arnaldog12

from keras.utils.data_utils import Sequence
from imblearn.over_sampling import RandomOverSampler
from imblearn.keras import balanced_batch_generator

class BalancedDataGenerator(Sequence):
    """ImageDataGenerator + RandomOversampling"""
    def __init__(self, x, y, datagen, batch_size=32):
        self.datagen = datagen
        self.batch_size = min(batch_size, x.shape[0])
        datagen.fit(x)

mohan-barathi

# Make uses /bin/sh by default, which is incompatible with the bashisms seen
# below.

# Thanks to T-Troll <issue 29806> and freedomtan <issue 26731>
SHELL := /bin/bash

# Find where we're running from, so we can store generated files here.
ifeq ($(origin MAKEFILE_DIR), undefined)
	MAKEFILE_DIR := $(shell dirname $(realpath $(lastword $(MAKEFILE_LIST))))
endif

arnaldog12

import numpy as np
import _pickle as pkl

# activation functions
def linear(x, derivative=False):
    return np.ones_like(x) if derivative else x

def sigmoid(x, derivative=False):
    if derivative:
        y = sigmoid(x)

tokestermw

""" 
Play with saving .

Closest:
    https://github.com/tensorflow/tensorflow/issues/616#issuecomment-205620223
"""
import numpy as np

import tensorflow as tf
from tensorflow.python.platform import gfile

omimo

# Create a simple TF Graph 
# By Omid Alemi - Jan 2017
# Works with TF <r1.0

import tensorflow as tf

I = tf.placeholder(tf.float32, shape=[None,3], name='I') # input
W = tf.Variable(tf.zeros_initializer(shape=[3,2]), dtype=tf.float32, name='W') # weights
b = tf.Variable(tf.zeros_initializer(shape=[2]), dtype=tf.float32, name='b') # biases
O = tf.nn.relu(tf.matmul(I, W) + b, name='O') # activation / output

ricgu8086

import numpy as np
from keras.models import Sequential
from keras.layers.core import Activation, Dense
from keras.optimizers import SGD

X = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
y = np.array([[0],[1],[1],[0]], "float32")

model = Sequential()
model.add(Dense(2, input_dim=2, activation='sigmoid'))

mblondel

"""
Multiclass SVMs (Crammer-Singer formulation).

A pure Python re-implementation of:

Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex.
Mathieu Blondel, Akinori Fujino, and Naonori Ueda.
ICPR 2014.
http://www.mblondel.org/publications/mblondel-icpr2014.pdf
"""

Kartones

PSQL

Magic words:

psql -U postgres

Some interesting flags (to see all, use -h or --help depending on your psql version):

• -E: will describe the underlaying queries of the \ commands (cool for learning!)
• -l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)

mblondel

# Mathieu Blondel, September 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)