def regression_problem(x):
bias = x[:total_bias_params]
weight = x[total_bias_params:]
genome = generate_genome_with_hidden_units(n_input=config.n_input,
n_output=config.n_output,
n_hidden=n_neurons_per_layer)
nodes = genome.node_genes
connections = genome.connection_genes
i = 0
for key, node in nodes.items():
node.bias_mean = bias[i]
node.bias_std = bias[i+1]
i += 2
i = 0
for key, connection in connections.items():
connection.weight_mean = weight[i]
connection.weight_std = weight[i + 1]
i += 2
evaluation_engine = EvaluationStochasticEngine(batch_size=50000)
loss = \
evaluation_engine.evaluate_genome(genome=genome, n_samples=N_SAMPLES, return_all=False)
print(loss)
return loss
def main():
model_filename = 'network-regression_1.pt'
network = FeedForward(n_input=config.n_input,
n_output=config.n_output,
n_neurons_per_layer=n_neurons_per_layer,
n_hidden_layers=1)
parameters = torch.load(f'./../../deep_learning/models/{model_filename}')
network.load_state_dict(parameters)
std = -3.1
genome = get_genome_from_standard_network(network, std=std)
evaluation_engine = EvaluationStochasticEngine(testing=False)
x, y_true, y_pred, kl_posterior = \
evaluation_engine.evaluate_genome(genome, n_samples=1, is_gpu=False, return_all=True)
print()
print(f'KL Posterior: {kl_posterior}')
x = x.numpy()
x = evaluation_engine.dataset.input_scaler.inverse_transform(x)
y_pred = evaluation_engine.dataset.output_scaler.inverse_transform(
y_pred.numpy())
y_true = evaluation_engine.dataset.output_scaler.inverse_transform(
y_true.numpy())
# plot results
plt.figure(figsize=(20, 20))
plt.plot(x, y_true, 'r*')
plt.plot(x, y_pred, 'b*')
plt.show()
print(f'MSE: {mean_squared_error(y_true, y_pred) * 100} %')
def main():
model_filename = 'network-classification.pt'
network = FeedForward(n_input=config.n_input, n_output=config.n_output,
n_neurons_per_layer=n_neurons_per_layer,
n_hidden_layers=1)
parameters = torch.load(f'./../../deep_learning/models/{model_filename}')
network.load_state_dict(parameters)
std = -3.1
genome = get_genome_from_standard_network(network, std=std)
# genome = generate_genome_with_hidden_units(2, 2, n_hidden=1)
evaluation_engine = EvaluationStochasticEngine(testing=False)
x, y_true, y_pred, kl_posterior = \
evaluation_engine.evaluate_genome(genome, n_samples=100, is_gpu=False, return_all=True)
print()
print(f'KL Posterior: {kl_posterior}')
x = x.numpy()
x = evaluation_engine.dataset.input_scaler.inverse_transform(x)
y_true = y_true.numpy()
# plot results
y_pred = np.argmax(y_pred.numpy(), 1)
df = pd.DataFrame(x, columns=['x1', 'x2'])
df['y'] = y_pred
x1_limit, x2_limit = evaluation_engine.dataset.get_separation_line()
plt.figure()
ax = sns.scatterplot(x='x1', y='x2', hue='y', data=df)
ax.plot(x1_limit, x2_limit, 'g-', linewidth=2.5)
plt.show()
print('Confusion Matrix:')
print(confusion_matrix(y_true, y_pred))
print(f'Accuracy: {accuracy_score(y_true, y_pred) * 100} %')
def regression_problem_learn_from_nn():
# standard network
network = FeedForward(n_input=config.n_input,
n_output=config.n_output,
n_neurons_per_layer=n_neurons_per_layer,
n_hidden_layers=1)
parameters = torch.load('./deep_learning/models/network.pt')
network.load_state_dict(parameters)
genome = prepare_genome(parameters)
print(genome)
evaluation_engine = EvaluationStochasticEngine(testing=False,
batch_size=None)
x, y_true, y_pred, kl_posterior = \
evaluation_engine.evaluate_genome(genome, n_samples=100, is_gpu=False, return_all=True)
plt.figure(figsize=(20, 20))
plt.plot(x.numpy().reshape(-1), y_true.numpy().reshape(-1), 'b*')
plt.plot(x.numpy().reshape(-1), y_pred.numpy().reshape(-1), 'r*')
plt.show()
print(f'KL Div - Posterior: {kl_posterior}')