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Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures

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import numpy
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in

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"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

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"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer.

Authors: Fabian Pedregosa <fabian@fseoane.net>
         Alexandre Gramfort <alexandre.gramfort@inria.fr>
"""

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import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

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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

mehdidc

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

mehdidc

Code Complete by Steve McConnell

Jeff Atwood (Coding Horror)
https://blog.codinghorror.com/code-reviews-just-do-it/

Measuring Defect Potentials and Defect Removal Efficiency
http://rbcs-us.com/site/assets/files/1337/measuring-defect-potentials-and-defect-removal-efficiency.pdf

Expectations, Outcomes, and Challenges Of Modern Code Review
https://www.microsoft.com/en-us/research/publication/expectations-outcomes-and-challenges-of-modern-code-review/

mehdidc

Code Complete by Steve McConnell

Jeff Atwood (Coding Horror)
https://blog.codinghorror.com/code-reviews-just-do-it/

Measuring Defect Potentials and Defect Removal Efficiency
http://rbcs-us.com/site/assets/files/1337/measuring-defect-potentials-and-defect-removal-efficiency.pdf

Expectations, Outcomes, and Challenges Of Modern Code Review
https://www.microsoft.com/en-us/research/publication/expectations-outcomes-and-challenges-of-modern-code-review/

mehdidc

import PIL.Image
from cStringIO import StringIO
import IPython.display
import numpy as np
def showarray(a, fmt='png'):
    a = np.uint8(a)
    f = StringIO()
    PIL.Image.fromarray(a).save(f, fmt)
    IPython.display.display(IPython.display.Image(data=f.getvalue()))