| """ | |
| 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 is free and unencumbered software released into the public domain. | |
| Anyone is free to copy, modify, publish, use, compile, sell, or | |
| distribute this software, either in source code form or as a compiled | |
| binary, for any purpose, commercial or non-commercial, and by any | |
| means. | |
| In jurisdictions that recognize copyright laws, the author or authors | |
| of this software dedicate any and all copyright interest in the | |
| software to the public domain. We make this dedication for the benefit |
| #!/bin/bash | |
| ### steps #### | |
| # Verify the system has a cuda-capable gpu | |
| # Download and install the nvidia cuda toolkit and cudnn | |
| # Setup environmental variables | |
| # Verify the installation | |
| ### | |
| ### to verify your gpu is cuda enable check |
Current version: 1.0.19 1.0.15 (as of 2018-12-10)
| raise ValueError("DEPRECATED/FROZEN - see https://github.com/kastnerkyle/tools for the latest") | |
| # License: BSD 3-clause | |
| # Authors: Kyle Kastner | |
| # Harvest, Cheaptrick, D4C, WORLD routines based on MATLAB code from M. Morise | |
| # http://ml.cs.yamanashi.ac.jp/world/english/ | |
| # MGC code based on r9y9 (Ryuichi Yamamoto) MelGeneralizedCepstrums.jl | |
| # Pieces also adapted from SPTK | |
| from __future__ import division | |
| import numpy as np |
| ffprobe -v quiet -print_format json -show_format -show_streams "lolwut.mp4" > "lolwut.mp4.json" |
| from contextlib import contextmanager | |
| import numpy as np | |
| import torch | |
| from torch import Tensor, ByteTensor | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| import pycuda.driver | |
| from pycuda.gl import graphics_map_flags | |
| from glumpy import app, gloo, gl |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman