added episilon for equality check
major layout changes in the network
This commit is contained in:
251
output_graphs.py
251
output_graphs.py
@@ -2,256 +2,7 @@ import re
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import pandas as pd
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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text = r"""INFO:__main__:step= 0 loss=1.3149878 acc=0.9093018
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text = r"""
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INFO:__main__:step=23200 loss=0.0000085 acc=1.0000000
|
|
||||||
INFO:__main__:step=23300 loss=0.0000350 acc=1.0000000
|
|
||||||
INFO:__main__:step=23400 loss=0.0000005 acc=1.0000000
|
|
||||||
INFO:__main__:step=23500 loss=0.0000538 acc=0.9999390
|
|
||||||
INFO:__main__:step=23600 loss=0.0000021 acc=1.0000000
|
|
||||||
INFO:__main__:step=23700 loss=0.0000365 acc=1.0000000
|
|
||||||
INFO:__main__:step=23800 loss=0.0000281 acc=1.0000000
|
|
||||||
INFO:__main__:step=23900 loss=0.0000091 acc=1.0000000
|
|
||||||
INFO:__main__:step=24000 loss=0.0000045 acc=1.0000000
|
|
||||||
INFO:__main__:step=24100 loss=0.0000023 acc=1.0000000
|
|
||||||
INFO:__main__:step=24200 loss=0.0000197 acc=1.0000000
|
|
||||||
INFO:__main__:step=24300 loss=0.0000013 acc=1.0000000
|
|
||||||
INFO:__main__:step=24400 loss=0.0000174 acc=1.0000000
|
|
||||||
INFO:__main__:step=24500 loss=0.0000380 acc=1.0000000
|
|
||||||
INFO:__main__:step=24600 loss=0.0000105 acc=1.0000000
|
|
||||||
INFO:__main__:step=24700 loss=0.0000001 acc=1.0000000
|
|
||||||
INFO:__main__:step=24800 loss=0.0000193 acc=1.0000000
|
|
||||||
INFO:__main__:step=24900 loss=0.0000280 acc=1.0000000
|
|
||||||
"""
|
"""
|
||||||
|
|
||||||
pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)")
|
pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)")
|
||||||
|
|||||||
@@ -3,32 +3,35 @@ from torch import nn
|
|||||||
|
|
||||||
# 2) Number "embedding" network: R -> R^d
|
# 2) Number "embedding" network: R -> R^d
|
||||||
class NumberEmbedder(nn.Module):
|
class NumberEmbedder(nn.Module):
|
||||||
def __init__(self, d=8):
|
def __init__(self, d=4, hidden=16):
|
||||||
super().__init__()
|
super().__init__()
|
||||||
self.net = nn.Sequential(
|
self.net = nn.Sequential(
|
||||||
nn.Linear(1, 16),
|
nn.Linear(1, hidden),
|
||||||
nn.ReLU(),
|
nn.ReLU(),
|
||||||
nn.Linear(16, d),
|
nn.Linear(hidden, d),
|
||||||
)
|
)
|
||||||
|
|
||||||
def forward(self, x):
|
def forward(self, x):
|
||||||
return self.net(x)
|
return self.net(x)
|
||||||
|
|
||||||
# 3) Comparator head: takes (ea, eb) -> logit for "a > b"
|
# 3) Comparator head: takes (ea, eb, e) -> logit for "a > b"
|
||||||
class PairwiseComparator(nn.Module):
|
class PairwiseComparator(nn.Module):
|
||||||
def __init__(self, d=8):
|
def __init__(self, d=4, hidden=16, k=1.0):
|
||||||
super().__init__()
|
super().__init__()
|
||||||
self.embed = NumberEmbedder(d)
|
self.log_k = nn.Parameter(torch.tensor([k]))
|
||||||
|
self.embed = NumberEmbedder(d, hidden)
|
||||||
self.head = nn.Sequential(
|
self.head = nn.Sequential(
|
||||||
nn.Linear(2 * d + 1, 16),
|
nn.Linear(d, hidden),
|
||||||
nn.ReLU(),
|
nn.ReLU(),
|
||||||
nn.Linear(16, 1),
|
nn.Linear(hidden, hidden),
|
||||||
|
nn.ReLU(),
|
||||||
|
nn.Linear(hidden, 1),
|
||||||
)
|
)
|
||||||
|
|
||||||
def forward(self, a, b):
|
def forward(self, a, b):
|
||||||
ea = self.embed(a)
|
# trying to force antisym here: h(a,b)=−h(b,a)
|
||||||
eb = self.embed(b)
|
phi = self.head(self.embed(a-b))
|
||||||
delta_ab = a - b
|
phi_neg = self.head(self.embed(b-a))
|
||||||
x = torch.cat([ea, eb, delta_ab], dim=-1)
|
logit = phi - phi_neg
|
||||||
|
|
||||||
return self.head(x) # logits
|
return (self.log_k ** 2) * logit
|
||||||
@@ -13,12 +13,12 @@ import pairwise_comp_nn as comp_nn
|
|||||||
DEVICE = torch.accelerator.current_accelerator() if torch.accelerator.is_available() else "cpu"
|
DEVICE = torch.accelerator.current_accelerator() if torch.accelerator.is_available() else "cpu"
|
||||||
|
|
||||||
# Valves
|
# Valves
|
||||||
DIMENSIONS = 1
|
DIMENSIONS = 2
|
||||||
TRAIN_STEPS = 10000
|
TRAIN_STEPS = 5000
|
||||||
TRAIN_BATCHSZ = 8192
|
TRAIN_BATCHSZ = 8192
|
||||||
TRAIN_PROGRESS = 100
|
TRAIN_PROGRESS = 10
|
||||||
BATCH_LOWER = -512.0
|
BATCH_LOWER = -100.0
|
||||||
BATCH_UPPER = 512.0
|
BATCH_UPPER = 100.0
|
||||||
DO_VERBOSE_EARLY_TRAIN = False
|
DO_VERBOSE_EARLY_TRAIN = False
|
||||||
MODEL_PATH = "./files/pwcomp.model"
|
MODEL_PATH = "./files/pwcomp.model"
|
||||||
LOGGING_PATH = "./files/output.log"
|
LOGGING_PATH = "./files/output.log"
|
||||||
@@ -46,17 +46,15 @@ def plt_embeddings(model: comp_nn.PairwiseComparator):
|
|||||||
|
|
||||||
for i in range(embeddings.shape[1]):
|
for i in range(embeddings.shape[1]):
|
||||||
plt.plot(xs.squeeze(), embeddings[:, i], label=f"dim {i}")
|
plt.plot(xs.squeeze(), embeddings[:, i], label=f"dim {i}")
|
||||||
|
|
||||||
plt.legend()
|
plt.legend()
|
||||||
plt.savefig(EMBED_CHART_PATH)
|
plt.savefig(EMBED_CHART_PATH)
|
||||||
plt.show()
|
#plt.show()
|
||||||
|
|
||||||
csv_data = list(zip(xs.squeeze().tolist(), embeddings.tolist()))
|
csv_data = list(zip(xs.squeeze().tolist(), embeddings.tolist()))
|
||||||
with open(file=EMBEDDINGS_DATA, mode="w", newline='') as f:
|
with open(file=EMBEDDINGS_DATA, mode="w", newline='') as f:
|
||||||
csv_file = csv.writer(f)
|
csv_file = csv.writer(f)
|
||||||
csv_file.writerows(csv_data)
|
csv_file.writerows(csv_data)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def get_torch_info():
|
def get_torch_info():
|
||||||
log.info("PyTorch Version: %s", torch.__version__)
|
log.info("PyTorch Version: %s", torch.__version__)
|
||||||
log.info("HIP Version: %s", torch.version.hip)
|
log.info("HIP Version: %s", torch.version.hip)
|
||||||
@@ -78,13 +76,10 @@ def sample_batch(batch_size: int, low=BATCH_LOWER, high=BATCH_UPPER):
|
|||||||
a = (high - low) * torch.rand(batch_size, 1) + low
|
a = (high - low) * torch.rand(batch_size, 1) + low
|
||||||
b = (high - low) * torch.rand(batch_size, 1) + low
|
b = (high - low) * torch.rand(batch_size, 1) + low
|
||||||
|
|
||||||
# train for if a > b
|
epsi = 1e-4
|
||||||
y = (a > b).float()
|
y = torch.where(a > b + epsi, 1.0,
|
||||||
|
torch.where(a < b - epsi, 0.0, 0.5))
|
||||||
|
|
||||||
# removed but left for my notes; it seems training for equality hurts classifing results that are ~eq
|
|
||||||
# when trained only on "if a > b => y", the model produces more accurate results when classifing if things are equal (~.5 prob).
|
|
||||||
# eq = (a == b).float()
|
|
||||||
# y = gt + 0.5 * eq
|
|
||||||
return a, b, y
|
return a, b, y
|
||||||
|
|
||||||
def training_entry():
|
def training_entry():
|
||||||
@@ -150,7 +145,7 @@ def infer_entry():
|
|||||||
with torch.no_grad():
|
with torch.no_grad():
|
||||||
probs = torch.sigmoid(model(a, b))
|
probs = torch.sigmoid(model(a, b))
|
||||||
|
|
||||||
log.info(f"Output probabilities for {pairs.count} pairs")
|
log.info(f"Output probabilities for {pairs.__len__()} pairs")
|
||||||
for (x, y), p in zip(pairs, probs):
|
for (x, y), p in zip(pairs, probs):
|
||||||
log.info(f"P({x} > {y}) = {p.item():.3f}")
|
log.info(f"P({x} > {y}) = {p.item():.3f}")
|
||||||
|
|
||||||
|
|||||||
Reference in New Issue
Block a user