| """ | |
| A keras attention layer that wraps RNN layers. | |
| Based on tensorflows [attention_decoder](https://github.com/tensorflow/tensorflow/blob/c8a45a8e236776bed1d14fd71f3b6755bd63cc58/tensorflow/python/ops/seq2seq.py#L506) | |
| and [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449). | |
| date: 20161101 | |
| author: wassname | |
| url: https://gist.github.com/wassname/5292f95000e409e239b9dc973295327a | |
| """ |
| from keras.layers import Recurrent | |
| import keras.backend as K | |
| from keras import activations | |
| from keras import initializers | |
| from keras import regularizers | |
| from keras import constraints | |
| from keras.engine import Layer | |
| from keras.engine import InputSpec | |
| # coding: utf-8 | |
| from __future__ import division | |
| import struct | |
| import sys | |
| FILE_NAME = "GoogleNews-vectors-negative300.bin" | |
| MAX_VECTORS = 200000 # This script takes a lot of RAM (>2GB for 200K vectors), if you want to use the full 3M embeddings then you probably need to insert the vectors into some kind of database | |
| FLOAT_SIZE = 4 # 32bit float |
##VGG19 model for Keras
This is the Keras model of the 19-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
| """ | |
| Simple example of manually performing "automatic" differentiation | |
| """ | |
| import numpy as np | |
| from numpy import exp, sin, cos | |
| def f(x, with_grad=False): | |
| # Need to cache intermediates from forward pass (might not use all of them). | |
| a = exp(x) |
| # Slightly Modified version of https://gist.github.com/dideler/5219706 | |
| from optparse import OptionParser | |
| import os.path | |
| import re | |
| import os | |
| from sets import Set | |
| regex = re.compile(("([a-z0-9!#$%&'*+\/=?^_`{|}~-]+(?:\.[a-z0-9!#$%&'*+\/=?^_`" | |
| "{|}~-]+)*(@|\sat\s)(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?(\.|" |