Greetings, AI enthusiasts and students seeking artificial intelligence assignment help! Today, we delve into the intricacies of AI through a couple of master-level questions, accompanied by comprehensive solutions. Whether you're a novice or a seasoned practitioner, these exercises will surely enrich your understanding and sharpen your skills in the fascinating realm of artificial intelligence.
Question 1:
Consider a scenario where you are tasked with implementing a sentiment analysis system using a recurrent neural network (RNN). You have a dataset consisting of user reviews, each labeled with a sentiment (positive, negative, or neutral). Develop a Python code snippet using TensorFlow/Keras to construct and train an RNN model for sentiment analysis. Ensure to preprocess the text data appropriately, define the architecture of the RNN model, compile it with an appropriate loss function and optimizer, and train it on the dataset. Finally, evaluate the model's performance on a separate test dataset and report the accuracy.
Solution:
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense
# Assuming 'reviews' is a list containing the text of user reviews and 'labels' is a list containing the corresponding sentiment labels
# Preprocessing text data
tokenizer = Tokenizer()
tokenizer.fit_on_texts(reviews)
sequences = tokenizer.texts_to_sequences(reviews)
maxlen = 100
data = pad_sequences(sequences, maxlen=maxlen)
labels = np.asarray(labels)
# Splitting data into training and testing sets
training_samples = 10000
validation_samples = len(data) - training_samples
x_train = data[:training_samples]
y_train = labels[:training_samples]
x_val = data[training_samples: training_samples + validation_samples]
y_val = labels[training_samples: training_samples + validation_samples]
# Defining the RNN model
model = Sequential()
model.add(Embedding(max_words, embedding_dim, input_length=maxlen))
model.add(LSTM(64))
model.add(Dense(3, activation='softmax'))
# Compiling the model
model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Training the model
history = model.fit(x_train, y_train, epochs=10, batch_size=128, validation_data=(x_val, y_val))
# Evaluating the model
test_loss, test_acc = model.evaluate(x_test, y_test)
print('Test accuracy:', test_acc)
Question 2:
You are tasked with implementing a genetic algorithm to optimize the parameters of a neural network for image classification. Develop a Python code snippet using a genetic algorithm to evolve the weights and biases of a simple neural network with one hidden layer for classifying images from the CIFAR-10 dataset. Ensure to define appropriate mutation and crossover operations, as well as selection mechanisms. Evaluate the performance of the evolved neural network on a separate validation dataset.
Solution:
import numpy as np
import random
# Assuming 'cifar_images' is a dataset containing images and their corresponding labels
# Define fitness function
def fitness_function(parameters):
# Initialize neural network with parameters
# Train neural network on CIFAR-10 dataset
# Calculate accuracy on validation dataset
return accuracy_on_validation_dataset
# Define genetic algorithm parameters
population_size = 100
mutation_rate = 0.1
num_generations = 20
# Initialize population
population = [initialize_parameters() for _ in range(population_size)]
# Main loop
for generation in range(num_generations):
# Evaluate fitness of each individual in the population
fitness_scores = [fitness_function(parameters) for parameters in population]
# Select parents for reproduction (tournament selection)
selected_parents = tournament_selection(population, fitness_scores)
# Create offspring via crossover and mutation
offspring = []
while len(offspring) population_size:
parent1, parent2 = random.sample(selected_parents, 2)
child = crossover(parent1, parent2)
child = mutate(child, mutation_rate)
offspring.append(child)
# Replace old population with offspring
population = offspring
# Select best individual from final population
best_parameters = max(population, key=fitness_function)
# Evaluate performance of best individual on validation dataset
best_accuracy = fitness_function(best_parameters)
print("Best accuracy on validation dataset:", best_accuracy)
These solutions provide a practical insight into implementing artificial intelligence algorithms, enriching your learning experience and preparing you for real-world applications. Stay tuned for more challenging exercises and expert insights. Happy coding!
