def fibonacci_sequence(n):
    if n <= 0:
        return []
    elif n == 1:
        return [0]
    elif n == 2:
        return [0, 1]
    else:
        sequence = [0, 1]
        while len(sequence) < n:
            next_number = sequence[-1] + sequence[-2]
            sequence.append(next_number)
        return sequence

import transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification

def perform_sentiment_analysis(text):
    # Load pre-trained model and tokenizer
    model_name = "bert-base-uncased"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(model_name)

    # Tokenize text and prepare input
    inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")

    # Perform inference
    outputs = model(**inputs)

    # Get predicted sentiment
    predicted_sentiment = torch.argmax(outputs.logits).item()

    # Map sentiment index to label
    sentiment_labels = ["Negative", "Neutral", "Positive"]
    predicted_label = sentiment_labels[predicted_sentiment]

    return predicted_label

import timeit
from datetime import datetime

def create_odd_list(numbers):
    start_time = datetime.now()
    odd_list = []

    for num in numbers:
        if num % 2 != 0:
            odd_list.append(num)

    end_time = datetime.now()
    execution_time = end_time - start_time

    return odd_list, execution_time

# Example usage
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
odd_numbers, time_taken = create_odd_list(numbers)
print("Odd numbers:", odd_numbers)
print("Execution time:", time_taken)

import timeit
from datetime import datetime

def create_odd_list(numbers):
    start_time = datetime.now()
    odd_list = [num for num in numbers if num % 2 != 0]
    end_time = datetime.now()
    execution_time = end_time - start_time

    return odd_list, execution_time

# Example usage
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
odd_numbers, time_taken = create_odd_list(numbers)
print("Odd numbers:", odd_numbers)
print("Execution time:", time_taken)

Invariant Violation: Native module cannot be null.

The error message 'Invariant Violation: Native module cannot be null' typically occurs when there is an issue with the integration of the `react-native-reanimated` module in your React Native app. This error suggests that the native module required for `react-native-reanimated` is not being properly linked or initialized. To resolve this error, you can try the following steps:

1. Ensure that you have installed `react-native-reanimated` correctly by running the necessary installation commands specified in the documentation.

2. Make sure that you have linked the `react-native-reanimated` library to your project. You can use the `react-native link` command or follow the manual linking instructions provided in the `react-native-reanimated` documentation.

3. Verify that the necessary dependencies and configurations for `react-native-reanimated` are properly set up in your project. This may involve checking the versions of React Native, Gradle, or CocoaPods, depending on your platform.

4. If you have recently upgraded React Native or made changes to your project, try cleaning the build artifacts and recompiling the app. This can be done by running commands like `npm run clean` or `cd ios && pod install` (for iOS) and `cd android && gradlew clean` (for Android).

By following these steps and ensuring the proper integration and configuration of `react-native-reanimated`, you should be able to resolve the 'Invariant Violation: Native module cannot be null' error in your React Native app.

Certainly! Let's take the example of a CNN trained to classify images of cats and dogs. With model interpretability, we aim to understand how the CNN makes its predictions.

To achieve this, we can use techniques like visualization of intermediate feature maps. These maps show which regions of the input image activate specific filters in the CNN. By observing these activated regions, we can gain insights into the important features the CNN focuses on for classification.

Another method is occlusion analysis, where we systematically mask different parts of the input image and observe the impact on the CNN's prediction. This helps identify which areas of the image contribute the most to the classification decision.

Additionally, techniques like saliency maps or gradient-based methods can highlight the most influential pixels or regions in the image for the CNN's prediction. These methods provide a visual representation of the model's attention on different parts of the input.

By utilizing these interpretability techniques, we can gain a better understanding of how the CNN processes and analyzes images, and which image features are crucial for its predictions. This knowledge is valuable for verifying model behavior, identifying biases, and building trust in the model's decision-making process.

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Load the MNIST dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Preprocess the data
x_train = x_train.reshape(-1, 28*28) / 255.0
x_test = x_test.reshape(-1, 28*28) / 255.0
y_train = keras.utils.to_categorical(y_train)
y_test = keras.utils.to_categorical(y_test)

# Define the model architecture
model = keras.Sequential([
 layers.Dense(256, activation='relu', input_shape=(28*28,)),
 layers.Dense(128, activation='relu'),
 layers.Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(x_train, y_train, validation_split=0.2, epochs=10, batch_size=128)

# Evaluate the model
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)
print('Test accuracy:', test_acc)

% Load the MNIST dataset
[x_train, y_train, x_test, y_test] = mnist_helper();

% Preprocess the data
x_train = reshape(x_train, [], 784) / 255.0;
x_test = reshape(x_test, [], 784) / 255.0;
y_train = categorical(y_train);
y_test = categorical(y_test);

% Define the model architecture
model = [
    fullyConnectedLayer(256)
    reluLayer
    fullyConnectedLayer(128)
    reluLayer
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer
];

% Compile the model
options = trainingOptions('adam', ...
    'Loss', 'categoricalcrossentropy', ...
    'Metrics', 'accuracy');

% Train the model
model = trainNetwork(x_train, y_train, model, options);

% Evaluate the model
y_pred = classify(model, x_test);
test_acc = sum(y_pred == y_test) / numel(y_test);
fprintf('Test accuracy: %.2f%%\n', test_acc * 100);

import * as tf from '@tensorflow/tfjs';

// Load the MNIST dataset
const { xs: xTrain, ys: yTrain, xs: xTest, ys: yTest } = await tf.data
  .mnist('file://path/to/mnist-data')
  .batch(32)
  .toArray();

// Preprocess the data
const xTrain = xTrain.reshape([-1, 28 * 28]).div(255);
const xTest = xTest.reshape([-1, 28 * 28]).div(255);
const yTrain = tf.oneHot(yTrain, 10);
const yTest = tf.oneHot(yTest, 10);

// Define the model architecture
const model = tf.sequential();
model.add(tf.layers.dense({ units: 256, activation: 'relu', inputShape: [28 * 28] }));
model.add(tf.layers.dense({ units: 128, activation: 'relu' }));
model.add(tf.layers.dense({ units: 10, activation: 'softmax' }));

// Compile the model
model.compile({ optimizer: 'adam', loss: 'categoricalCrossentropy', metrics: ['accuracy'] });

// Train the model
await model.fit(xTrain, yTrain, {
  validationSplit: 0.2,
  epochs: 10,
  batchSize: 128,
});

// Evaluate the model
const { loss, accuracy } = model.evaluate(xTest, yTest);
console.log('Test loss:', loss);
console.log('Test accuracy:', accuracy);