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" Don't try to be vi compatible | |
set nocompatible | |
" Helps force plugins to load correctly when it is turned back on below | |
filetype off | |
" TODO: Load plugins here (pathogen or vundle) | |
" Turn on syntax highlighting | |
syntax on |
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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... |
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{0: 'tench, Tinca tinca', | |
1: 'goldfish, Carassius auratus', | |
2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', | |
3: 'tiger shark, Galeocerdo cuvieri', | |
4: 'hammerhead, hammerhead shark', | |
5: 'electric ray, crampfish, numbfish, torpedo', | |
6: 'stingray', | |
7: 'cock', | |
8: 'hen', | |
9: 'ostrich, Struthio camelus', |
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import os | |
import numpy as np | |
import scipy.ndimage as ndimage | |
import matplotlib | |
import matplotlib.pyplot as plt | |
def frac_eq_to(image, value=0): | |
return (image == value).sum() / float(np.prod(image.shape)) | |
def extract_patches(image, patchshape, overlap_allowed=0.5, cropvalue=None, |