jetnew

from keras.layers import LSTM, Dense, RepeatVector, TimeDistributed
from keras.models import Sequential

class LSTM_Autoencoder:
  def __init__(self, optimizer='adam', loss='mse'):
    self.optimizer = optimizer
    self.loss = loss
    self.n_features = 1
    
  def build_model(self):

jintao-zero

#!/usr/bin/env python
# -*- coding: UTF-8 -*-

import sys
import urllib
import urllib2
from lxml import etree
import time
import random
import logging

ajbrock

import theano
import lasagne.layers
from lasagne.layers import Conv2DLayer as C2D
from lasagne.nonlinearities import rectify as relu
from lasagne.layers import NonlinearityLayer as NL
from lasagne.layers import ElemwiseSumLayer as ESL
from lasagne.layers import batch_norm as BN

l_in = lasagne.layers.InputLayer(shape=(None,3,64,64)) # Assume incoming shape is a batch x RGB x W x H image
encoder_stem = C2D(

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD