ikhlestov

import torch


class MyFunction(torch.autograd.Function):

    @staticmethod
    def forward(ctx, input):
        ctx.save_for_backward(input)
        output = torch.sign(input)
        return output

ikhlestov

from torch import nn


class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.feature_extractor = nn.Sequential(
            nn.Conv2d(3, 12, kernel_size=3, padding=1, stride=1),
            nn.Conv2d(12, 24, kernel_size=3, padding=1, stride=1),
        )

ikhlestov

from collections import OrderedDict

import torch.nn as nn


# Example of using Sequential
model = nn.Sequential(
    nn.Conv2d(1, 20, 5),
    nn.ReLU(),
    nn.Conv2d(20, 64, 5),

ikhlestov

import torch

first_counter = torch.Tensor([0])
second_counter = torch.Tensor([10])
some_value = torch.Tensor(15)

while (first_counter < second_counter)[0]:
    first_counter += 2
    second_counter += 1

ikhlestov

import tensorflow as tf


first_counter = tf.constant(0)
second_counter = tf.constant(10)
some_value = tf.Variable(15)


# condition should handle all args:
def cond(first_counter, second_counter, *args):

ikhlestov

import torch
from torch.autograd import Variable
import torch.nn.functional as F


x = Variable(torch.randn(10, 20), requires_grad=False)
y = Variable(torch.randn(10, 3), requires_grad=False)
# define some weights
w1 = Variable(torch.randn(20, 5), requires_grad=True)
w2 = Variable(torch.randn(5, 3), requires_grad=True)

ikhlestov

import torch
from torch.autograd import Variable

# define an inputs
x_tensor = torch.randn(10, 20)
y_tensor = torch.randn(10, 5)
x = Variable(x_tensor, requires_grad=False)
y = Variable(y_tensor, requires_grad=False)
# define some weights
w = Variable(torch.randn(20, 5), requires_grad=True)

ikhlestov

import torch
import numpy as np

numpy_tensor = np.random.randn(10, 20)

# convert numpy array to pytorch array
pytorch_tensor = torch.Tensor(numpy_tensor)
# or another way
pytorch_tensor = torch.from_numpy(numpy_tensor)

ikhlestov

import json


class TimeLiner:
    _timeline_dict = None

    def update_timeline(self, chrome_trace):
        # convert crome trace to python dict
        chrome_trace_dict = json.loads(chrome_trace)
        # for first run store full trace

ikhlestov

import os
import tempfile

import tensorflow as tf
from tensorflow.contrib.layers import fully_connected as fc
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.client import timeline

batch_size = 100