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

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

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

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

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

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

from sklearn.datasets import load_boston
from sklearn.linear_model import (LinearRegression, Ridge, SGDRegressor,
                                  Lasso, ElasticNetCV)

from sklearn.preprocessing import MinMaxScaler
import numpy as np
#from minepy import MINE

from sklearn.metrics import mean_squared_error
 

kashif

from sklearn.datasets import load_boston
from sklearn.linear_model import (LinearRegression, Ridge, LassoCV, ElasticNetCV,
                                  ElasticNet, Lasso, RandomizedLasso)
from sklearn.feature_selection import RFE, f_regression
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
import numpy as np
import pdb
#from minepy import MINE

kashif

from sklearn.datasets import load_boston
from sklearn.linear_model import (LinearRegression, Ridge, 
                                  Lasso, RandomizedLasso)
from sklearn.feature_selection import RFE, f_regression
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
import numpy as np
#from minepy import MINE

from sklearn.metrics import mean_squared_error

kashif

from __future__ import print_function

from keras.datasets import cifar10
from keras.layers import merge, Input
from keras.layers.convolutional import Convolution2D, ZeroPadding2D, AveragePooling2D
from keras.layers.core import Dense, Activation, Flatten
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils