| """ | |
| 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) |
| """ | |
| 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) |
##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
| Dependences: | |
| sudo apt-get update | |
| sudo apt-get install build-essential | |
| apt-get install python-dev | |
| sudo pip install -U setuptools | |
| Steps: | |
| download from https://mrjbq7.github.io/ta-lib/install.html |
| # The NN | |
| with tf.device(gpu): | |
| # Input is has numberOfBands for the pre-processed image and numberOfBands for the original image | |
| xy = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 2*numberOfBands]) | |
| with tf.name_scope("split") as scope: | |
| x = tf.slice(xy, [0,0,0,0], [-1,-1,-1,numberOfBands]) # low res image | |
| y = tf.slice(xy, [0,0,0,numberOfBands], [-1,-1,-1,-1]) # high res image | |
| with tf.name_scope("initial_costs") as scope: | |
| # used as a measure of improvement not for optimization |