thomwolf

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...

cgoldberg

#!/bin/bash
# download and install latest geckodriver for linux or mac.
# required for selenium to drive a firefox browser.

install_dir="/usr/local/bin"
json=$(curl -s https://api.github.com/repos/mozilla/geckodriver/releases/latest)
if [[ $(uname) == "Darwin" ]]; then
    url=$(echo "$json" | jq -r '.assets[].browser_download_url | select(contains("macos"))')
elif [[ $(uname) == "Linux" ]]; then
    url=$(echo "$json" | jq -r '.assets[].browser_download_url | select(contains("linux64"))')