diff --git a/output_graphs.py b/output_graphs.py index 1412f37..684f022 100644 --- a/output_graphs.py +++ b/output_graphs.py @@ -2,256 +2,7 @@ import re import pandas as pd import matplotlib.pyplot as plt -text = r"""INFO:__main__:step= 0 loss=1.3149878 acc=0.9093018 -INFO:__main__:step= 100 loss=0.0089776 acc=0.9993286 -INFO:__main__:step= 200 loss=0.0088239 acc=0.9996948 -INFO:__main__:step= 300 loss=0.0075553 acc=0.9996948 -INFO:__main__:step= 400 loss=0.0065352 acc=0.9995728 -INFO:__main__:step= 500 loss=0.0053752 acc=0.9997559 -INFO:__main__:step= 600 loss=0.0043060 acc=0.9998169 -INFO:__main__:step= 700 loss=0.0045364 acc=0.9996338 -INFO:__main__:step= 800 loss=0.0037988 acc=0.9996948 -INFO:__main__:step= 900 loss=0.0037188 acc=0.9998779 -INFO:__main__:step= 1000 loss=0.0034959 acc=0.9996338 -INFO:__main__:step= 1100 loss=0.0032190 acc=0.9998169 -INFO:__main__:step= 1200 loss=0.0033895 acc=1.0000000 -INFO:__main__:step= 1300 loss=0.0031267 acc=0.9998779 -INFO:__main__:step= 1400 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loss=0.0000193 acc=1.0000000 -INFO:__main__:step=24900 loss=0.0000280 acc=1.0000000 +text = r""" """ pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)") diff --git a/pairwise_comp_nn.py b/pairwise_comp_nn.py index 39d9953..f33df5f 100644 --- a/pairwise_comp_nn.py +++ b/pairwise_comp_nn.py @@ -3,32 +3,35 @@ from torch import nn # 2) Number "embedding" network: R -> R^d class NumberEmbedder(nn.Module): - def __init__(self, d=8): + def __init__(self, d=4, hidden=16): super().__init__() self.net = nn.Sequential( - nn.Linear(1, 16), + nn.Linear(1, hidden), nn.ReLU(), - nn.Linear(16, d), + nn.Linear(hidden, d), ) def forward(self, 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): - def __init__(self, d=8): + def __init__(self, d=4, hidden=16, k=1.0): super().__init__() - self.embed = NumberEmbedder(d) + self.log_k = nn.Parameter(torch.tensor([k])) + self.embed = NumberEmbedder(d, hidden) self.head = nn.Sequential( - nn.Linear(2 * d + 1, 16), + nn.Linear(d, hidden), nn.ReLU(), - nn.Linear(16, 1), + nn.Linear(hidden, hidden), + nn.ReLU(), + nn.Linear(hidden, 1), ) def forward(self, a, b): - ea = self.embed(a) - eb = self.embed(b) - delta_ab = a - b - x = torch.cat([ea, eb, delta_ab], dim=-1) + # trying to force antisym here: h(a,b)=−h(b,a) + phi = self.head(self.embed(a-b)) + phi_neg = self.head(self.embed(b-a)) + logit = phi - phi_neg - return self.head(x) # logits \ No newline at end of file + return (self.log_k ** 2) * logit \ No newline at end of file diff --git a/pairwise_compare.py b/pairwise_compare.py index b96041b..55d1e7f 100755 --- a/pairwise_compare.py +++ b/pairwise_compare.py @@ -13,12 +13,12 @@ import pairwise_comp_nn as comp_nn DEVICE = torch.accelerator.current_accelerator() if torch.accelerator.is_available() else "cpu" # Valves -DIMENSIONS = 1 -TRAIN_STEPS = 10000 +DIMENSIONS = 2 +TRAIN_STEPS = 5000 TRAIN_BATCHSZ = 8192 -TRAIN_PROGRESS = 100 -BATCH_LOWER = -512.0 -BATCH_UPPER = 512.0 +TRAIN_PROGRESS = 10 +BATCH_LOWER = -100.0 +BATCH_UPPER = 100.0 DO_VERBOSE_EARLY_TRAIN = False MODEL_PATH = "./files/pwcomp.model" LOGGING_PATH = "./files/output.log" @@ -46,17 +46,15 @@ def plt_embeddings(model: comp_nn.PairwiseComparator): for i in range(embeddings.shape[1]): plt.plot(xs.squeeze(), embeddings[:, i], label=f"dim {i}") - plt.legend() - plt.savefig(EMBED_CHART_PATH) - plt.show() - + + plt.legend() + plt.savefig(EMBED_CHART_PATH) + #plt.show() csv_data = list(zip(xs.squeeze().tolist(), embeddings.tolist())) with open(file=EMBEDDINGS_DATA, mode="w", newline='') as f: csv_file = csv.writer(f) csv_file.writerows(csv_data) - - def get_torch_info(): log.info("PyTorch Version: %s", torch.__version__) 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 b = (high - low) * torch.rand(batch_size, 1) + low - # train for if a > b - y = (a > b).float() + epsi = 1e-4 + 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 def training_entry(): @@ -150,7 +145,7 @@ def infer_entry(): with torch.no_grad(): 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): log.info(f"P({x} > {y}) = {p.item():.3f}")