LMescheder

sjchoi86

import math
import scipy.io
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import tensorflow as tf

# PLOT3 IN PYTHON
def plot3(a, b, c, mark=".", col="b", title=""):

emschwartz

1st ETH -> XRP Interledger Escrow Payment

Here are the transaction IDs (and links to the block explorers) for the first livenet Ethereum and Ripple Consensus Ledger (RCL) escrowed transfers comprising an Interledger payment.

1. Ethereum prepare: 0x00cbb6149b9cfb3cedf280251c3060b2a38776fa7792b578b6f9f39ce5ee0266
2. RCL prepare: 7F0A5F16C84568D96DA6A66058CD9EAA881236237642BF7427A458957A752B6B
3. RCL fulfill: 12A4CAFAE95254844513C5C11488A1195C08DEFF673C97AC74AAC121935DDE36
4. Ethereum fulfill: 0xb59dd839ab0b5e7d4e663b7cfc0ddb70eaf73dd2785b3d3a4abdf1a61817007d

SHA-256 Condition: `d2

poolio

fhuszar

fhuszar

siemanko

"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation:

tomokishii

#
#  mnist_cnn_bn.py   date. 5/21/2016
#                    date. 6/2/2017 check TF 1.1 compatibility
#

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

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):