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View gist:ecf50d3730103beb9c30a306fd70d158
# use the same data augmentation scheme as the FlowNet2 paper
layer {
name: "img0"
type: "CustomData"
top: "img0"
top: "img1"
top: "flow_gt"
top: "aux"
include {
phase: TRAIN
View numpy-format-proposal-v1.md
View mnist.py
from __future__ import division, print_function, absolute_import
import os
import struct
from array import array
import numpy as np
def load_mnist(section="training", offset=0, count=None, ret='xy',
x_dtype=np.float64, y_dtype=np.int64, path=None):
"""
View MNIST loader
from __future__ import division, print_function, absolute_import
import os
import struct
from array import array
import numpy as np
def load_mnist(section="training", offset=0, count=None, ret='xy',
x_dtype=np.float64, y_dtype=np.int64, path=None):
"""
View gist:9499068
# Author: Nelle Varoquaux, Andrew Tulloch
# Uses the pool adjacent violators algorithm (PAVA), with the
# enhancement of searching for the longest decreasing subsequence to
# pool at each step.
import numpy as np
cimport numpy as np
cimport cython