awesome-cheatsheet | : beers : awesome cheatsheet | Learning library

import numpy as np
import torch
from torchvision import models
import torchvision.transforms as transforms
from PIL import Image
from flask import Flask, jsonify, request
import json


app = Flask(__name__)
app.config['JSON_SORT_KEYS'] = False

class  

import torch.nn.utils.prune as prune

parameters_to_prune = (
    (model.conv1, 'weight'),
    (model.conv2, 'weight'),
    (model.fc1, 'weight'),
    (model.fc2, 'weight'),
    (model.fc3, 'weight'),
)

prune.global_unstructured(
    parameters_to_p  

def weight_init(m):

	# LINEAR
	if type(m) == nn.Linear:
		torch.nn.init.xavier_uniform(m.weight)
		m.bias.data.fill_(0.01)

	# CONVS
	classname = m.__class__.__name__
	if classname.find('Conv') != -1:
		nn.init.xavier_uniform_(m.weight, gain=nn.init