SandroLuck

import torch.nn.functional as F
from pytorch_lightning import seed_everything, LightningModule, Trainer
from pytorch_lightning.callbacks import EarlyStopping
from torch import nn, optim, rand, sum as tsum, reshape, save
from torch.utils.data import DataLoader, Dataset

SAMPLE_DIM = 21000

class CustomDataset(Dataset):
    def __init__(self, samples=42):

Hironsan

ノートブックのリンク

• Chapter04 前処理
• Chapter05 特徴エンジニアリング
• Chapter06 機械学習モデル
• Chapter07 ニューラルネットワークの基礎
• Chapter08 単語分散表現
• Chapter09 テキスト分類
• Chapter10 系列ラベリング
• Chapter11 系列変換

dvgodoy

torch.manual_seed(42)

x_tensor = torch.from_numpy(x).float()
y_tensor = torch.from_numpy(y).float()

# Builds dataset with ALL data
dataset = TensorDataset(x_tensor, y_tensor)
# Splits randomly into train and validation datasets
train_dataset, val_dataset = random_split(dataset, [80, 20])

thomaswilley

# Get Apple Health data as Pandas DataFrame
# ===
# pre-reqs: python3, lxml, pandas
# to get started:
#   export and mail yourself your data following steps within the Health app on iPhone
#   download and unzip contents of exported zip file; find path to export.xml and set path_to_exportxml below

import pandas as pd
import xml.etree.ElementTree
import datetime