Architectures / Recommendation

🧮 Neural Collaborative Filtering

He et al. 2017 NCF — user/item embeddings → concat → MLP → score (concat-then-MLP variant)

Layers

Parameters

35.21M

Input

Output

1 × 1

Verifier

Clean

Every number on this page is computed from the graph by the same functions the app runs, not written by hand.

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When to pick it

Pick as the simplest deep CF baseline before reaching for fused (NeuMF) or graph-based variants. No feature engineering required.

Structure

12 layers. Output shapes are propagated from the input shape, batch dimension excluded.

	Layer	Type	Parameters	Output shape
1	User ID	Input	shape=[1]	1
2	User Embedding	Embedding	vocabSize=100000	1 × 32
3	Item ID	Input	shape=[1]	1
4	Item Embedding	Embedding	vocabSize=1000000	1 × 32
5	Concat [u; i]	Concatenate		1 × 64
6	MLP 1	Linear	outFeatures=64, inFeatures=64	1 × 64
7	ReLU 1	ReLU		1 × 64
8	MLP 2	Linear	outFeatures=32, inFeatures=64	1 × 32
9	ReLU 2	ReLU		1 × 32
10	Score Head	Linear	outFeatures=1, inFeatures=32	1 × 1
11	Sigmoid	Sigmoid		1 × 1
12	P(click)	Output		1 × 1

What the verifier says

The same 41 structural checks that run on every edit in the app, on this graph.

info"Sigmoid" feeds directly into Output. PyTorch's nn.CrossEntropyLoss already applies log-softmax internally, an explicit Softmax causes double-application and degrades training stability. Fix: Remove Softmax/Sigmoid for training. Restore it in a separate inference wrapper or ONNX export. (Sigmoid)
output-activation

infoSigmoid saturates to [0,1] / [-1,1], and its gradient approaches zero for large inputs. In networks deeper than 5 layers, this halts learning in early layers. Fix: Use ReLU, GELU, or SiLU for hidden layers. Keep Sigmoid only at binary classification outputs; Tanh in specific contexts (GAN generators, LSTM gates). (Sigmoid)
vanishing-gradient

info9 layers with no BatchNorm, LayerNorm, or GroupNorm. Without normalization, activations can explode or vanish across layers, causing slow or unstable training. Fix: Add BatchNorm after Conv2d (CV tasks), LayerNorm after attention/FFN (NLP/LLM), or GroupNorm for small batch sizes.
deep-no-norm

infoPyTorch initializes Linear/Conv with Kaiming (He) init, which is derived for ReLU-family activations. Feeding a saturating activation (sigmoid/tanh) from a He-initialized layer starts training in the saturated tails, shrinking early gradients. Fix: Initialize these layers with Xavier instead: nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain("sigmoid"|"tanh")), or switch the activation to a ReLU-family one. (Score Head)
init-activation-mismatch

The PyTorch it exports

Generated from the graph above. First 46 lines; the app exports the whole file, plus the training loop, the data contract and a deploy bundle.

# Architecture designed with Neurarch: https://neurarch.com
# PyTorch: compatible with Python 3.8+ and torch>=1.12
# Colab: pip install torch torchvision  (usually pre-installed)

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple

class NeuralCollaborativeFiltering(nn.Module):
    def __init__(self):
        super().__init__()

        self.embedding_1 = nn.Embedding(100000, 32)
        self.embedding_2 = nn.Embedding(1000000, 32)
        self.linear_1 = nn.Linear(64, 64)
        self.linear_2 = nn.Linear(64, 32)
        self.linear_3 = nn.Linear(32, 1)

    def forward(self, src, tgt=None):
        # User ID shape: [1]
        # Item ID shape: [1]
        embedding_er_emb = self.embedding_1(src)
        embedding_em_emb = self.embedding_2(tgt)
        concatenate_concat = torch.cat([embedding_er_emb, embedding_em_emb], dim=-1)
        linear_fc1 = self.linear_1(concatenate_concat)
        relu_act1 = F.relu(linear_fc1)
        linear_fc2 = self.linear_2(relu_act1)
        relu_act2 = F.relu(linear_fc2)
        linear_fc3 = self.linear_3(relu_act2)
        sigmoid_sig = torch.sigmoid(linear_fc3)
        # Output
        return sigmoid_sig


if __name__ == '__main__':
    model = NeuralCollaborativeFiltering()
    model.eval()

    src = torch.randint(0, 1, (1))  # (batch, src_seq_len)
    tgt = torch.randint(0, 1, (1))  # (batch, tgt_seq_len)
    with torch.no_grad():
        output = model(src, tgt)

    print(f'Src shape    : {tuple(src.shape)}')
    print(f'Tgt shape    : {tuple(tgt.shape)}')

For agents

This architecture is machine-readable end to end. An agent can list the set, fetch this graph, edit it, and have the edit verified before any GPU time is spent.

Markdown twin Index JSON This graph as JSON MCP server POST /api/v1/check

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