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import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
def relu(x):
return np.maximum(0, x)
def relu_derivative(x):
return (x > 0).astype(float)
def tanh(x):
return np.tanh(x)
def tanh_derivative(x):
return 1 - np.tanh(x)**2
class EveOptimizer:
def __init__(self, params, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8):
self.params = params
self.lr = learning_rate
self.beta1 = beta1
self.beta2 = beta2
self.epsilon = epsilon
self.t = 0
self.m = [np.zeros_like(p) for p in params]
self.v = [np.zeros_like(p) for p in params]
self.fractal_memory = [np.zeros_like(p) for p in params]
def step(self, grads):
self.t += 1
for i, (param, grad) in enumerate(zip(self.params, grads)):
self.m[i] = self.beta1 * self.m[i] + (1 - self.beta1) * grad
self.v[i] = self.beta2 * self.v[i] + (1 - self.beta2) * (grad ** 2)
m_hat = self.m[i] / (1 - self.beta1 ** self.t)
v_hat = self.v[i] / (1 - self.beta2 ** self.t)
fractal_factor = self.fractal_adjustment(param, grad)
self.fractal_memory[i] = 0.9 * self.fractal_memory[i] + 0.1 * fractal_factor
param -= self.lr * m_hat / (np.sqrt(v_hat) + self.epsilon) * self.fractal_memory[i]
def fractal_adjustment(self, param, grad):
c = np.mean(grad) + 1j * np.std(param)
z = 0
for _ in range(10):
z = z**2 + c
if abs(z) > 2:
break
return 1 / (1 + abs(z))
class BatchNormalization:
def __init__(self, input_shape):
self.gamma = np.ones(input_shape)
self.beta = np.zeros(input_shape)
self.epsilon = 1e-5
self.moving_mean = np.zeros(input_shape)
self.moving_var = np.ones(input_shape)
def forward(self, x, training=True):
if training:
mean = np.mean(x, axis=0)
var = np.var(x, axis=0)
self.moving_mean = 0.99 * self.moving_mean + 0.01 * mean
self.moving_var = 0.99 * self.moving_var + 0.01 * var
else:
mean = self.moving_mean
var = self.moving_var
x_norm = (x - mean) / np.sqrt(var + self.epsilon)
out = self.gamma * x_norm + self.beta
if training:
self.cache = (x, x_norm, mean, var)
return out
def backward(self, dout):
x, x_norm, mean, var = self.cache
m = x.shape[0]
dx_norm = dout * self.gamma
dvar = np.sum(dx_norm * (x - mean) * -0.5 * (var + self.epsilon)**(-1.5), axis=0)
dmean = np.sum(dx_norm * -1 / np.sqrt(var + self.epsilon), axis=0) + dvar * np.mean(-2 * (x - mean), axis=0)
dx = dx_norm / np.sqrt(var + self.epsilon) + dvar * 2 * (x - mean) / m + dmean / m
dgamma = np.sum(dout * x_norm, axis=0)
dbeta = np.sum(dout, axis=0)
return dx, dgamma, dbeta
class Reward:
def __init__(self):
self.lowest_avg_batch_loss = float('inf')
self.lowest_max_batch_loss = float('inf')
self.best_weights = None
def update(self, avg_batch_loss, max_batch_loss, network):
improved = False
if avg_batch_loss < self.lowest_avg_batch_loss:
self.lowest_avg_batch_loss = avg_batch_loss
improved = True
if max_batch_loss < self.lowest_max_batch_loss:
self.lowest_max_batch_loss = max_batch_loss
improved = True
if improved:
self.best_weights = self.get_network_weights(network)
def get_network_weights(self, network):
weights = []
for layer in network.layers:
layer_weights = []
for agent in layer.agents:
agent_weights = {
'weights': agent.weights.copy(),
'bias': agent.bias.copy(),
'bn_gamma': agent.bn.gamma.copy(),
'bn_beta': agent.bn.beta.copy()
}
layer_weights.append(agent_weights)
weights.append(layer_weights)
return weights
def apply_best_weights(self, network):
if self.best_weights is not None:
for layer, layer_weights in zip(network.layers, self.best_weights):
for agent, agent_weights in zip(layer.agents, layer_weights):
agent.weights = agent_weights['weights'].copy()
agent.bias = agent_weights['bias'].copy()
agent.bn.gamma = agent_weights['bn_gamma'].copy()
agent.bn.beta = agent_weights['bn_beta'].copy()
class Agent:
def __init__(self, id, input_size, output_size, fractal_method):
self.id = id
self.weights = np.random.randn(input_size, output_size) * np.sqrt(2. / input_size)
self.bias = np.zeros((1, output_size))
self.fractal_method = fractal_method
self.bn = BatchNormalization((output_size,))
self.optimizer = EveOptimizer([self.weights, self.bias, self.bn.gamma, self.bn.beta])
def forward(self, x, training=True):
self.last_input = x
z = np.dot(x, self.weights) + self.bias
z_bn = self.bn.forward(z, training)
self.last_output = relu(z_bn)
return self.last_output
def backward(self, error, l2_lambda=1e-5):
delta = error * relu_derivative(self.last_output)
delta, dgamma, dbeta = self.bn.backward(delta)
dw = np.dot(self.last_input.T, delta) + l2_lambda * self.weights
db = np.sum(delta, axis=0, keepdims=True)
self.optimizer.step([dw, db, dgamma, dbeta])
return np.dot(delta, self.weights.T)
def apply_fractal(self, x):
return self.fractal_method(x)
class Swarm:
def __init__(self, num_agents, input_size, output_size, fractal_method):
self.agents = [Agent(i, input_size, output_size, fractal_method) for i in range(num_agents)]
def forward(self, x, training=True):
results = [agent.forward(x, training) for agent in self.agents]
return np.mean(results, axis=0)
def backward(self, error, l2_lambda):
errors = [agent.backward(error, l2_lambda) for agent in self.agents]
return np.mean(errors, axis=0)
def apply_fractal(self, x):
results = [agent.apply_fractal(x) for agent in self.agents]
return np.mean(results, axis=0)
class SwarmNeuralNetwork:
def __init__(self, layer_sizes, fractal_methods):
self.layers = []
for i in range(len(layer_sizes) - 2):
self.layers.append(Swarm(num_agents=3,
input_size=layer_sizes[i],
output_size=layer_sizes[i+1],
fractal_method=fractal_methods[i]))
self.output_layer = Swarm(num_agents=1,
input_size=layer_sizes[-2],
output_size=layer_sizes[-1],
fractal_method=fractal_methods[-1])
self.reward = Reward()
def forward(self, x, training=True):
self.layer_outputs = [x]
for layer in self.layers:
x = layer.forward(x, training)
self.layer_outputs.append(x)
self.final_output = tanh(self.output_layer.forward(x, training))
return self.final_output
def backward(self, error, l2_lambda=1e-5):
error = error * tanh_derivative(self.final_output)
error = self.output_layer.backward(error, l2_lambda)
for i in reversed(range(len(self.layers))):
error = self.layers[i].backward(error, l2_lambda)
def train(self, X, y, epochs, batch_size=32, l2_lambda=1e-5, patience=50):
best_mse = float('inf')
patience_counter = 0
for epoch in range(epochs):
indices = np.arange(len(X))
np.random.shuffle(indices)
self.reward.apply_best_weights(self)
epoch_losses = []
for start_idx in range(0, len(X) - batch_size + 1, batch_size):
batch_indices = indices[start_idx:start_idx+batch_size]
X_batch = X[batch_indices]
y_batch = y[batch_indices]
output = self.forward(X_batch)
error = y_batch - output
error = np.clip(error, -1, 1)
self.backward(error, l2_lambda)
epoch_losses.append(np.mean(np.square(error)))
avg_batch_loss = np.mean(epoch_losses)
max_batch_loss = np.max(epoch_losses)
self.reward.update(avg_batch_loss, max_batch_loss, self)
mse = np.mean(np.square(y - self.forward(X, training=False)))
if epoch % 100 == 0:
print(f"Epoch {epoch}, MSE: {mse:.6f}, Avg Batch Loss: {avg_batch_loss:.6f}, Min Batch Loss: {np.min(epoch_losses):.6f}, Max Batch Loss: {max_batch_loss:.6f}")
if mse < best_mse:
best_mse = mse
patience_counter = 0
else:
patience_counter += 1
if patience_counter >= patience:
print(f"Early stopping at epoch {epoch}")
break
return best_mse
def apply_fractals(self, x):
fractal_outputs = []
for i, layer in enumerate(self.layers):
x = self.layer_outputs[i+1]
fractal_output = layer.apply_fractal(x)
fractal_outputs.append(fractal_output)
return fractal_outputs
def sierpinski_fractal(input_data):
t = np.linspace(0, 2 * np.pi, input_data.shape[0])
x = np.mean(input_data) * np.cos(t)
y = np.mean(input_data) * np.sin(t)
return x, y
def mandelbrot_fractal(input_data, max_iter=10):
output = np.zeros(input_data.shape[0])
for i in range(input_data.shape[0]):
c = input_data[i, 0] + 0.1j * np.std(input_data)
z = 0
for n in range(max_iter):
if abs(z) > 2:
output[i] = n
break
z = z*z + c
else:
output[i] = max_iter
return output
def julia_fractal(input_data, max_iter=10):
output = np.zeros(input_data.shape[0])
c = -0.8 + 0.156j
for i in range(input_data.shape[0]):
z = input_data[i, 0] + 0.1j * np.std(input_data)
for n in range(max_iter):
if abs(z) > 2:
output[i] = n
break
z = z*z + c
else:
output[i] = max_iter
return output
def run_snn(epochs, batch_size, l2_lambda, patience):
np.random.seed(42)
X = np.linspace(0, 10, 1000).reshape(-1, 1)
y = np.sin(X).reshape(-1, 1)
X = (X - X.min()) / (X.max() - X.min())
y = (y - y.min()) / (y.max() - y.min())
snn = SwarmNeuralNetwork(layer_sizes=[1, 32, 16, 8, 1],
fractal_methods=[sierpinski_fractal, mandelbrot_fractal, julia_fractal, julia_fractal])
snn.train(X, y, epochs=epochs, batch_size=batch_size, l2_lambda=l2_lambda, patience=patience)
y_pred = snn.forward(X, training=False)
fractal_outputs = snn.apply_fractals(X)
fig, axs = plt.subplots(2, 2, figsize=(15, 10))
axs[0, 0].plot(X, y, label='True')
axs[0, 0].plot(X, y_pred, label='Predicted')
axs[0, 0].legend()
axs[0, 0].set_title('True vs Predicted')
x, y = fractal_outputs[0]
axs[0, 1].plot(x, y)
axs[0, 1].set_title('Sierpinski Fractal Output')
axs[1, 0].plot(X, fractal_outputs[1])
axs[1, 0].set_title('Mandelbrot Fractal Output')
axs[1, 1].plot(X, fractal_outputs[2])
axs[1, 1].set_title('Julia Fractal Output')
plt.tight_layout()
return fig
with gr.Blocks() as demo:
epochs = gr.Slider(1, 10000, value=5000, label="Epochs")
batch_size = gr.Slider(1, 100, value=32, label="Batch Size")
l2_lambda = gr.Slider(0.0001, 0.1, value=0.00001, label="L2 Lambda")
patience = gr.Slider(1, 1000, value=50, label="Patience")
plot = gr.Plot()
def update_plot(epochs, batch_size, l2_lambda, patience):
return run_snn(epochs, batch_size, l2_lambda, patience)
btn = gr.Button("Run SNN")
btn.click(update_plot, inputs=[epochs, batch_size, l2_lambda, patience], outputs=plot)
demo.launch()
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