mttk

import torch
import torch.nn as nn
import torch.nn.functional as F

# Just some regular data definition

device = 'cuda:0'
embed_dim = 2
hidden_dim = 2
B = 4

mttk

import torch
import torch.nn as nn
import torch.nn.functional as F

embed_dim = 2
hidden_dim = 2
B = 4
T = 2
y = torch.tensor([0, 1, 0, 1])

mttk

lstm = torch.lstm_cell
grad_history = []

def store_grads(name, timestep):
    def hook(grad):
        print(name, grad, timestep)
        grad_history[timestep][name] = grad
    return hook

def lstm_stepwise(input, h0, w_ih, w_hh, b_ih, b_hh):

mttk

SSDS 2017 - 2^nd Int'l Summer School on Data Science

Center of Research Excellence for Data Science and Advanced Cooperative Systems, Research Unit for Data Science

Hands-on sessions for 2nd International Summer School on Data Science organized by the Center of Research Excellence for Data Science and Advanced Cooperative Systems, Research Unit for Data Science, from September 25-29, 2017 in Split, Croatia.

Table Of Contents

• Day 1 - Introduction to Tensorflow
• Day 2 - Convolutional Neural Networks for Image Classification

mttk

import logging
import os, sys
import numpy as np
import matplotlib.pyplot as plt
import gym
import Image

# Import the renderer as it's what we'll be using to plot 
from gym.envs.classic_control import rendering