Following instructions from the excellent https://www.rinkeby.io/
A full node lets you access all state. There is a light node (state-on-demand) and wallet-only (no state) instructions as well,
Following instructions from the excellent https://www.rinkeby.io/
A full node lets you access all state. There is a light node (state-on-demand) and wallet-only (no state) instructions as well,
def _isotonic_regression(np.ndarray[DOUBLE, ndim=1] y, | |
np.ndarray[DOUBLE, ndim=1] weight, | |
np.ndarray[DOUBLE, ndim=1] solution): | |
cdef: | |
Py_ssize_t current, i | |
unsigned int len_active_set | |
DOUBLE v, w | |
len_active_set = y.shape[0] |
# Alec Radford, Indico, Kyle Kastner | |
# License: MIT | |
""" | |
Convolutional VAE in a single file. | |
Bringing in code from IndicoDataSolutions and Alec Radford (NewMu) | |
Additionally converted to use default conv2d interface instead of explicit cuDNN | |
""" | |
import theano | |
import theano.tensor as T | |
from theano.compat.python2x import OrderedDict |
import numpy as np | |
from keras.models import Sequential | |
from keras.layers import Dense, Dropout, Activation, Flatten | |
from keras.layers import Convolution2D, MaxPooling2D | |
from keras.optimizers import SGD | |
from keras.regularizers import l2, activity_l2 | |
from keras.utils import np_utils | |
from sklearn import metrics | |
# to run this code, you'll need to load the following data: |
Latency Comparison Numbers | |
-------------------------- | |
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 |