search – How to create a dataset for Deep Learning project?

I am new to deep learning. I have taken a few courses on the same.
I have a doubt in order to create a dataset, the examples I have seen so far, many of the researchers have used H5py/HDF5 file to create the datasets. Can anyone suggest to me how should I proceed? Can we use the CSV file instead?
E.g. If I want to create a dataset for the features. Should I create 2 datasets for the training and test sets? Or Within a single dataset, I should create 2 different ones?

“I have read that the Keras can simply get CSV into datasets.” Should I use this approach?

Ubuntu on Laptop for Deep Learning

I need a powerful laptop running Ubuntu with nVidia gpu for deep learning.
Which ones do you recommend?
Are nVidia Geforce RTX 20 series better than Quadro RTX 3000/4000/5000?
Thank you.

dnd 3.5e – I am looking for information and the physical description of Deep Halflings

They lose the bonuses to Climb, Jump and move Silently, but gain bonuses to Appraise and Craft checks with stone or metal.

They also have Stonecunning and Darkvision

“These halflings are shorter and stockier than the more common lightfeet.

Deep Halflings are about 2.5 feet tall and weigh between 30 and 35 pounds. Deep halflings speak Dwarven fluently.

dnd 3.5e – What is the description and information for the Deep Orc race in 3.5 D&D?

I need a physical description and information on the Deep Orc race from 3.5 D&D.
Can someone give me information or direct me to a source that can help me?

I tried searching, but the problem is that it often leads me to the Orog which I am unsure if it’s the same as a Deep Orc because the sources lead me to Forgotten Realms pages. Forgotten Realms creatures are not the same. The elven subrace names and even the appearance of the various elf types varies as an example. Even when I search for images I’ll get the 5e orog or something from Pathfinder.

I need a physical description and information for the Deep Orc race in 3.5 D&D

I need a physical description and information on the Deep Orc race from 3.5 D&D.
Can someone give me information or direct me to a source that can help me?

Help Setting Up Cyberduck for S3 Deep Glacier Upload

I need help figuring out how to setup specifically S3 DEEP Glacier file transfer. I have like 10TB I need to start uploading. PM me if you know how to set this up and how much you would charge. I was trying to use Cyberduck and wouldn't mind using it but got confused.

Deep map, Python – Code Review Stack Exchange

Goal: apply fn to every element of an arbitrarily nested iterable (tuple, list, dict, np.ndarray), including to iterables. Ex:

def fn1(x, key):  # ignore `key` for now
    return str(x) if not isinstance(x, Iterable) else x
def fn2(x, key):
    return x ** 2 if isinstance(x, (int, float, np.generic)) else x

arr = np.random.randint(0, 9, size=(2, 2))
obj = (1, {'a': 3, 'b': 4, 'c': ('5', 6., (7, 8)), 'd': 9}, arr)

deepmap(obj, fn1) == ('1', {'a': '3', 'b': '4', 'c': ('5', '6.0', ('7', '8')), 'd': '9'}, 
                      array(((6, 1), (5, 4))))
deepmap(obj, fn2) == (1, {'a': 9, 'b': 16, 'c': ('5', 36.0, (49, 64)), 'd': 81}, 
                      array(((36,  1), (25, 16))))

deeplen should also be a special case of deepmap, with proper fn (just a demo, don’t care for optimizing this):

def deeplen(obj):
    count = (0)
    def fn(x, key):
        if not isinstance(x, Iterable) or isinstance(x, str):
            count(0) += 1
        return x
    deepmap(obj, fn)
    return count(0)

deeplen(obj) == 12

My working implementation, along tests, below. Unsure this works without OrderedDict (Python >=3.6 default); it does if key order doesn’t change even as values are changed (but no keys inserted/popped). A resolution is appending actual Mapping keys to key instead of their index, but this complicates implementing. (As for why pass key to fn: we can implement e.g. deepequal, comparing obj against another obj; this requires key info).

Any improvements? I haven’t tested exhaustively – maybe there are objects for which this fails, hence subject to extendability. Performance/readability could be better also.


Implementation: live demo

from collections.abc import Iterable, Mapping

def deepmap(obj, fn):
    def deepget(obj, key=None, drop_keys=0):
        if not key:
            return obj
        if drop_keys != 0:
            key = key(:-drop_keys)
        for k in key:
            if isinstance(obj, Mapping):
                k = list(obj)(k)  # get key by index (OrderedDict, Python >=3.6)
            obj = obj(k)
        return obj

    def dkey(x, k):
        return list(x)(k) if isinstance(x, Mapping) else k

    def _process_key(obj, key, depth, revert_tuple_keys, recursive=False):
        container = deepget(obj, key, 1)
        item      = deepget(obj, key, 0)

        if isinstance(item, Iterable) and not isinstance(item, str) and (
                not recursive):
            depth += 1
        if len(key) == depth:
            if key(-1) == len(container) - 1:  # iterable end reached
                depth -= 1      # exit iterable
                key = key(:-1)  # drop iterable key
                if key in revert_tuple_keys:
                    supercontainer = deepget(obj, key, 1)
                    k = dkey(supercontainer, key(-1))
                    supercontainer(k) = tuple(deepget(obj, key))
                    revert_tuple_keys.pop(revert_tuple_keys.index(key))
                if depth == 0 or len(key) == 0:
                    key = None  # exit flag
                else:
                    # recursively exit iterables, decrementing depth
                    # and dropping last key with each recursion
                    key, depth = _process_key(obj, key, depth, revert_tuple_keys,
                                              recursive=True)
            else:  # iterate next element
                key(-1) += 1
        elif depth > len(key):
            key.append(0)  # iterable entry
        return key, depth

    key = (0) 
    depth = 1
    revert_tuple_keys = ()

    if isinstance(obj, tuple):
        obj = list(obj)
        revert_tuple_keys.append(None)  # revert to tuple at function exit

    while key is not None:
        container = deepget(obj, key, 1)
        item      = deepget(obj, key, 0)

        if isinstance(container, tuple):
            ls = list(container)  # cast to list to enable mutating
            ls(key(-1)) = fn(item, key)

            supercontainer = deepget(obj, key, 2)
            k = dkey(supercontainer, key(-2))
            supercontainer(k) = ls
            revert_tuple_keys.append(key(:-1))  # revert to tuple at iterable exit
        else:
            k = dkey(container, key(-1))
            container(k) = fn(item, key)

        key, depth = _process_key(obj, key, depth, revert_tuple_keys)

    if None in revert_tuple_keys:
        obj = tuple(obj)
    return obj

Testing:

import numpy as np
from collections.abc import Iterable
from copy import deepcopy
from time import time
from deepmap import deepmap


def fn1(x, key):
    return str(x) if not isinstance(x, Iterable) else x

def fn2(x, key):
    return x ** 2 if isinstance(x, (int, float, np.generic)) else x

def fn3(x, key):
    return str(x)

def make_bigobj():
    arrays = (np.random.randn(100, 100), np.random.uniform(30, 40, 10))
    lists = ((1, 2, '3', '4', 5, (6, 7)) * 555, {'a': 1, 'b': arrays(0)})
    dicts = {'x': (1, {2: (3, 4)}, (5, '6', {'7': 8}) * 99) * 55,
             'b': ({'a': 5, 'b': 3}) * 333, ('k', 'g'): (5, 9, (1, 2))}
    tuples = (1, (2, {3: np.array((4., 5.))}, (6, 7, 8, 9) * 21) * 99,
              (10, (11,) * 5) * 666)
    return {'arrays': arrays, 'lists': lists,
            'dicts': dicts, 'tuples': tuples}

def deeplen(obj):
    count = (0)
    def fn(x, key):
        if not isinstance(x, Iterable) or isinstance(x, str):
            count(0) += 1
        return x
    deepmap(obj, fn)
    return count(0)

#### CORRECTNESS  ##############################################################
np.random.seed(4)
arr = np.random.randint(0, 9, (2, 2))
obj = (1, {'a': 3, 'b': 4, 'c': ('5', 6., (7, 8)), 'd': 9}, arr)

out1 = deepmap(deepcopy(obj), fn1)
assert str(out1) == ("('1', {'a': '3', 'b': '4', 'c': ('5', '6.0', ('7', '8')), "
                     "'d': '9'}, array(((7, 5),n       (1, 8))))")
out2 = deepmap(deepcopy(obj), fn2)
assert str(out2) == ("(1, {'a': 9, 'b': 16, 'c': ('5', 36.0, (49, 64)), "
                     "'d': 81}, array(((49, 25),n       ( 1, 64))))")
out3 = deepmap(deepcopy(obj), fn3)
assert str(out3) == (r"""('1', "{'a': 3, 'b': 4, 'c': ('5', 6.0, (7, 8)), """
                     r"""'d': 9}", '((7 5)n (1 8))')""")

#### PERFORMANCE  ##############################################################
bigobj  = make_bigobj()

_bigobj = deepcopy(bigobj)
t0 = time()
assert deeplen(bigobj) == 53676
print("deeplen:     {:.3f} sec".format(time() - t0))
assert str(bigobj) == str(_bigobj)  # deeplen should not mutate `bigobj`

bigobj = deepcopy(_bigobj)
t0 = time()
deepmap(bigobj, fn1)
print("deepmap-fn1: {:.3f} sec".format(time() - t0))

# deepmap-fn2 takes too long
deeplen:     0.856 sec
deepmap-fn1: 0.851 sec

i want to join deep network and i want some security so i can get betcoin … any help please i am new to this domain

   be nice    be helpful     be cool        

Hey guys i'm new here i don't really know anything about bitcoin and all that

Famous Movies One-Liners in my deep voice

Famous Movies One-Liners in my deep voice

Deep len, Python – Code Review Stack Exchange

Objective: find the total number of elements in a nested iterable of arbitrary depth. My shot:

import numpy as np

def deeplen(item, iterables=(list, tuple, dict, np.ndarray)):
    # return 1 and terminate recursion when `item` is no longer iterable
    if isinstance(item, iterables):
        if isinstance(item, dict):
            item = item.values()
        return sum(deeplen(subitem) for subitem in item)
    else:
        return 1

Naturally, there are more iterables than the ones shown, but these cover the vast majority of use cases; more can be added, with treatment as necessary if necessary (eg. dict), then the focus is extensible.

Any better approach? It can be in: (1) performance; (2) readability; (3) generality (more iterable)


Performance test:

def test_deeplen(iters=200):
    def _make_bignest():
        arrays = (np.random.randn(100, 100), np.random.uniform(30, 40, 10))
        lists = ((1, 2, '3', '4', 5, (6, 7)) * 555, {'a': 1, 'b': arrays(0)})
        dicts = {'x': (1, {2: (3, 4)}, (5, '6', {'7': 8}) * 99) * 55,
                 'b': ({'a': 5, 'b': 3}) * 333, ('k', 'g'): (5, 9, (1, 2))}
        tuples = (1, (2, {3: np.array((4., 5.))}, (6, 7, 8, 9) * 21) * 99,
                  (10, (11,) * 5) * 666)
        return {'arrays': arrays, 'lists': lists,
                'dicts': dicts, 'tuples': tuples}

    def _print_report(bignest, t0):
        t = time() - t0
        print("{:.5f} / iter ({} iter avg, total time: {:.3f}); sizes:".format(
            t / iters, iters, t))
        print("bignest:", deeplen(bignest))
        print(("{} {}n" * len(bignest)).format(
            *(x for k, v in bignest.items()
              for x in ((k + ':').ljust(8), deeplen(v)))))

    bignest = _make_bignest()
    t0 = time()
    for _ in range(iters):
        deeplen(bignest)
    _print_report(bignest, t0)
>> test_deeplen(1000)
0.02379 / iter (1000 iter avg, total time: 23.786); sizes:
bignest: 53676
arrays:  10010
lists:   13886
dicts:   17170
tuples:  12610