ilblackdragon

import logging

import numpy as np
import tensorflow as tf
from tensorflow.contrib import layers


GO_TOKEN = 0
END_TOKEN = 1
UNK_TOKEN = 2

marekgalovic

import tensorflow as tf
import numpy as np

class ConvolutionalAttentionNLI(object):
    
    def __init__(self, embeddings_shape, target_classes=2, conv_filter_size=3, conv_projection_size=300, attention_output_size=200, comparison_output_size=100, learning_rate=0.05):
        self._embeddings_shape = embeddings_shape
        self._target_classes = target_classes
        self._conv_filter_size = conv_filter_size
        self._conv_projection_size = conv_projection_size

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

farizrahman4u

import numpy as np

__author__ = 'Fariz Rahman'


def eq(x, y):
	return x.lower().replace(" ", "") == y.lower().replace(" ", "")

def get_words(x):
	x = x.replace("  ", " ")

fchollet

'''Functional Keras is a more functional replacement for the Graph API.
'''

###################
# 2 LSTM branches #
###################
a = Input(input_shape=(10, 32))  # output is a TF/TH placeholder, augmented with Keras attributes
b = Input(input_shape=(10, 32))
encoded_a = LSTM(32)(a)  # output is a TF/TH tensor
encoded_b = LSTM(32)(b)

DSA101

# Time series forecasting based on multiple time series, including the original one
# This script is based on the following examples and discussions:
# https://gist.github.com/lukovkin/1aefa4509e066690b892
# https://groups.google.com/forum/#!topic/keras-users/9GsDwkSdqBg

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
import theano

viksit

#!/usr/bin/env python
# see http://matpalm.com/blog/2015/03/28/theano_word_embeddings/
import theano
import theano.tensor as T
import numpy as np
import random

E = np.asarray(np.random.randn(6, 2), dtype='float32')
t_E = theano.shared(E)
t_idxs = T.ivector()

hnykda

import pandas as pd  
from random import random

flow = (list(range(1,10,1)) + list(range(10,1,-1)))*100
pdata = pd.DataFrame({"a":flow, "b":flow})  
pdata.b = pdata.b.shift(9)  
data = pdata.iloc[10:] * random()  # some noise  

import numpy as np

ognis1205

import numpy
import sys
import scipy.stats as stats
import matplotlib.pyplot as plotter
from gensim import corpora, models, similarities, matutils


# Defines dictionary from the specified corpus.
dictionary = corpora.Dictionary(
    line.lower().split() for line in open('corpus_train.txt', 'rb')

tomazzaman

check process hhvm with pidfile /var/run/hhvm/pid
  group hhvm
  start program = "/usr/sbin/service hhvm start" with timeout 60 seconds
  stop program  = "/usr/sbin/service hhvm stop"
  if failed unixsocket /var/run/hhvm/hhvm.sock then restart
  if mem > 400.0 MB for 1 cycles then restart
  if 5 restarts with 5 cycles then timeout