Integrate All Components

[Startonix]

mother_brain_simulator.py

import numpy as np import tensorflow as tf from concurrent.futures import ThreadPoolExecutor

class MotherBrainSimulator: def init(self): self.cpu = self.cpu_module self.tpu = self.tpu_module self.gpu = self.gpu_module self.tensor_product = self.tensor_product_example

def cpu_module(self, data):
    return np.sum(data)

def tpu_module(self, model, dataset, epochs=5):
    strategy = tf.distribute.TPUStrategy()
    with strategy.scope():
        model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
        model.fit(dataset, epochs=epochs)
    return model

def gpu_module(self, model, dataset, epochs=5):
    import torch
    import torch.nn as nn
    import torch.optim as optim
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters())
    for epoch in range(epochs):
        for data, target in dataset:
            data, target = data.to(device), target.to(device)
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
    return model

def tensor_product_example(self, A, B):
    return tf.tensordot(A, B, axes=1)

def run_simulation(self, data, model, dataset):
    # Run CPU simulation
    cpu_result = self.cpu(data)

    # Run TPU simulation
    tpu_trained_model = self.tpu(model, dataset)

    # Run GPU simulation
    gpu_trained_model = self.gpu(model, dataset)

    # Perform tensor product operation
    tensor_result = self.tensor_product(data, data)

    return {
        "cpu_result": cpu_result,
        "tpu_trained_model": tpu_trained_model,
        "gpu_trained_model": gpu_trained_model,
        "tensor_result": tensor_result
    }

Example usage

simulator = MotherBrainSimulator()

Example data and model

data = np.random.rand(100, 100) model = tf.keras.Sequential([ tf.keras.layers.Dense(10, activation='relu'), tf.keras.layers.Dense(10, activation='softmax