some of us, whereas 100% to the others. Period. dec-2019 update: The problem still exists … Read more
Print X_train shape. What do you see? I’d bet X_train is 2d (matrix with a single column), while y_train 1d (vector). In turn you get different sizes. I think using X_train[:,0] for plotting (which is from where the error originates) should solve the problem