| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
This configuration worked for me, hope it helps
It is based on: https://becominghuman.ai/deep-learning-gaming-build-with-nvidia-titan-xp-and-macbook-pro-with-thunderbolt2-5ceee7167f8b
and on: https://stackoverflow.com/questions/44744737/tensorflow-mac-os-gpu-support
| #include <time.h> // Robert Nystrom | |
| #include <stdio.h> // @munificentbob | |
| #include <stdlib.h> // for Ginny | |
| #define r return // 2008-2019 | |
| #define l(a, b, c, d) for (i y=a;y\ | |
| <b; y++) for (int x = c; x < d; x++) | |
| typedef int i;const i H=40;const i W | |
| =80;i m[40][80];i g(i x){r rand()%x; | |
| }void cave(i s){i w=g(10)+5;i h=g(6) | |
| +3;i t=g(W-w-2)+1;i u=g(H-h-2)+1;l(u |
| $ python xor.py | |
| Training: | |
| Epoch 0 MSE: 1.765 | |
| Epoch 100 MSE: 0.015 | |
| Epoch 200 MSE: 0.005 | |
| * Target MSE reached * | |
| Evaluating: | |
| 1 XOR 0 = 1 ( 0.904) Error: 0.096 | |
| 0 XOR 1 = 1 ( 0.908) Error: 0.092 | |
| 1 XOR 1 = 0 (-0.008) Error: 0.008 |
