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#![feature(arbitrary_self_types)]
use pyo3::prelude::*;
use pyo3::pyclass::PyClassShell;
use pyo3::types::{PyBytes, PyTuple};
use pyo3::ToPyObject;

use bincode::{deserialize, serialize};
use serde::{Deserialize, Serialize};

#[derive(Serialize, Deserialize)]

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PSQL

Magic words:

psql -U postgres

Some interesting flags (to see all, use -h or --help depending on your psql version):

• -E: will describe the underlaying queries of the \ commands (cool for learning!)
• -l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)

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import torch
import torch.nn as nn


def compute_scale_and_shift(prediction, target, mask):
    # system matrix: A = [[a_00, a_01], [a_10, a_11]]
    a_00 = torch.sum(mask * prediction * prediction, (1, 2))
    a_01 = torch.sum(mask * prediction, (1, 2))
    a_11 = torch.sum(mask, (1, 2))

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To remove a submodule you need to:

• Delete the relevant section from the .gitmodules file.
• Stage the .gitmodules changes git add .gitmodules
• Delete the relevant section from .git/config.
• Run git rm --cached path_to_submodule (no trailing slash).
• Run rm -rf .git/modules/path_to_submodule (no trailing slash).
• Commit git commit -m "Removed submodule "
• Delete the now untracked submodule files rm -rf path_to_submodule

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These instructions will explain how to install tensorflow on mac with cuda enabled GPU suport. I assume you know what tensorflow is and why you would want to have a deep learning framework running on your computer.

Prerequisites

Make sure to update your homebrew formulas

brew update

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#!/usr/bin/env python

"""
Pass data between processes started through the multiprocessing module
using pyzmq and process them with PyCUDA
"""

import numpy as np
import zmq
import multiprocessing as mp

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import os
import numpy as np
from matplotlib import pyplot as plt
from time import time

from foxhound import activations
from foxhound import updates
from foxhound import inits
from foxhound.theano_utils import floatX, sharedX

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import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from theano.tensor.signal.downsample import max_pool_2d
from theano.tensor.extra_ops import repeat
from theano.sandbox.cuda.dnn import dnn_conv

from time import time
import numpy as np
from matplotlib import pyplot as plt