Untitled

import numpy as np
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import SGD



X, y = make_moons(n_samples=1000, noise=0.2, random_state=42)


scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)


X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)



model = Sequential()
model.add(Dense(10, input_shape=(2,), activation='tanh'))  # Changed to tanh
model.add(Dense(8, activation='tanh'))                     # Changed to tanh
model.add(Dense(1, activation='sigmoid'))




model.compile(optimizer=SGD(learning_rate=0.01),           
              loss='binary_crossentropy',
              metrics=['accuracy'])


history = model.fit(X_train, y_train, epochs=50, batch_size=16, validation_data=(X_test, y_test))


loss, accuracy = model.evaluate(X_test, y_test)
print(f"Test Accuracy: {accuracy * 100:.2f}%")