decision_boundary_linear_data.py

# Imports
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import make_blobs
from mlxtend.plotting import plot_decision_regions

# Configuration options
num_samples_total = 1000
training_split = 250

# Generate data
X, targets = make_blobs(n_samples = num_samples_total, centers = [(0,0), (15,15)], n_features = 2, center_box=(0, 1), cluster_std = 2.5)
targets[np.where(targets == 0)] = -1
X_training = X[training_split:, :]
X_testing = X[:training_split, :]
Targets_training = targets[training_split:]
Targets_testing = targets[:training_split]

# Generate scatter plot for training data
plt.scatter(X_training[:,0], X_training[:,1])
plt.title('Linearly separable data')
plt.xlabel('X1')
plt.ylabel('X2')
plt.show()

# Set the input shape
feature_vector_shape = len(X_training[0])
input_shape = (feature_vector_shape,)
print(f'Feature shape: {input_shape}')

# Create the model
model = Sequential()
model.add(Dense(50, input_shape=input_shape, activation='relu', kernel_initializer='he_uniform'))
model.add(Dense(1, activation='tanh'))

# Configure the model and start training
model.compile(loss='squared_hinge', optimizer='adam', metrics=['accuracy'])
model.fit(X_training, Targets_training, epochs=50, batch_size=25, verbose=1, validation_split=0.2)

# Test the model after training
test_results = model.evaluate(X_testing, Targets_testing, verbose=1)
print(f'Test results - Loss: {test_results[0]} - Accuracy: {test_results[1]*100}%')

# Plot decision boundary
plot_decision_regions(X_testing, Targets_testing, clf=model, legend=2)
plt.show()