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Steffen Illium
2022-02-27 17:56:25 +01:00
parent 78a919395b
commit 926b27b4ef
3 changed files with 167 additions and 104 deletions
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sparse_net.py
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@ -1,25 +1,29 @@
from collections import defaultdict
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
from torch import nn
import functionalities_test
from network import Net
from functionalities_test import is_identity_function
from tqdm import tqdm,trange
from functionalities_test import is_identity_function, test_for_fixpoints, epsilon_error_margin
from tqdm import tqdm, trange
import numpy as np
from pathlib import Path
import torch
from torch.nn import Flatten
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor, Compose, Resize
def xavier_init(m):
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight.data)
return nn.init.xavier_uniform_(m.weight.data)
if isinstance(m, torch.Tensor):
return nn.init.xavier_uniform_(m)
class SparseLayer(nn.Module):
@ -101,7 +105,9 @@ class SparseLayer(nn.Module):
for weights in self.weights:
if torch.isinf(weights).any() or torch.isnan(weights).any():
with torch.no_grad():
xavier_init(weights)
where_nan = torch.nan_to_num(weights, -99, -99, -99)
mask = torch.where(where_nan == -99, 0, 1)
weights[:] = (where_nan * mask + torch.randn_like(weights) * (1 - mask))[:]
@property
def particle_weights(self):
@ -139,8 +145,9 @@ def test_sparse_layer():
optimizer = torch.optim.SGD(net.parameters(), lr=0.008, momentum=0.9)
# optimizer = torch.optim.SGD([layer.coalesce().values() for layer in net.sparse_sub_layer], lr=0.004, momentum=0.9)
df = pd.DataFrame(columns=['Epoch', 'Func Type', 'Count'])
train_iterations = 20000
for train_iteration in trange(20000):
for train_iteration in trange(train_iterations):
optimizer.zero_grad()
X, Y = net.get_self_train_inputs_and_targets()
output = net(X)
@ -163,12 +170,11 @@ def test_sparse_layer():
counter = defaultdict(lambda: 0)
id_functions = functionalities_test.test_for_fixpoints(counter, list(net.particles))
counter = dict(counter)
tqdm.write(f"identity_fn after {train_iteration + 1} self-train epochs: {counter}")
tqdm.write(f"identity_fn after {train_iterations} self-train epochs: {counter}")
for key, value in counter.items():
df.loc[df.shape[0]] = (train_iteration, key, value)
df.loc[df.shape[0]] = (train_iterations, key, value)
df.to_csv('counter.csv', mode='w')
import seaborn as sns
import matplotlib.pyplot as plt
c = pd.read_csv('counter.csv', index_col=0)
sns.lineplot(data=c, x='Epoch', y='Count', hue='Func Type')
plt.savefig('counter.png', dpi=300)
@ -191,6 +197,11 @@ def embed_vector(x, repeat_dim):
class SparseNetwork(nn.Module):
@property
def nr_nets(self):
return sum(x.nr_nets for x in self.sparselayers)
def __init__(self, input_dim, depth, width, out, residual_skip=True, activation=None,
weight_interface=5, weight_hidden_size=2, weight_output_size=1
):
@ -216,16 +227,13 @@ class SparseNetwork(nn.Module):
if self.activation:
tensor = self.activation(tensor)
for nl_idx, network_layer in enumerate(self.hidden_layers):
# Sparse Layer pass
# if idx % 2 == 1 and self.residual_skip:
if self.residual_skip:
residual = tensor
tensor = self.sparse_layer_forward(tensor, network_layer)
if self.activation:
tensor = self.activation(tensor)
if nl_idx % 2 == 0 and self.residual_skip:
residual = tensor.clone()
if nl_idx % 2 == 1 and self.residual_skip:
# noinspection PyUnboundLocalVariable
tensor += residual
# if idx % 2 == 0 and self.residual_skip:
if self.residual_skip:
tensor = tensor + residual
tensor = self.sparse_layer_forward(tensor, self.last_layer, view_dim=self.out_dim)
return tensor
@ -282,7 +290,7 @@ class SparseNetwork(nn.Module):
output = layer(x)
# loss = sum([loss_fn(out, target) for out, target in zip(output, target_data)]) / len(output)
loss = loss_fn(output, target_data) * 85
loss = loss_fn(output, target_data) * layer.nr_nets
losses.append(loss.detach())
loss.backward()
@ -311,39 +319,42 @@ def test_sparse_net():
data_dim = np.prod(dataset[0][0].shape)
metanet = SparseNetwork(data_dim, depth=3, width=5, out=10)
batchx, batchy = next(iter(d))
metanet(batchx)
print(f"identity_fn after {train_iteration+1} self-train iterations: {sum([torch.allclose(out[i], Y[i], rtol=0, atol=epsilon) for i in range(net.nr_nets)])}/{net.nr_nets}")
out = metanet(batchx)
result = sum([torch.allclose(out[i], batchy[i], rtol=0, atol=epsilon_error_margin) for i in range(metanet.nr_nets)])
# print(f"identity_fn after {train_iteration+1} self-train iterations: {result} /{net.nr_nets}")
def test_sparse_net_sef_train():
net = SparseNetwork(5, 5, 6, 10)
epochs = 10000
df = pd.DataFrame(columns=['Epoch', 'Func Type', 'Count'])
optimizer = torch.optim.SGD(net.parameters(), lr=0.004, momentum=0.9)
for epoch in trange(epochs):
_ = net.combined_self_train(optimizer)
sparse_metanet = SparseNetwork(15*15, 5, 6, 10).to('cuda')
init_st_store_path = Path('counter.csv')
optimizer = torch.optim.SGD(sparse_metanet.parameters(), lr=0.004, momentum=0.9)
init_st_epochs = 10000
init_st_df = pd.DataFrame(columns=['Epoch', 'Func Type', 'Count'])
if epoch % 500 == 0:
for st_epoch in trange(init_st_epochs):
_ = sparse_metanet.combined_self_train(optimizer)
if st_epoch % 500 == 0:
counter = defaultdict(lambda: 0)
id_functions = functionalities_test.test_for_fixpoints(counter, list(net.particles))
id_functions = test_for_fixpoints(counter, list(sparse_metanet.particles))
counter = dict(counter)
tqdm.write(f"identity_fn after {epoch + 1} self-train epochs: {counter}")
tqdm.write(f"identity_fn after {st_epoch} self-train epochs: {counter}")
for key, value in counter.items():
df.loc[df.shape[0]] = (epoch, key, value)
net.reset_diverged_particles()
init_st_df.loc[init_st_df.shape[0]] = (st_epoch, key, value)
sparse_metanet.reset_diverged_particles()
counter = defaultdict(lambda: 0)
id_functions = functionalities_test.test_for_fixpoints(counter, list(net.particles))
id_functions = test_for_fixpoints(counter, list(sparse_metanet.particles))
counter = dict(counter)
tqdm.write(f"identity_fn after {epochs} self-train epochs: {counter}")
tqdm.write(f"identity_fn after {init_st_epochs} self-train epochs: {counter}")
for key, value in counter.items():
df.loc[df.shape[0]] = (epoch, key, value)
df.to_csv('counter.csv', mode='w')
import seaborn as sns
import matplotlib.pyplot as plt
c = pd.read_csv('counter.csv', index_col=0)
init_st_df.loc[init_st_df.shape[0]] = (init_st_epochs, key, value)
init_st_df.to_csv(init_st_store_path, mode='w', index=False)
c = pd.read_csv(init_st_store_path)
sns.lineplot(data=c, x='Epoch', y='Count', hue='Func Type')
plt.savefig('counter.png', dpi=300)
plt.savefig(init_st_store_path, dpi=300)
def test_manual_for_loop():
@ -353,7 +364,7 @@ def test_manual_for_loop():
rounds = 1000
for net in tqdm(nets):
optimizer = torch.optim.SGD(net.parameters(), lr=0.004, momentum=0.9)
optimizer = torch.optim.SGD(net.parameters(), lr=0.0001, momentum=0.9)
for i in range(rounds):
optimizer.zero_grad()
input_data = net.input_weight_matrix()