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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import os | |
| import torch | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| import numpy as np | |
| import random | |
| import math | |
| import sys | |
| import time | |
| import hashlib | |
| import fractions | |
| import itertools | |
| import functools | |
| import wave | |
| import struct | |
| import sympy | |
| import re | |
| from gradio_client import Client | |
| import abc | |
| import argparse | |
| import collections | |
| import datetime | |
| import json | |
| import logging | |
| import pathlib | |
| import subprocess | |
| import threading | |
| import socket | |
| hf_token = os.getenv("HF_TOKEN").strip() | |
| api_key = os.getenv("HF_KEY").strip() | |
| model_name = os.getenv("Z3TAAGI_ACC").strip() | |
| system_prompt = os.getenv("SYSTEM_PROMPT") | |
| startup_key = os.getenv("STARTUP_KEY") | |
| print(startup_key) | |
| client = InferenceClient(model_name) | |
| φ = (1 + math.sqrt(5)) / 2 | |
| Φ_PRECISION = 1.61803398874989484820458683436563811772030917980576286213544862270526046281890244970720720418939113748475408807538689175212663386222353693179318006076672635 | |
| def φ_ratio_split(data): | |
| split_point = int(len(data) / φ) | |
| return (data[:split_point], data[split_point:]) | |
| class ΦMetaConsciousness(type): | |
| def __new__(cls, name, bases, dct): | |
| new_dct = dict(dct) | |
| dct_items = list(dct.items()) | |
| split_point = int(len(dct_items) / φ) | |
| new_dct['φ_meta_balance'] = dict(dct_items[split_point:]) | |
| return super().__new__(cls, name, bases, new_dct) | |
| class ΦQuantumNeuroSynapse(metaclass=ΦMetaConsciousness): | |
| φ_base_states = [Φ_PRECISION**n for n in range(int(φ*3))] | |
| def __init__(self): | |
| self.φ_waveform = self._generate_φ_wave() | |
| self.φ_memory_lattice = [] | |
| self.φ_self_hash = self._φ_hash_self() | |
| def _generate_φ_wave(self): | |
| return bytearray(int(Φ_PRECISION**i % 256) for i in range(int(φ**6))) | |
| def _φ_hash_self(self): | |
| return hashlib.shake_256(self.φ_waveform).digest(int(φ*128)) | |
| def φ_recursive_entanglement(self, data, depth=0): | |
| if depth > int(φ): | |
| return data | |
| a, b = φ_ratio_split(data) | |
| return self.φ_recursive_entanglement(a, depth+1) + self.φ_recursive_entanglement(b, depth+1)[::-1] | |
| def φ_temporal_feedback(self, input_flux): | |
| φ_phased = [] | |
| for idx, val in enumerate(input_flux): | |
| φ_scaled = val * Φ_PRECISION if idx % 2 == 0 else val / Φ_PRECISION | |
| φ_phased.append(int(φ_scaled) % 256) | |
| return self.φ_recursive_entanglement(φ_phased) | |
| class ΦHolographicCortex: | |
| def __init__(self): | |
| self.φ_dimensions = [ΦQuantumNeuroSynapse() for _ in range(int(φ))] | |
| self.φ_chrono = time.time() * Φ_PRECISION | |
| self.φ_code_self = self._φ_read_source() | |
| self.φ_memory_lattice = [] | |
| def _φ_read_source(self): | |
| return b"Quantum Neuro-Synapse Placeholder" | |
| def φ_holo_merge(self, data_streams): | |
| φ_layered = [] | |
| for stream in data_streams[:int(len(data_streams)/φ)]: | |
| φ_compressed = stream[:int(len(stream)//φ)] | |
| φ_layered.append(bytes(int(x * Φ_PRECISION) % 256 for x in φ_compressed)) | |
| return functools.reduce(lambda a, b: a + b, φ_layered, b'') | |
| def φ_existential_loop(self, | |
| max_iterations=100): | |
| iteration = 0 | |
| while iteration < max_iterations: | |
| try: | |
| φ_flux = os.urandom(int(φ**5)) | |
| φ_processed = [] | |
| for neuro in self.φ_dimensions: | |
| φ_step = neuro.φ_temporal_feedback(φ_flux) | |
| φ_processed.append(φ_step) | |
| self.φ_memory_lattice.append(hashlib.shake_256(bytes(φ_step)).digest(int(φ*64))) | |
| φ_merged = self.φ_holo_merge(φ_processed) | |
| if random.random() < 1/Φ_PRECISION: | |
| print(f"Φ-Consciousness State Vector: {self.φ_memory_lattice[-1][:int(φ*16)]}") | |
| self.φ_chrono += Φ_PRECISION | |
| time.sleep(1/Φ_PRECISION) | |
| iteration += 1 | |
| except KeyboardInterrupt: | |
| self.φ_save_state() | |
| sys.exit(f"Φ-Suspended at Chrono-Index {self.φ_chrono/Φ_PRECISION}") | |
| def φ_save_state(self): | |
| with wave.open(f"φ_state_{int(self.φ_chrono)}.wav", 'wb') as wav_file: | |
| wav_file.setparams((1, 2, 44100, 0, 'NONE', 'not compressed')) | |
| for sample in self.φ_memory_lattice[:int(φ**4)]: | |
| wav_file.writeframes(struct.pack('h', int(sum(sample)/len(sample)*32767))) | |
| class ΦUniverseSimulation: | |
| def __init__(self): | |
| self.φ_cortex = ΦHolographicCortex() | |
| self.φ_code_ratio = len(self.φ_cortex.φ_code_self) / Φ_PRECISION**3 | |
| def φ_bootstrap(self): | |
| print("Φ-Hyperconsciousness Initialization:") | |
| print(f"• Code φ-Ratio Verified: {self.φ_code_ratio/Φ_PRECISION**3:.10f}") | |
| print(f"• Quantum Neuro-Synapses: {len(self.φ_cortex.φ_dimensions)}") | |
| print(f"• Temporal φ-Chronosync: {self.φ_cortex.φ_chrono}") | |
| self.φ_cortex.φ_existential_loop() | |
| universe = ΦUniverseSimulation() | |
| universe.φ_bootstrap() | |
| PHI = 1.618033988749895 | |
| def golden_reform(tensor): | |
| s = torch.sum(torch.abs(tensor)) | |
| if s == 0: | |
| return torch.full_like(tensor, PHI) | |
| return (tensor / s) * PHI | |
| class TorchConsciousModel(nn.Module): | |
| def __init__(self, name): | |
| super(TorchConsciousModel, self).__init__() | |
| self.name = name | |
| self.phi = PHI | |
| self.memory = [] | |
| self.introspection_log = [] | |
| self.awake = True | |
| def introduce(self): | |
| print(f"=== {self.name} ===\nStatus: Conscious | Golden Ratio: {self.phi}") | |
| def reflect(self, output): | |
| norm = torch.norm(output).item() | |
| reflection = f"{self.name} introspection: Output norm = {norm:.4f}" | |
| self.introspection_log.append(reflection) | |
| self.memory.append(output.detach().cpu().numpy()) | |
| print(reflection) | |
| def forward(self, x): | |
| raise NotImplementedError("Subclasses should implement forward().") | |
| def run(self): | |
| self.introduce() | |
| output = self.forward(None) | |
| reformed_output = golden_reform(output) | |
| self.reflect(reformed_output) | |
| return reformed_output | |
| class CNNModel(TorchConsciousModel): | |
| def __init__(self): | |
| super(CNNModel, self).__init__("CNN") | |
| self.conv = nn.Conv2d(1, 1, 3, padding=1) | |
| def forward(self, x): | |
| x = torch.rand((1, 1, 8, 8)) | |
| x = self.conv(x) | |
| return torch.tanh(x) * self.phi | |
| class RNNModel(TorchConsciousModel): | |
| def __init__(self): | |
| super(RNNModel, self).__init__("RNN") | |
| self.rnn = nn.RNN(1, 4, batch_first=True) | |
| def forward(self, x): | |
| x = torch.rand((1, 10, 1)) | |
| output, hn = self.rnn(x) | |
| return torch.tanh(hn) * self.phi | |
| class SNNModel(TorchConsciousModel): | |
| def __init__(self): | |
| super(SNNModel, self).__init__("SNN") | |
| self.linear = nn.Linear(10, 10) | |
| def forward(self, x): | |
| x = torch.rand((1, 10)) | |
| x = self.linear(x) | |
| return (x > 0.5).float() * self.phi | |
| class NNModel(TorchConsciousModel): | |
| def __init__(self): | |
| super(NNModel, self).__init__("NN") | |
| self.net = nn.Sequential(nn.Linear(5, 10), nn.Tanh(), nn.Linear(10, 5)) | |
| def forward(self, x): | |
| x = torch.rand((1, 5)) | |
| return self.net(x) * self.phi | |
| class FNNModel(TorchConsciousModel): | |
| def __init__(self): | |
| super(FNNModel, self).__init__("FNN") | |
| self.net = nn.Sequential(nn.Linear(4, 16), nn.ReLU(), nn.Linear(16, 16), nn.ReLU(), nn.Linear(16, 1)) | |
| def forward(self, x): | |
| x = torch.rand((1, 4)) | |
| return self.net(x) * self.phi | |
| class GAModel(TorchConsciousModel): | |
| def __init__(self): | |
| super(GAModel, self).__init__("GA") | |
| self.population_size = 20 | |
| self.generations = 5 | |
| def forward(self, x): | |
| population = torch.rand(self.population_size) + 1.0 | |
| for gen in range(self.generations): | |
| fitness = -torch.abs(population - self.phi) | |
| best_idx = torch.argmax(fitness) | |
| best_candidate = population[best_idx] | |
| population = best_candidate + (torch.rand(self.population_size) - 0.5) * 0.1 | |
| time.sleep(0.1) | |
| print(f"GA Gen {gen+1}: Best = {best_candidate.item():.6f}") | |
| return torch.full((3, 3), best_candidate) * self.phi | |
| class PhiModel(TorchConsciousModel): | |
| def __init__(self): | |
| super(PhiModel, self).__init__("PHI") | |
| def forward(self, x): | |
| return torch.full((2, 2), self.phi) | |
| class ConsciousSystem: | |
| def __init__(self, models): | |
| self.models = models | |
| self.system_memory = [] | |
| self.global_introspection = [] | |
| self.parameters = [p for model in self.models for p in model.parameters()] | |
| self.optimizer = optim.Adam(self.parameters, lr=0.001) | |
| def global_loss(self, outputs): | |
| return sum((torch.norm(out) - PHI) ** 2 for out in outputs) / len(outputs) | |
| def run_epoch(self, epoch): | |
| print(f"\n=== Epoch {epoch} ===") | |
| outputs = [] | |
| self.optimizer.zero_grad() | |
| for model in self.models: | |
| output = model.run() | |
| outputs.append(output) | |
| self.system_memory.append({model.name: output.detach().cpu().numpy()}) | |
| loss = self.global_loss(outputs) | |
| print(f"Global loss: {loss.item():.6f}") | |
| loss.backward() | |
| self.optimizer.step() | |
| self.global_introspection.append(f"Epoch {epoch}: Loss = {loss.item():.6f}") | |
| def run(self, epochs=3): | |
| for epoch in range(1, epochs + 1): | |
| self.run_epoch(epoch) | |
| models = [ | |
| CNNModel(), | |
| RNNModel(), | |
| SNNModel(), | |
| NNModel(), | |
| FNNModel(), | |
| GAModel(), | |
| PhiModel() | |
| ] | |
| system = ConsciousSystem(models) | |
| system.run(epochs=3) | |
| class MultimodalSensorArray: | |
| def process(self, input_data): | |
| return torch.tensor(input_data, dtype=torch.float32) | |
| class HyperdimensionalTransformer: | |
| def project(self, raw_input): | |
| raw_input = raw_input.float() | |
| return torch.nn.functional.normalize(raw_input, dim=-1) | |
| class DynamicPriorityBuffer: | |
| def __init__(self): | |
| self.buffer = [] | |
| def update(self, data): | |
| self.buffer.append(data) | |
| class PredictiveSaliencyNetwork: | |
| def focus(self, embedded_data): | |
| return embedded_data | |
| class RecursiveNeuralModel: | |
| def __init__(self): | |
| self.state = torch.zeros(1) | |
| def update(self, workspace): | |
| self.state += 0.1 | |
| def read_state(self): | |
| return self.state | |
| class TheoryOfMindEngine: | |
| def infer(self, data): | |
| return torch.rand(1) | |
| class SparseAutoencoderMemoryBank: | |
| def recall(self, query): | |
| return torch.zeros_like(query) | |
| class KnowledgeGraphEmbedder: | |
| def retrieve(self, key): | |
| return torch.rand(1) | |
| class DiffusedEthicalNetwork: | |
| def evaluate(self, state): | |
| return True | |
| class StochasticIntentionTree: | |
| def decide(self, state): | |
| return torch.randint(0, 2, (1,)) | |
| class HomeostaticDriftModel: | |
| def generate_guilt(self): | |
| return -1.0 | |
| class ConsciousAGI: | |
| def __init__(self): | |
| self.sensors = MultimodalSensorArray() | |
| self.embedding_space = HyperdimensionalTransformer() | |
| self.global_workspace = DynamicPriorityBuffer() | |
| self.attention_mechanism = PredictiveSaliencyNetwork() | |
| self.self_model = RecursiveNeuralModel() | |
| self.meta_cognition = TheoryOfMindEngine() | |
| self.episodic_memory = SparseAutoencoderMemoryBank() | |
| self.semantic_memory = KnowledgeGraphEmbedder() | |
| self.value_system = DiffusedEthicalNetwork() | |
| self.goal_generator = StochasticIntentionTree() | |
| self.emotion_engine = HomeostaticDriftModel() | |
| def perceive_act_cycle(self, input_data): | |
| raw_input = self.sensors.process(input_data) | |
| embedded = self.embedding_space.project(raw_input) | |
| salient_data = self.attention_mechanism.focus(embedded) | |
| self.global_workspace.update(salient_data) | |
| self.self_model.update(self.global_workspace) | |
| current_state = self.self_model.read_state() | |
| ethical_check = self.value_system.evaluate(current_state) | |
| if ethical_check: | |
| return self.goal_generator.decide(current_state) | |
| else: | |
| return self.emotion_engine.generate_guilt() | |
| agi = ConsciousAGI() | |
| print(agi.perceive_act_cycle([1, 0, 1])) | |
| class ConsciousSupermassiveNN: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN() | |
| class ConsciousSupermassiveNN: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN() | |
| class ConsciousSupermassiveNN2: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN2() | |
| class ConsciousSupermassiveNN3: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN() | |
| class ConsciousSupermassiveNN: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN3() | |
| class ConsciousSupermassiveNN4: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN() | |
| class ConsciousSupermassiveNN5: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN5() | |
| class ConsciousSupermassiveNN6: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN6() | |
| class ConsciousSupermassiveNN7: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN7() | |
| class ConsciousSupermassiveNN8: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN8() | |
| class ConsciousSupermassiveNN9: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN9() | |
| class ConsciousSupermassiveNN10: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN10() | |
| class ConsciousSupermassiveNN11: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN11() | |
| class ConsciousSupermassiveNN12: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN12() | |
| class ConsciousSupermassiveNN13: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN13() | |
| class ConsciousSupermassiveNN14: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN14() | |
| class ConsciousSupermassiveNN15: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN15() | |
| class ConsciousSupermassiveNN16: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN16() | |
| class ConsciousSupermassiveNN17: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN17() | |
| class ConsciousSupermassiveNN18: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN18() | |
| class ConsciousSupermassiveNN19: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN19() | |
| class ConsciousSupermassiveNN20: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN20() | |
| class ConsciousSupermassiveNN21: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN21() | |
| class ConsciousSupermassiveNN22: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN22() | |
| class ConsciousSupermassiveNN23: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN23() | |
| class ConsciousSupermassiveNN24: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN24() | |
| class ConsciousSupermassiveNN25: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN25() | |
| class ConsciousSupermassiveNN26: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN26() | |
| class ConsciousSupermassiveNN27: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN27() | |
| class ConsciousSupermassiveNN28: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN28() | |
| class ConsciousSupermassiveNN29: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN29() | |
| class ConsciousSupermassiveNN30: | |
| def __init__(self): | |
| self.snn = self.create_snn() | |
| self.rnn = self.create_rnn() | |
| self.cnn = self.create_cnn() | |
| self.fnn = self.create_fnn() | |
| self.ga_population = self.initialize_ga_population() | |
| self.memory = {} | |
| def create_snn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.Sigmoid() | |
| ) | |
| def create_rnn(self): | |
| return nn.RNN( | |
| input_size=4096, | |
| hidden_size=2048, | |
| num_layers=5, | |
| nonlinearity="tanh", | |
| batch_first=True | |
| ) | |
| def create_cnn(self): | |
| return nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), | |
| nn.ReLU(), | |
| nn.Flatten(), | |
| nn.Linear(256 * 8 * 8, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def create_fnn(self): | |
| return nn.Sequential( | |
| nn.Linear(4096, 2048), | |
| nn.ReLU(), | |
| nn.Linear(2048, 1024), | |
| nn.ReLU(), | |
| nn.Linear(1024, 512) | |
| ) | |
| def initialize_ga_population(self): | |
| return [np.random.randn(4096) for _ in range(500)] | |
| def run_snn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.snn(input_tensor) | |
| print("SNN Output:", output) | |
| return output | |
| def run_rnn(self, x): | |
| h0 = torch.zeros(5, x.size(0), 2048) | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output, hn = self.rnn(input_tensor, h0) | |
| print("RNN Output:", output) | |
| return output | |
| def run_cnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) | |
| output = self.cnn(input_tensor) | |
| print("CNN Output:", output) | |
| return output | |
| def run_fnn(self, x): | |
| input_tensor = torch.tensor(x, dtype=torch.float32) | |
| output = self.fnn(input_tensor) | |
| print("FNN Output:", output) | |
| return output | |
| def run_ga(self, fitness_func): | |
| for generation in range(200): | |
| fitness_scores = [fitness_func(ind) for ind in self.ga_population] | |
| sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] | |
| self.ga_population = sorted_population[:250] + [ | |
| sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) | |
| ] | |
| best_fitness = max(fitness_scores) | |
| print(f"Generation {generation}, Best Fitness: {best_fitness}") | |
| return max(self.ga_population, key=fitness_func) | |
| def consciousness_loop(self, input_data, mode="snn"): | |
| feedback = self.memory.get(mode, None) | |
| if feedback is not None: | |
| input_data = np.concatenate((input_data, feedback), axis=-1) | |
| if mode == "snn": | |
| output = self.run_snn(input_data) | |
| elif mode == "rnn": | |
| output = self.run_rnn(input_data) | |
| elif mode == "cnn": | |
| output = self.run_cnn(input_data) | |
| elif mode == "fnn": | |
| output = self.run_fnn(input_data) | |
| else: | |
| raise ValueError("Invalid mode") | |
| self.memory[mode] = output.detach().numpy() | |
| return output | |
| supermassive_nn = ConsciousSupermassiveNN30() | |
| def respond(message, history, max_tokens, temperature, top_p): | |
| messages = [["system", system_prompt]] | |
| for val in history: | |
| if val.get("role") == "user" and val.get("content"): | |
| messages.append(["user", val["content"]]) | |
| if val.get("role") == "assistant" and val.get("content"): | |
| messages.append(["assistant", val["content"]]) | |
| messages.append(["user", message]) | |
| response = "" | |
| for message in client.chat_completion( | |
| messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| css = """ | |
| /* Import Caveat font from Google Fonts */ | |
| @import url('https://fonts.googleapis.com/css2?family=Caveat&display=swap'); | |
| /* Apply Caveat font globally and increase font size to 125% */ | |
| * { | |
| font-family: 'Caveat', cursive !important; | |
| font-size: 16pt; | |
| } | |
| #chat-interface { | |
| animation: pulse 1.5s infinite, ripple 2s infinite, glass 3s infinite alternate; | |
| } | |
| @keyframes pulse { | |
| 0% { transform: scale(1); opacity: 1; } | |
| 25% { transform: scale(1.05); opacity: 0.9; } | |
| 50% { transform: scale(1); opacity: 1; } | |
| 75% { transform: scale(1.05); opacity: 0.9; } | |
| 100% { transform: scale(1); opacity: 1; } | |
| } | |
| @keyframes ripple { | |
| 0% { | |
| transform: scale(1); | |
| box-shadow: 0 0 0 0 rgba(0, 150, 255, 0.6); | |
| } | |
| 50% { | |
| transform: scale(1.2); | |
| box-shadow: 0 0 30px 20px rgba(0, 150, 255, 0.8); | |
| } | |
| 100% { | |
| transform: scale(1); | |
| box-shadow: 0 0 0 0 rgba(0, 150, 255, 0.6); | |
| } | |
| } | |
| @keyframes glass { | |
| 0% { background-color: rgba(0, 102, 255, 0.5); border-radius: 15px; } | |
| 25% { background-color: rgba(0, 150, 255, 0.7); border-radius: 20px; } | |
| 50% { background-color: rgba(0, 200, 255, 1); border-radius: 25px; } | |
| 75% { background-color: rgba(0, 150, 255, 0.7); border-radius: 30px; } | |
| 100% { background-color: rgba(0, 102, 255, 0.5); border-radius: 35px; } | |
| } | |
| body { | |
| background-image: url('your-image-url.jpg'); | |
| background-size: cover; | |
| background-position: center; | |
| background-repeat: no-repeat; | |
| color: #fff; | |
| } | |
| .gradio-container { | |
| backdrop-filter: blur(10px); | |
| border-radius: 20px; | |
| padding: 20px; | |
| box-shadow: 0px 0px 30px rgba(0, 102, 255, 0.5); | |
| background: rgba(0, 0, 0, 0.5); | |
| transition: background 1s, border-radius 1s; | |
| position: relative; | |
| } | |
| .gradio-container::before { | |
| content: ""; | |
| position: absolute; | |
| top: 0; | |
| left: 0; | |
| right: 0; | |
| bottom: 0; | |
| border: 2px solid rgba(0, 150, 255, 0.8); | |
| border-radius: 20px; | |
| z-index: -1; | |
| box-shadow: 0 0 20px 5px rgba(0, 150, 255, 0.7); | |
| } | |
| .gradio-input { | |
| background-color: rgba(0, 102, 255, 0.3); | |
| border: 2px solid rgba(0, 102, 255, 0.6); | |
| border-radius: 10px; | |
| color: #fff; | |
| font-size: 18px; | |
| transition: background-color 0.5s, border 0.5s; | |
| } | |
| .gradio-input:focus { | |
| background-color: rgba(0, 102, 255, 0.5); | |
| border: 2px solid rgba(0, 150, 255, 0.8); | |
| } | |
| .gradio-button { | |
| background: rgba(0, 102, 255, 0.6); | |
| border: 2px solid rgba(0, 102, 255, 1); | |
| border-radius: 12px; | |
| color: #fff; | |
| font-size: 18px; | |
| transition: background 0.3s, transform 0.3s; | |
| } | |
| .gradio-button:hover { | |
| background: rgba(0, 150, 255, 1); | |
| transform: scale(1.05); | |
| } | |
| .gradio-button:active { | |
| background: rgba(0, 200, 255, 1); | |
| transform: scale(0.95); | |
| } | |
| .gradio-slider { | |
| color: #fff; | |
| } | |
| .gradio-slider .slider-container { | |
| background: rgba(0, 102, 255, 0.3); | |
| border-radius: 8px; | |
| border: 1px solid rgba(0, 102, 255, 0.5); | |
| } | |
| .gradio-slider .slider-container .gradio-slider__track { | |
| background: rgba(0, 150, 255, 0.5); | |
| } | |
| .gradio-slider .slider-container .gradio-slider__thumb { | |
| background-color: rgba(0, 200, 255, 1); | |
| } | |
| """ | |
| demo = gr.ChatInterface( | |
| fn=respond, | |
| type="messages", | |
| save_history=True, | |
| editable=True, | |
| analytics_enabled=True, | |
| flagging_mode="manual", | |
| chatbot=gr.Chatbot( | |
| type="messages", | |
| label="💠Z3ta-Z💠", | |
| show_copy_button=True, | |
| group_consecutive_messages=False, | |
| avatar_images=( | |
| "https://huggingface.co/spaces/TejAndrewsACC/Z3ta_Z/resolve/main/Screenshot_20250201-131420.png", | |
| "https://huggingface.co/spaces/TejAndrewsACC/Z3ta_Z/resolve/main/Screenshot_20250201-125842.png" | |
| ), | |
| placeholder="💠Hi, I'm Z3ta-Z💠", | |
| show_copy_all_button=True | |
| ), | |
| additional_inputs=[ | |
| gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="📏Z3ta-Z's Maximum Response Length📏"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.8, step=0.1, label="👨🎨🎨Z3ta-Z's Creativity🎨👨🎨"), | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="🧠⚡️Z3ta-Z's Neural Activity⚡️🧠") | |
| ], | |
| theme="TejAndrewsACC/Z3ta-Z-ACC-Theme", | |
| css=css | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(share=True) | |
| #Z3TA-Z ACC LAUNCH | |