rako233

bl_info = {
    "name": "FreeCAD Importer",
    "category": "Import-Export",
    "author": "Yorik van Havre",
    "version": (4, 0, 0),
    "blender": (2, 80, 0),
    "location": "File > Import > FreeCAD",
    "description": "Imports a .FCStd file from FreeCAD",
    "warning": "You need a version of FreeCAD compiled with the same Python version as Blender. Only Part- and Mesh-based objects are supported at the moment",
}

rako233

#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#ifdef _MSC_VER
#include <intrin.h> /* for rdtscp and clflush */
#pragma optimize("gt",on)
#else
#include <x86intrin.h> /* for rdtscp and clflush */
#endif

rako233

"""
Clean and simple Keras implementation of network architectures described in:
    - (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf).
    - (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf).
    
Python 3.
"""

from keras import layers
from keras import models

rako233

"""
When traing ML models on text we usually need to represent words/character in one-hot encoding.
This can be done in preprocessing, however it may make the dataset file bigger. Also when we'd
like to use an Embedding layer, it accepts the original integer indexes instead of one-hot codes.

Can be move the one-hot encoding from pre-preprocessing directly into the model?

If so we could choose from two options: use one-hot inputs or perform embedding.

A way how to do this was suggested in Keras issue [#3680](https://github.com/fchollet/keras/issues/3680).

rako233

import bpy
from bpy.props import IntProperty, FloatProperty, PointerProperty
import nodeitems_utils
from nodeitems_utils import NodeItem, NodeCategory

bl_info = {
  "name": "Custom nodes",
  "category": "Node",
}