class pytorch_nn():
def __init__(self):
weights_pth = os.path.join('./checkpoints', 'nn_weights.pth')
self.model = MLP(input_size=5, output_size=3)
self.model.load_state_dict(
torch.load(weights_pth, map_location=torch.device('cpu')))
self.model.eval()
def predict(self, in_vector):
in_vector = torch.from_numpy(np.array(in_vector))
in_vector = Variable(in_vector).float()
outputs = self.model.forward(in_vector)
prob, pred = outputs.max(0, keepdim=True)
return outputs, prob, pred
def getNewNN(thisDict):
weights_pth = "checkpoints/nn_weights.pth"
model = MLP(input_size=5, output_size=3)
model.load_state_dict(
torch.load(weights_pth, map_location=torch.device('cpu')))
model.eval()
in_vector = []
for key, value in thisDict.items():
print(key, value)
temp = masterMerged.loc[masterMerged[key + "_x"] == value].head(1)
in_vector.append(temp.iloc[0][key + "_y"])
#in_vector = [1, 589, 9, 1, 0]
in_vector = torch.from_numpy(np.array(in_vector))
in_vector = Variable(in_vector).float()
outputs = model.forward(in_vector)
return outputs.tolist()
run_save_prefix = os.path.join(save_folder, f"{dataset_name}/e={eta}/")
print(run_save_prefix)
ensure_dir(run_save_prefix)
for number_of_nodes in NODES:
run_name = f'NODE{number_of_nodes:02d}_e={eta}_d={dataset_name}'.replace(".", ",")
mae_array = []
for i in range(LOOPS):
i += 1
print(run_name)
net = MLP(train[0], train[1], number_of_nodes, momentum=0, outtype="linear")
train_losses, valid_losses, _, _, pocket_epoch = net.train(
train[0], train[1], valid[0], valid[1], eta, 300000,
early_stop_count=100000, shuffle=False, batch_size=batch_size
)
net.forward(test[0])
print(mae(net.outputs, test[1]))
mae_array.append(mae(net.outputs, test[1]))
mean, stddev = statistics.mean(mae_array), statistics.stdev(mae_array)
results[dataset_name][eta][number_of_nodes] = mean, stddev
plt.title(f'Performance e={eta} d={dataset_name}')
plt.plot(NODES, np.log([results[dataset_name][eta][node][0] for node in NODES]))
plt.xlabel('number of nodes')
plt.ylabel('log of test MAE')
plt.savefig(fname=os.path.join(f'{run_save_prefix}', f'node_performance'), dpi=300)
plt.close()
results_filename = f"results etas:{ETA_VALUES}" \
f" n:{NODES}" \
dataset[1],
n_hidden_nodes,
momentum=momentum)
train_losses, _, train_accuracies, _, pocket_epoch = net.train(
dataset[0],
dataset[1],
dataset[0],
dataset[1],
eta,
epochs,
early_stop_count=early_stop,
shuffle=shuffle,
batch_size=batch_size,
early_stopping_threshold=early_stopping_threshold)
train_accuracies = np.array(train_accuracies) * 100 / n
net.forward(dataset[0])
print("pocket epoch", pocket_epoch)
print(
f"w1:\n{net.weights1}\nw2:{net.weights2}\nnet.hidden:\n{net.hidden}\noutputs:{net.outputs}\n"
)
save_prefix = os.path.join(save_folder, name)
fig, ax1 = plt.subplots()
plt.title(name + " Learning curve")
x = np.arange(len(train_losses))
ax1.set_xlabel('Epoch')
ax1.set_ylabel('Log MSE loss')
ax1.plot(x,
np.log(train_losses),
color='tab:red',