🍋 laifi

Cursor's Memory Bank

I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.

Memory Bank Structure

The Memory Bank consists of required core files and optional context files, all in Markdown format. Files build upon each other in a clear hierarchy:

flowchart TD

Keras Models Examples

一系列常用模型的Keras实现

DNN

Multilayer Perceptron (MLP) for multi-class softmax classification

from keras.models import Sequential

	#!/usr/bin/env python3

	# Copyright (c) Facebook, Inc. and its affiliates.
	# All rights reserved.
	#
	# This source code is licensed under the BSD-style license found in the
	# LICENSE.txt file in the root directory of this source tree.


	def get_torchbiggraph_config():

	# coding=utf-8
	# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software

	def cosine_distance(x1, x2=None, eps=1e-8):
	x2 = x1 if x2 is None else x2
	w1 = x1.norm(p=2, dim=1, keepdim=True)
	w2 = w1 if x2 is x1 else x2.norm(p=2, dim=1, keepdim=True)
	return 1 - torch.mm(x1, x2.t()) / (w1 * w2.t()).clamp(min=eps)