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#Steps:

#1. Set up the environment:

!pip install tensorflow

import numpy as np

#2. Define the OR and XOR gate datasets:

# Define OR and XOR datasets

X_OR = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)

y_OR = np.array([[0], [1], [1], [1]], dtype=np.float32)

X_XOR = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)

y_XOR = np.array([[0], [1], [1], [0]], dtype=np.float32)

#3. Create a single-layer perceptron model:

import tensorflow as tf

# Function to create and train a single-layer perceptron with improved

#accuracy

def train_perceptron(X, y, epochs=100, learning_rate=0.1):

 model = tf.keras.Sequential([

 tf.keras.layers.Dense(1, activation='sigmoid', input_shape=(2,))

 ])

 model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=

 learning_rate),loss='binary_crossentropy',

 metrics=['accuracy'])

 model.fit(X, y, epochs=epochs, verbose=0)

 return model

#4. Train the model on OR and XOR datasets:

# Train on OR gate with improved accuracy

model_OR = train_perceptron(X_OR, y_OR, epochs=500, learning_rate=0.5)

# Evaluate on OR

loss_OR, accuracy_OR = model_OR.evaluate(X_OR, y_OR)

print(f"OR Gate Accuracy: {accuracy_OR}")

# Train on XOR gate with improved accuracy (not possible with a single-

#layer perceptron)

# However, we can try increasing epochs and learning rate

model_XOR = train_perceptron(X_XOR, y_XOR, epochs=1000, learning_rate=0.8)

# Evaluate on XOR

loss_XOR, accuracy_XOR = model_XOR.evaluate(X_XOR, y_XOR)

print(f"XOR Gate Accuracy: {accuracy_XOR}")

#5. Analyze the results:

#Make a prediction using model_OR

input1 = 0

input2 = 0

user_input = np.array([[input1, input2]])

prediction = model_OR.predict(user_input)

if prediction > 0.5:

 print("The model predicts 1 for your input.")

else:

 print("The model predicts 0 for your input.")

#Do the same using model_XOR