stephenway

git commit --amend --author "Stephen Way <way.stephen@gmail.com>" --no-edit && \
git rebase --continue
...
git push origin master --force

mmmikael

from __future__ import absolute_import
from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

import random
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import *
from keras.optimizers import SGD, RMSprop

gabrieleangeletti

import tensorflow as tf
import numpy as np
import os

import zconfig
import utils

class DenoisingAutoencoder(object):

    """ Implementation of Denoising Autoencoders using TensorFlow.

danijar

# Example for my blog post at:
# http://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf


def lazy_property(function):
    attribute = '_' + function.__name__

danijar

# Working example for my blog post at:
# http://danijar.com/variable-sequence-lengths-in-tensorflow/
import functools
import sets
import tensorflow as tf
from tensorflow.models.rnn import rnn_cell
from tensorflow.models.rnn import rnn


def lazy_property(function):

jkleint

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

benwu232

import tensorflow as tf

tf.GraphKeys.USEFUL = 'useful'

v1 = tf.placeholder(tf.float32, name="v1")
v2 = tf.placeholder(tf.float32, name="v2")
v3 = tf.mul(v1, v2)
vx = tf.Variable(10.0, name="vx")
v4 = tf.add(v3, vx, name="v4")

benwu232

import tensorflow as tf

tf.GraphKeys.USEFUL = 'useful'

saver = tf.train.import_meta_graph("./model_ex1.meta")
sess = tf.Session()
saver.restore(sess, "./model_ex1")

var_list = tf.get_collection(tf.GraphKeys.USEFUL)
v1 = var_list[0]

Kaapp

import csv
import hashlib
import re
import numpy as np
import tensorflow as tf
from PIL import Image
from tensorflow.python.util import compat

class DataHandler:

abirkill

#!/usr/bin/env python
import ephem
import math

'''
  Takes two PyEphem objects, one for the sun and one for the satellite, and the
  satellite's standard magnitude value (-1.3 for the International Space Station),
  and calculates the visual magnitude of the satellite.
'''
def calculate_visual_magnitude(sun, satellite, standard_magnitude):