by Suf | Nov 13, 2024 | Machine Learning, PyTorch
Contents Introduction Gradients in Neural Networks Backpropagation and Gradient Descent Without zero_grad() With zero_grad() Plotting Losses Monitoring Loss Summary When training neural networks in PyTorch, calling zero_grad() is essential before backpropagating...
by Suf | Nov 13, 2024 | Data Science, Machine Learning, PyTorch
PyTorch provides a variety of tensor operations, and understanding the differences between torch.mm, torch.matmul, and torch.mul is essential when working with tensor computations. Although they might look similar, these functions serve different purposes and operate...
by Suf | Mar 27, 2022 | Programming, Python, Tips
In TensorFlow 2.0, tf.GraphDef is no longer in use. TensorFlow 2.0 encapsulates graph computations as Python functions instead of using Session making TensorFlow more Pythonic. If you want to continue using GraphDef in TensorFlow 2.0, use tf.compat.v1.Graphdef()...
by Suf | Mar 26, 2022 | Machine Learning, Programming, Python, Tips
TensorFlow 2 has integrated deep-learning Keras API as tensorflow.keras. If you try to import from the standalone Keras API with a Tensorflow 2 installed on your system, this can raise incompatibility issues, and you may raise the AttributeError: module...
by Suf | Mar 16, 2022 | Machine Learning, Programming, Python, PyTorch
Concatenating joins a sequence of tensors along an existing axis. The PyTorch function for concatenation is cat(). Stacking joins a sequence of tensors along a new axis. The PyTorch function for stacking is stack(). Table of Contents Introduction PyTorch Cat PyTorch...
