kashif

import torch
import torch.nn as nn


class EvoNorm2d(nn.Module):
    __constants__ = ['num_features', 'eps', 'nonlinearity']
    
    def __init__(self, num_features, eps=1e-5, nonlinearity=True):
        super(EvoNorm2d, self).__init__()
        

kashif

GeoDatabases

What is a GeoDatabase? and how is it diff from a regular db

• Stores geometries
• multi-dimensional
• R-Tree indexing (Query planner uses it?)
• projections
• Supports Datums ?
• Vector and raster support

kashif

class AMSgrad(Optimizer):
    """AMSGrad optimizer.

    Default parameters follow those provided in the Adam paper.

    # Arguments
        lr: float >= 0. Learning rate.
        beta_1: float, 0 < beta < 1. Generally close to 1.
        beta_2: float, 0 < beta < 1. Generally close to 1.
        epsilon: float >= 0. Fuzz factor.

kashif

def input_fn(file_pattern, labels,
             image_size=(224,224),
             shuffle=False,
             batch_size=64, 
             num_epochs=None, 
             buffer_size=4096,
             prefetch_buffer_size=None):

    table = tf.contrib.lookup.index_table_from_tensor(mapping=tf.constant(labels))
    num_classes = len(labels)

kashif

bash -c '
<% if knife_config[:bootstrap_proxy] -%>
(
cat <<'EOP'
<%= "proxy = #{knife_config[:bootstrap_proxy]}" %>
EOP
) > ~/.curlrc
<% end -%>

if [ ! -f /usr/bin/chef-client ]; then

kashif

class AccSGD(Optimizer):
    """AccSGD optimizer.

    Arguments:
        lr (float): learning rate
        kappa (float, optional): ratio of long to short step (default: 1000)
        xi (float, optional): statistical advantage parameter (default: 10)
        smallConst (float, optional): any value <=1 (default: 0.7)

    # References

kashif

import numpy as np
import gym
from gym.spaces import Discrete, Box
from gym.wrappers import Monitor
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten


# ================================================================
# Policies

kashif

name: "autocolorize"
input: "data"
input_dim: 1
input_dim: 1
input_dim: 514
input_dim: 514
layer {
  name: "data"
  type: "Input"
  top: "data"

kashif

• a short description of your company and what you do/offer
• a detailed description of the students tasks/project
• requirements (knowledge of, e. g. Spatial DB course, programming skills)
• task/project plans (what will they do?)
• what are the students going to work with/learn:
• programming languages
• infrastructure (versioning system, build system)

kashif

========================================================================
   BYTE UNIX Benchmarks (Version 5.1.3)

   System: europe.spacialdb.com: GNU/Linux
   OS: GNU/Linux -- 3.0.0-12-server -- #20-Ubuntu SMP Fri Oct 7 16:36:30 UTC 2011
   Machine: x86_64 (x86_64)
   Language: en_US.utf8 (charmap="UTF-8", collate="UTF-8")
   CPU 0: Intel(R) Core(TM) i7 CPU 920 @ 2.67GHz (5346.4 bogomips)
          Hyper-Threading, x86-64, MMX, Physical Address Ext, SYSENTER/SYSEXIT, SYSCALL/SYSRET, Intel virtualization
   CPU 1: Intel(R) Core(TM) i7 CPU 920 @ 2.67GHz (5346.4 bogomips)