I have been trying to replace the Iris Flower dataset with the Palmer Penguin dataset for a neural network tutorial. I am using the tutorial at https://www.kaggle.com/antmarakis/another-neural-network-from-scratch
The Palmer Penguin dataset should be a good replacement for the Iris Flower dataset because they both have 4 input variables and three species for the output. So I removed the rows with missing data from the penguin dataset and reduced it to 50 rows for each class to resemble the Iris Flower dataset. Unfortunately the training, validation, and testing accuracy are not very good and I cannot figure out how to improve it. I have tried removing the body mass in grams since it has far larger values than the other measurements in millimeters. I cannot get good predictions for data rows not used in the training.
Here is my modified code for the neural network:
import numpy as np
import pandas as pd
penguins = pd.read_csv("data/penguins_size_no_missing_extracted.csv")
penguins = penguins.sample(frac=1).reset_index(drop=True) # Shuffle
X = penguins[['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm']]
X = np.array(X)
print(X[:5])
from sklearn.preprocessing import OneHotEncoder
one_hot_encoder = OneHotEncoder(sparse=False)
Y = penguins.species
Y = one_hot_encoder.fit_transform(np.array(Y).reshape(-1, 1))
print(Y[:5])
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.15)
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size=0.1)
def NeuralNetwork(X_train, Y_train, X_val=None, Y_val=None, epochs=10, nodes=[], lr=0.15):
hidden_layers = len(nodes) - 1
weights = InitializeWeights(nodes)
for epoch in range(1, epochs+1):
weights = Train(X_train, Y_train, lr, weights)
if(epoch % 20 == 0):
print("Epoch {}".format(epoch))
print("Training Accuracy: {}".format(Accuracy(X_train, Y_train, weights)))
if X_val.any():
print("Validation Accuracy: {}".format(Accuracy(X_val, Y_val, weights)))
print()
return weights
def InitializeWeights(nodes):
"""Initialize weights with random values in [-1, 1] (including bias)"""
layers, weights = len(nodes), []
for i in range(1, layers):
w = [[np.random.uniform(-1, 1) for k in range(nodes[i-1] + 1)]
for j in range(nodes[i])]
weights.append(np.matrix(w))
return weights
def ForwardPropagation(x, weights, layers):
activations, layer_input = [x], x
for j in range(layers):
activation = Sigmoid(np.dot(layer_input, weights[j].T))
activations.append(activation)
layer_input = np.append(1, activation) # Augment with bias
return activations
def BackPropagation(y, activations, weights, layers):
outputFinal = activations[-1]
error = np.matrix(y - outputFinal) # Error at output
for j in range(layers, 0, -1):
currActivation = activations[j]
if(j > 1):
# Augment previous activation
prevActivation = np.append(1, activations[j-1])
else:
# First hidden layer, prevActivation is input (without bias)
prevActivation = activations[0]
delta = np.multiply(error, SigmoidDerivative(currActivation))
weights[j-1] += lr * np.multiply(delta.T, prevActivation)
w = np.delete(weights[j-1], [0], axis=1) # Remove bias from weights
error = np.dot(delta, w) # Calculate error for current layer
return weights
def Train(X, Y, lr, weights):
layers = len(weights)
for i in range(len(X)):
x, y = X[i], Y[i]
x = np.matrix(np.append(1, x)) # Augment feature vector
activations = ForwardPropagation(x, weights, layers)
weights = BackPropagation(y, activations, weights, layers)
return weights
def Sigmoid(x):
return 1 / (1 + np.exp(-x))
def SigmoidDerivative(x):
return np.multiply(x, 1-x)
def Predict(item, weights):
layers = len(weights)
item = np.append(1, item) # Augment feature vector
##_Forward Propagation_##
activations = ForwardPropagation(item, weights, layers)
outputFinal = activations[-1].A1
index = FindMaxActivation(outputFinal)
# Initialize prediction vector to zeros
y = [0 for i in range(len(outputFinal))]
y[index] = 1 # Set guessed class to 1
return y # Return prediction vector
def FindMaxActivation(output):
"""Find max activation in output"""
m, index = output[0], 0
for i in range(1, len(output)):
if(output[i] > m):
m, index = output[i], i
return index
def Accuracy(X, Y, weights):
"""Run set through network, find overall accuracy"""
correct = 0
for i in range(len(X)):
x, y = X[i], list(Y[i])
guess = Predict(x, weights)
if(y == guess):
# Guessed correctly
correct += 1
return correct / len(X)
f = len(X[0]) # Number of features
o = len(Y[0]) # Number of outputs / classes
layers = [f, 4, 8, o] # Number of nodes in layers
lr, epochs = 0.15, 100
weights = NeuralNetwork(X_train, Y_train, X_val, Y_val, epochs=epochs, nodes=layers, lr=lr);
print("Testing Accuracy: {}".format(Accuracy(X_test, Y_test, weights)))
print()
# Make predictions
x = [41.5,18.5,201]
guess = Predict(x, weights)
print("Prediction: ", end='')
print(guess)
x = [50.2,18.7,198]
guess = Predict(x, weights)
print("Prediction: ", end='')
print(guess)
x = [49.9,16.1,213]
guess = Predict(x, weights)
print("Prediction: ", end='')
print(guess)
The output is:
[[ 59.6 17. 230. ]
[ 49. 19.5 210. ]
[ 45.3 13.7 210. ]
[ 43.2 16.6 187. ]
[ 45.2 15.8 215. ]]
[[0. 0. 1.]
[0. 1. 0.]
[0. 0. 1.]
[0. 1. 0.]
[0. 0. 1.]]
Epoch 20
Training Accuracy: 0.3508771929824561
Validation Accuracy: 0.38461538461538464
Epoch 40
Training Accuracy: 0.3508771929824561
Validation Accuracy: 0.38461538461538464
Epoch 60
Training Accuracy: 0.3508771929824561
Validation Accuracy: 0.38461538461538464
Epoch 80
Training Accuracy: 0.32456140350877194
Validation Accuracy: 0.23076923076923078
Epoch 100
Training Accuracy: 0.32456140350877194
Validation Accuracy: 0.23076923076923078
Testing Accuracy: 0.43478260869565216
Prediction: [0, 1, 0]
Prediction: [0, 1, 0]
Prediction: [0, 1, 0]
The CSV file has the following data:
species,island,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g,sex
Adelie,Torgersen,39.5,17.4,186,3800,FEMALE
Adelie,Torgersen,40.3,18,195,3250,FEMALE
Adelie,Torgersen,36.7,19.3,193,3450,FEMALE
Adelie,Torgersen,39.3,20.6,190,3650,MALE
Adelie,Torgersen,38.9,17.8,181,3625,FEMALE
Adelie,Torgersen,39.2,19.6,195,4675,MALE
Adelie,Torgersen,41.1,17.6,182,3200,FEMALE
Adelie,Torgersen,38.6,21.2,191,3800,MALE
Adelie,Torgersen,34.6,21.1,198,4400,MALE
Adelie,Torgersen,36.6,17.8,185,3700,FEMALE
Adelie,Torgersen,38.7,19,195,3450,FEMALE
Adelie,Torgersen,42.5,20.7,197,4500,MALE
Adelie,Torgersen,34.4,18.4,184,3325,FEMALE
Adelie,Torgersen,46,21.5,194,4200,MALE
Adelie,Biscoe,37.8,18.3,174,3400,FEMALE
Adelie,Biscoe,37.7,18.7,180,3600,MALE
Adelie,Biscoe,35.9,19.2,189,3800,FEMALE
Adelie,Biscoe,38.2,18.1,185,3950,MALE
Adelie,Biscoe,38.8,17.2,180,3800,MALE
Adelie,Biscoe,35.3,18.9,187,3800,FEMALE
Adelie,Biscoe,40.6,18.6,183,3550,MALE
Adelie,Biscoe,40.5,17.9,187,3200,FEMALE
Adelie,Biscoe,37.9,18.6,172,3150,FEMALE
Adelie,Biscoe,40.5,18.9,180,3950,MALE
Adelie,Dream,39.5,16.7,178,3250,FEMALE
Adelie,Dream,37.2,18.1,178,3900,MALE
Adelie,Dream,39.5,17.8,188,3300,FEMALE
Adelie,Dream,40.9,18.9,184,3900,MALE
Adelie,Dream,36.4,17,195,3325,FEMALE
Adelie,Dream,39.2,21.1,196,4150,MALE
Adelie,Dream,38.8,20,190,3950,MALE
Adelie,Dream,42.2,18.5,180,3550,FEMALE
Adelie,Dream,37.6,19.3,181,3300,FEMALE
Adelie,Dream,39.8,19.1,184,4650,MALE
Adelie,Dream,36.5,18,182,3150,FEMALE
Adelie,Dream,40.8,18.4,195,3900,MALE
Adelie,Dream,36,18.5,186,3100,FEMALE
Adelie,Dream,44.1,19.7,196,4400,MALE
Adelie,Dream,37,16.9,185,3000,FEMALE
Adelie,Dream,39.6,18.8,190,4600,MALE
Adelie,Dream,41.1,19,182,3425,MALE
Adelie,Dream,36,17.9,190,3450,FEMALE
Adelie,Dream,42.3,21.2,191,4150,MALE
Adelie,Biscoe,39.6,17.7,186,3500,FEMALE
Adelie,Biscoe,40.1,18.9,188,4300,MALE
Adelie,Biscoe,35,17.9,190,3450,FEMALE
Adelie,Biscoe,42,19.5,200,4050,MALE
Adelie,Biscoe,34.5,18.1,187,2900,FEMALE
Adelie,Biscoe,41.4,18.6,191,3700,MALE
Adelie,Biscoe,39,17.5,186,3550,FEMALE
Chinstrap,Dream,50,19.5,196,3900,MALE
Chinstrap,Dream,51.3,19.2,193,3650,MALE
Chinstrap,Dream,45.4,18.7,188,3525,FEMALE
Chinstrap,Dream,52.7,19.8,197,3725,MALE
Chinstrap,Dream,45.2,17.8,198,3950,FEMALE
Chinstrap,Dream,46.1,18.2,178,3250,FEMALE
Chinstrap,Dream,51.3,18.2,197,3750,MALE
Chinstrap,Dream,46,18.9,195,4150,FEMALE
Chinstrap,Dream,51.3,19.9,198,3700,MALE
Chinstrap,Dream,46.6,17.8,193,3800,FEMALE
Chinstrap,Dream,51.7,20.3,194,3775,MALE
Chinstrap,Dream,47,17.3,185,3700,FEMALE
Chinstrap,Dream,52,18.1,201,4050,MALE
Chinstrap,Dream,45.9,17.1,190,3575,FEMALE
Chinstrap,Dream,50.5,19.6,201,4050,MALE
Chinstrap,Dream,50.3,20,197,3300,MALE
Chinstrap,Dream,58,17.8,181,3700,FEMALE
Chinstrap,Dream,46.4,18.6,190,3450,FEMALE
Chinstrap,Dream,49.2,18.2,195,4400,MALE
Chinstrap,Dream,42.4,17.3,181,3600,FEMALE
Chinstrap,Dream,48.5,17.5,191,3400,MALE
Chinstrap,Dream,43.2,16.6,187,2900,FEMALE
Chinstrap,Dream,50.6,19.4,193,3800,MALE
Chinstrap,Dream,46.7,17.9,195,3300,FEMALE
Chinstrap,Dream,52,19,197,4150,MALE
Chinstrap,Dream,50.5,18.4,200,3400,FEMALE
Chinstrap,Dream,49.5,19,200,3800,MALE
Chinstrap,Dream,46.4,17.8,191,3700,FEMALE
Chinstrap,Dream,52.8,20,205,4550,MALE
Chinstrap,Dream,40.9,16.6,187,3200,FEMALE
Chinstrap,Dream,54.2,20.8,201,4300,MALE
Chinstrap,Dream,42.5,16.7,187,3350,FEMALE
Chinstrap,Dream,51,18.8,203,4100,MALE
Chinstrap,Dream,49.7,18.6,195,3600,MALE
Chinstrap,Dream,47.5,16.8,199,3900,FEMALE
Chinstrap,Dream,47.6,18.3,195,3850,FEMALE
Chinstrap,Dream,52,20.7,210,4800,MALE
Chinstrap,Dream,46.9,16.6,192,2700,FEMALE
Chinstrap,Dream,53.5,19.9,205,4500,MALE
Chinstrap,Dream,49,19.5,210,3950,MALE
Chinstrap,Dream,46.2,17.5,187,3650,FEMALE
Chinstrap,Dream,50.9,19.1,196,3550,MALE
Chinstrap,Dream,45.5,17,196,3500,FEMALE
Chinstrap,Dream,50.9,17.9,196,3675,FEMALE
Chinstrap,Dream,50.8,18.5,201,4450,MALE
Chinstrap,Dream,50.1,17.9,190,3400,FEMALE
Chinstrap,Dream,49,19.6,212,4300,MALE
Chinstrap,Dream,51.5,18.7,187,3250,MALE
Chinstrap,Dream,49.8,17.3,198,3675,FEMALE
Chinstrap,Dream,48.1,16.4,199,3325,FEMALE
Gentoo,Biscoe,50,16.3,230,5700,MALE
Gentoo,Biscoe,48.7,14.1,210,4450,FEMALE
Gentoo,Biscoe,50,15.2,218,5700,MALE
Gentoo,Biscoe,47.6,14.5,215,5400,MALE
Gentoo,Biscoe,46.5,13.5,210,4550,FEMALE
Gentoo,Biscoe,45.4,14.6,211,4800,FEMALE
Gentoo,Biscoe,46.7,15.3,219,5200,MALE
Gentoo,Biscoe,43.3,13.4,209,4400,FEMALE
Gentoo,Biscoe,46.8,15.4,215,5150,MALE
Gentoo,Biscoe,40.9,13.7,214,4650,FEMALE
Gentoo,Biscoe,49,16.1,216,5550,MALE
Gentoo,Biscoe,45.5,13.7,214,4650,FEMALE
Gentoo,Biscoe,48.4,14.6,213,5850,MALE
Gentoo,Biscoe,45.8,14.6,210,4200,FEMALE
Gentoo,Biscoe,49.3,15.7,217,5850,MALE
Gentoo,Biscoe,42,13.5,210,4150,FEMALE
Gentoo,Biscoe,49.2,15.2,221,6300,MALE
Gentoo,Biscoe,46.2,14.5,209,4800,FEMALE
Gentoo,Biscoe,48.7,15.1,222,5350,MALE
Gentoo,Biscoe,50.2,14.3,218,5700,MALE
Gentoo,Biscoe,45.1,14.5,215,5000,FEMALE
Gentoo,Biscoe,46.5,14.5,213,4400,FEMALE
Gentoo,Biscoe,46.3,15.8,215,5050,MALE
Gentoo,Biscoe,42.9,13.1,215,5000,FEMALE
Gentoo,Biscoe,46.1,15.1,215,5100,MALE
Gentoo,Biscoe,47.8,15,215,5650,MALE
Gentoo,Biscoe,48.2,14.3,210,4600,FEMALE
Gentoo,Biscoe,50,15.3,220,5550,MALE
Gentoo,Biscoe,47.3,15.3,222,5250,MALE
Gentoo,Biscoe,42.8,14.2,209,4700,FEMALE
Gentoo,Biscoe,45.1,14.5,207,5050,FEMALE
Gentoo,Biscoe,59.6,17,230,6050,MALE
Gentoo,Biscoe,49.1,14.8,220,5150,FEMALE
Gentoo,Biscoe,48.4,16.3,220,5400,MALE
Gentoo,Biscoe,42.6,13.7,213,4950,FEMALE
Gentoo,Biscoe,44.4,17.3,219,5250,MALE
Gentoo,Biscoe,44,13.6,208,4350,FEMALE
Gentoo,Biscoe,48.7,15.7,208,5350,MALE
Gentoo,Biscoe,42.7,13.7,208,3950,FEMALE
Gentoo,Biscoe,49.6,16,225,5700,MALE
Gentoo,Biscoe,45.3,13.7,210,4300,FEMALE
Gentoo,Biscoe,49.6,15,216,4750,MALE
Gentoo,Biscoe,50.5,15.9,222,5550,MALE
Gentoo,Biscoe,43.6,13.9,217,4900,FEMALE
Gentoo,Biscoe,45.5,13.9,210,4200,FEMALE
Gentoo,Biscoe,50.5,15.9,225,5400,MALE
Gentoo,Biscoe,44.9,13.3,213,5100,FEMALE
Gentoo,Biscoe,45.2,15.8,215,5300,MALE
Gentoo,Biscoe,46.6,14.2,210,4850,FEMALE
Gentoo,Biscoe,48.5,14.1,220,5300,MALE
