Perceptron as a binary classifier

raise SystemExit("Stop right there!");

An exception has occurred, use %tb to see the full traceback.

SystemExit: Stop right there!

Importing libraries and packages

# System
import os

# Mathematical operations and data manipulation
import pandas as pd

# Modelling
import tensorflow as tf

# Statistics
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score

# Plotting
import matplotlib.pyplot as plt

%matplotlib inline

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

Set paths

# Path to datasets directory
data_path = "./datasets"
# Path to assets directory (for saving results to)
assets_path = "./assets"

Loading dataset

dataset = pd.read_csv(f"{data_path}/data.csv")

dataset.head()

	label	x1	x2
0	1	2.6487	4.5192
1	1	1.5438	2.4443
2	1	1.8990	4.2409
3	1	2.4711	5.8097
4	1	3.3590	6.4423

The data has three columns. x1 and x2 are the features, and the label column contains the labels 0 or 1.

Exploring dataset

plt.scatter(
    dataset[dataset["label"] == 0]["x1"],
    dataset[dataset["label"] == 0]["x2"],
    marker="*",
)
plt.scatter(
    dataset[dataset["label"] == 1]["x1"],
    dataset[dataset["label"] == 1]["x2"],
    marker="<",
)

<matplotlib.collections.PathCollection at 0x7fe2e64dbdf0>

Training of the perceptron

# Split into features and labels. Convert values at the end into
# matrix format
x_input = dataset[["x1", "x2"]].values
y_label = dataset[["label"]].values

# Creating TensorFlow variables for features and labels and
# typecasting them to float
x = tf.Variable(x_input, dtype=tf.float32)
y = tf.Variable(y_label, dtype=tf.float32)

# Training of the perceptron
Number_of_features = 2
Number_of_units = 1
learning_rate = 0.01

# Weights and bias
weight = tf.Variable(tf.zeros([Number_of_features, Number_of_units]))
bias = tf.Variable(tf.zeros([Number_of_units]))

# Optimizer
optimizer = tf.optimizers.SGD(learning_rate)

def perceptron(xx):
    z = tf.add(tf.matmul(xx, weight), bias)
    output = tf.sigmoid(z)
    return output


def train(i):
    for n in range(i):
        loss = lambda: abs(
            tf.reduce_mean(
                tf.nn.sigmoid_cross_entropy_with_logits(
                    labels=y, logits=perceptron(x)
                )
            )
        )
        optimizer.minimize(loss, [weight, bias])


# Train the perceptron
train(1000)

Statistics

tf.print(weight)

[[-0.844034076]
 [0.673354685]]

tf.print(bias)

[0.0593947768]

# Passing the input data to check whether the perceptron
# classifies it correctly
ypred = perceptron(x)
# Rounding off the output to convert it into binary format
ypred = tf.round(ypred)

# Measuring the accuracy
acc = accuracy_score(y.numpy(), ypred.numpy())
print(acc)

1.0

# Performance measurement of the model
cnf_matrix = confusion_matrix(y.numpy(), ypred.numpy())
print(cnf_matrix)

[[12  0]
 [ 0  9]]