Using Keras (now part of TensorFlow) is really easy. The complexity comes when you deal with large amounts of data figuring out the topology of a neural network. With the topology comes hyperparameter tuning and all that. It's a bit like painting: it's easy to hold a brush but it takes years to paint something worth looking at.
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from mxnet import gluon
import mxnet as mx
import numpy as np
x_input = mx.nd.empty((1, 5), mx.cpu())
x_input[:] = np.array([[1,2,3,4,5]], np.float32)
y_input = mx.nd.empty((1, 5), mx.cpu())
y_input[:] = np.array([[10, 15, 20, 22.5, 25]], np.float32)
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| from keras.layers.core import Dense | |
| from keras.models import Sequential | |
| from numpy import array | |
| import numpy as np | |
| import os | |
| import argparse | |
| from scipy import signal | |
| N = 10 |
| library(tfestimators) | |
| response <- function() "Species" | |
| features <- function() setdiff(names(iris), response()) | |
| # split into train, test datasets | |
| set.seed(123) | |
| partitions <- modelr::resample_partition(iris, c(test = 0.2, train = 0.8)) | |
| iris_train <- as.data.frame(partitions$train) | |
| iris_test <- as.data.frame(partitions$test) |
| import pyspark | |
| from pyspark import SparkContext, SparkConf, SQLContext | |
| from pyspark.sql import SparkSession | |
| conf = SparkConf().setMaster("local") | |
| sc = SparkContext(conf=conf) | |
| spark = SparkSession.builder.appName('Noether').getOrCreate() | |
| sc.addPyFile("~/Downloads/graphframes-0.5.0-spark2.1-s_2.11.jar") |
| import hashlib as hasher | |
| import datetime as date | |
| import requests | |
| import time | |
| import math | |
| import uuid | |
| import random | |