Random Time Series Data Prediction with Random Neural Network Model

This code defines a function that first generates random time series data, then creates a random neural network model, trains the model, and uses it for prediction.

Technology Stack : The code uses the Keras library from the TensorFlow package to create and train a neural network model, as well as the NumPy package for generating random data.

Code Type : Function

Code Difficulty : Intermediate

                
                    
import numpy as np
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout
from tensorflow.keras.optimizers import Adam

def generate_random_sequence_data(length=100, features=10):
    # Generate random sequence data
    x = np.random.random((length, features))
    y = np.sin(x).ravel()
    return x, y

def create_random_model(input_shape=(10,), layers_count=3, neurons_per_layer=64):
    # Create a random Keras model
    model = Sequential()
    for i in range(layers_count):
        if i == 0:
            model.add(Dense(neurons_per_layer, input_shape=input_shape, activation='relu'))
        else:
            model.add(Dense(neurons_per_layer, activation='relu'))
        if np.random.rand() > 0.5:
            model.add(Dropout(0.2))
    model.add(Dense(1, activation='linear'))
    model.compile(optimizer=Adam(learning_rate=np.random.rand()), loss='mean_squared_error')
    return model

def train_model(model, x, y, epochs=50, batch_size=32):
    # Train the model
    model.fit(x, y, epochs=epochs, batch_size=batch_size)

def predict_with_random_model():
    # Generate random sequence data
    x, y = generate_random_sequence_data()
    
    # Create a random model
    model = create_random_model()
    
    # Train the model
    train_model(model, x, y)
    
    # Predict using the model
    predictions = model.predict(x)
    return predictions

# Usage
predictions = predict_with_random_model()