conormm

# pytorch embeddings

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

import pandas as pd
import numpy as np

conormm

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from torch.autograd import Variable
import torch.nn.functional as F
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import pandas as pd

conormm

library(broom)
library(tidyverse)
library(gridExtra)

# imagine we run a marketing campaign and 1265 (n) people of 6000 (m) people sign up
# to the service
n <- 1265
m <- 6000

# You want to estimate, given this data, what is the probability (p) that

conormm

df %>% 
  ggplot(aes(probs, logloss, colour = label)) + 
  geom_line(size = .8) + 
  labs(
    title = "Log Loss over estimated probability", 
    x = "Estimated probabilty",
    y = "Log Loss", 
    colour = "Label"
  ) + 
  annotate("text", label = "Log Loss penalises overly confident, 

conormm

library(tidyverse)

# create range of probabilities
probs <- seq(0, 1, 0.001)

# apply log loss over full range of propabilites
t1 <- logloss(1, probs)
t0 <- logloss(0, probs)

# tidy the data

conormm

logloss <- function(label, p) {
  if (label == 1) {
    x <- -log(p) 
  } else {
    x <- -log(1 - p)
  }
  x
}

conormm

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.functional as F
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import SubsetRandomSampler

import numpy as np
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler

conormm

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.functional as F
from torch.utils.data import Dataset, DataLoader

import numpy as np
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler

conormm

library(modelr)
library(dplyr)
library(ggplot2)
library(animation)
library(gganimate)

options(scipen = 999)

n     <- 200 # number of observations
bias  <- 4

conormm

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.utils import np_utils
import numpy as np

l1_nodes = 200
l2_nodes = 100
final_layer_nodes = 10