tomis9's cookbook

Python

learning tensorflow

Aug 5, 2019 589 minutes read

The best way to gain intuiton to any new thing you learn is to start from a very beginning and play with it (let’s see what happens if I do this). That’s the power of reinforcement learning ;)

These packages will be useful in the nearest future:

import numpy as np
from sklearn.datasets import load_iris
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import accuracy_score

import tensorflow as tf

## /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   _np_qint8 = np.dtype([("qint8", np.int8, 1)])
## /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
## /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   _np_qint16 = np.dtype([("qint16", np.int16, 1)])
## /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
## /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   _np_qint32 = np.dtype([("qint32", np.int32, 1)])
## /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   np_resource = np.dtype([("resource", np.ubyte, 1)])
## /usr/local/lib/python3.5/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   _np_qint8 = np.dtype([("qint8", np.int8, 1)])
## /usr/local/lib/python3.5/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
## /usr/local/lib/python3.5/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   _np_qint16 = np.dtype([("qint16", np.int16, 1)])
## /usr/local/lib/python3.5/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
## /usr/local/lib/python3.5/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   _np_qint32 = np.dtype([("qint32", np.int32, 1)])
## /usr/local/lib/python3.5/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
##   np_resource = np.dtype([("resource", np.ubyte, 1)])

tf.logging.set_verbosity(tf.logging.ERROR)  # ignore warnings

A trivial example of tensorflow:

import tensorflow as tf

a = tf.Variable(10, name='a')  # a variable
b = tf.Variable(12, name='b')  # another variable

s = a + b  # a tensor

sess = tf.Session()
sess.run(a.initializer)
sess.run(b.initializer)
print(sess.run(s))

## 22

As you can see, one does not simply add two numbers in tensorflow.

lesson #1: you cannot initialize a tensor

sess = tf.Session()
sess.run(a.initializer)
sess.run(b.initializer)
# print(s.eval())  # does not work - you eval() is not connected to the session
# anyhow
print(sess.run(s))

## 22

lesson #2: eval does not recognize session by itself

with tf.Session() as sess:
    sess.run(a.initializer)
    sess.run(b.initializer)
    print(s.eval())  # does work - eval recognizes a default session

## 22

lesson #2: eval works in with clause
lesson #3: so far there are 2 ways to initialize a variable in a session

init = tf.global_variables_initializer()

with tf.Session() as sess:
    init.run()
    print(s.eval())

## 22

lesson #4: the third and most compact way to initialize variables - all the variables in one statement

s1 = tf.add(a, b)  # a tensor, not variable
with tf.Session() as sess:
    init.run()
    print(s1.eval())

## 22

lesson #5: tensorflow has it’s own mathematical functions

c = tf.Variable(np.array([[1, 2], [3, 4]]), name='c')
d = tf.Variable(np.array([[5, 6], [7, 8]]), name='d')

m = tf.matmul(c, d)  # a tensor again

init = tf.global_variables_initializer()
with tf.Session() as sess:
    init.run()
    print(m.eval())

## [[19 22]
##  [43 50]]

lesson #6: tensorflow can interpret numpy arrays as matrices
lesson #7: you can multiply matrices!

iris = load_iris()
data = iris.data
y = tf.Variable(data[:, 0].reshape(150, 1), name='y')
x0 = np.ones(150).reshape(150, 1)
x0_X = np.concatenate((x0, data[:, 1:]), axis=1)
X = tf.Variable(x0_X, name='X')

cov = tf.matmul(tf.transpose(X), X, name='cov')
inv_cov = tf.matrix_inverse(cov, name='inv_cov')
xy = tf.matmul(tf.transpose(X), y, name='xy')
beta = tf.matmul(inv_cov, xy)

init = tf.global_variables_initializer()
with tf.Session() as sess:
    init.run()
    print(beta.eval())

## [[ 1.85599749]
##  [ 0.65083716]
##  [ 0.70913196]
##  [-0.55648266]]

lr = LinearRegression()
lr.fit(data[:, 1:], data[:, 0])

## LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

print(np.concatenate((np.array([lr.intercept_]), lr.coef_)))

## [ 1.85599749  0.65083716  0.70913196 -0.55648266]

lesson #8: when creating a tensorflow vector, ou have to provide information if it’s horizontal or mathematical, just like in mathematics lesson #9: tensorflow gives the same results as sklearn (linear regression)

the code above looks quite like a mess, let’s clear it up

def get_data(tensorflow=True):
    iris = load_iris()
    data = iris.data
    y = data[:, 0].reshape(150, 1)
    x0 = np.ones(150).reshape(150, 1)
    X = np.concatenate((x0, data[:, 1:]), axis=1)
    if tensorflow:
        y = tf.constant(y, name='y')
        X = tf.constant(X, name='X')  # constant is a tensor
    return X, y


def construct_beta_graph(X, y):
    cov = tf.matmul(tf.transpose(X), X, name='cov')
    inv_cov = tf.matrix_inverse(cov, name='inv_cov')
    xy = tf.matmul(tf.transpose(X), y, name='xy')
    beta = tf.matmul(inv_cov, xy, name='beta')
    return beta


X, y = get_data()
beta = construct_beta_graph(X, y)
mse = tf.reduce_mean(tf.square(y - tf.matmul(X, beta)))

init = tf.global_variables_initializer()
with tf.Session() as sess:
    init.run()
    print(beta.eval())
    print(mse.eval())

## [[ 1.85599749]
##  [ 0.65083716]
##  [ 0.70913196]
##  [-0.55648266]]
## 0.09630269942460729

lesson #10: you can easily divide your code into modules to make it easier to read lesson #11: when dealing with input data, you can use tf.constant instead of tf.Variable, as the data never changes; constant is a tensor

X, y = get_data()
learning_rate = 0.0001

beta = tf.Variable(np.random.rand(4).reshape(4, 1))
gradient = tf.matmul(tf.transpose(X), tf.matmul(X, beta) - y)
new_beta = beta - learning_rate * gradient

mse_old = tf.reduce_mean(tf.square(y - tf.matmul(X, beta)))
mse_new = tf.reduce_mean(tf.square(y - tf.matmul(X, new_beta)))

init = tf.global_variables_initializer()
with tf.Session() as sess:
    init.run()
    print(beta.eval())
    print(new_beta.eval())
    print(mse_old.eval())
    print(mse_new.eval())

## [[0.94580898]
##  [0.65286533]
##  [0.69996579]
##  [0.19802118]]
## [[0.94631197]
##  [0.65571986]
##  [0.68772621]
##  [0.19227332]]
## 0.4080771193870396
## 0.38517602772066656

lesson #12: you can calculate the gradient of mse pretty simply on a piece of paper

X, y = get_data()
learning_rate = 0.01

beta = tf.Variable(np.random.rand(4).reshape(4, 1))
gradient = 2 / 150 * tf.matmul(tf.transpose(X), tf.matmul(X, beta) - y)

_training = tf.assign(beta, beta - learning_rate * gradient)

mse = tf.reduce_mean(tf.square(y - tf.matmul(X, beta)))

init = tf.global_variables_initializer()
with tf.Session() as sess:
    init.run()
    for i in range(100):
        _training.eval()
        print(mse.eval())
    print(beta.eval())

## array([[0.49052556],
##        [0.3645446 ],
##        [0.6686078 ],
##        [0.69335398]])
## 1.7662767668570791
## array([[0.50840721],
##        [0.42305024],
##        [0.70427084],
##        [0.70072483]])
## 1.37855062500962
## array([[0.51949658],
##        [0.46082013],
##        [0.71239398],
##        [0.69908781]])
## 1.24104970529489
## array([[0.52748338],
##        [0.48901116],
##        [0.70864429],
##        [0.69363088]])
## 1.156591496666379
## array([[0.53399938],
##        [0.5125613 ],
##        [0.69992456],
##        [0.68663759]])
## 1.0864860618268983
## array([[0.53976816],
##        [0.53366408],
##        [0.68927088],
##        [0.67911022]])
## 1.0229197490454076
## array([[0.5451125 ],
##        [0.55330156],
##        [0.67801363],
##        [0.67148588]])
## 0.9641095473908924
## array([[0.55017816],
##        [0.57192076],
##        [0.66673091],
##        [0.66395254]])
## 0.9094647938667378
## array([[0.55503271],
##        [0.58973217],
##        [0.65566921],
##        [0.65658838]])
## 0.8586439807356123
## array([[0.5597091 ],
##        [0.60684141],
##        [0.64492902],
##        [0.6494233 ]])
## 0.8113699451623304
## array([[0.56422489],
##        [0.62330742],
##        [0.63454668],
##        [0.64246611]])
## 0.7673927829654996
## array([[0.56859074],
##        [0.63916805],
##        [0.62453056],
##        [0.63571642]])
## 0.726481598549213
## array([[0.57281416],
##        [0.65445146],
##        [0.61487689],
##        [0.62916995]])
## 0.6884219406593147
## array([[0.57690118],
##        [0.66918112],
##        [0.60557689],
##        [0.62282088]])
## 0.6530144120473861
## array([[0.58085707],
##        [0.68337804],
##        [0.59661976],
##        [0.61666279]])
## 0.6200735699836035
## array([[0.58468668],
##        [0.69706174],
##        [0.58799408],
##        [0.61068919]])
## 0.5894269413483544
## array([[0.58839458],
##        [0.71025075],
##        [0.5796884 ],
##        [0.60489366]])
## 0.5609141139338415
## array([[0.59198513],
##        [0.72296281],
##        [0.57169146],
##        [0.59926994]])
## 0.5343858927167716
## array([[0.59546251],
##        [0.73521493],
##        [0.56399234],
##        [0.59381196]])
## 0.5097035154083924
## array([[0.59883075],
##        [0.74702352],
##        [0.55658046],
##        [0.58851386]])
## 0.48673792291281465
## array([[0.60209373],
##        [0.75840439],
##        [0.54944558],
##        [0.58336999]])
## 0.465369080810452
## array([[0.60525519],
##        [0.76937276],
##        [0.54257785],
##        [0.57837489]])
## 0.4454853482900649
## array([[0.60831874],
##        [0.77994333],
##        [0.53596774],
##        [0.57352329]])
## 0.4269828912098176
## array([[0.61128785],
##        [0.79013026],
##        [0.52960608],
##        [0.56881011]])
## 0.40976513620122645
## array([[0.61416587],
##        [0.79994722],
##        [0.52348401],
##        [0.56423046]])
## 0.3937422629459333
## array([[0.61695605],
##        [0.80940738],
##        [0.51759299],
##        [0.55977961]])
## 0.3788307319559861
## array([[0.6196615 ],
##        [0.81852345],
##        [0.51192478],
##        [0.55545301]])
## 0.36495284537497164
## array([[0.62228522],
##        [0.82730768],
##        [0.50647145],
##        [0.55124627]])
## 0.3520363384909623
## array([[0.62483012],
##        [0.83577189],
##        [0.50122532],
##        [0.54715514]])
## 0.34001399981368335
## array([[0.627299  ],
##        [0.84392747],
##        [0.49617902],
##        [0.54317554]])
## 0.3288233177185059
## array([[0.62969455],
##        [0.85178543],
##        [0.49132542],
##        [0.53930353]])
## 0.3184061517995217
## array([[0.63201937],
##        [0.85935635],
##        [0.48665764],
##        [0.5355353 ]])
## 0.3087084272038749
## array([[0.63427598],
##        [0.86665046],
##        [0.48216907],
##        [0.5318672 ]])
## 0.2996798503403407
## array([[0.63646679],
##        [0.87367762],
##        [0.47785331],
##        [0.52829569]])
## 0.29127364446751974
## array([[0.63859414],
##        [0.88044734],
##        [0.47370419],
##        [0.52481736]])
## 0.28344630377152297
## array([[0.64066028],
##        [0.88696878],
##        [0.46971578],
##        [0.52142892]])
## 0.27615736464024165
## array([[0.64266737],
##        [0.89325079],
##        [0.46588235],
##        [0.5181272 ]])
## 0.2693691929316914
## array([[0.64461752],
##        [0.8993019 ],
##        [0.46219836],
##        [0.51490914]])
## 0.2630467861180224
## array([[0.64651274],
##        [0.90513033],
##        [0.45865848],
##        [0.5117718 ]])
## 0.2571575892649782
## array([[0.64835498],
##        [0.91074402],
##        [0.45525758],
##        [0.50871233]])
## 0.25167132387934116
## array([[0.65014611],
##        [0.9161506 ],
##        [0.45199069],
##        [0.50572798]])
## 0.24655982872454107
## array([[0.65188795],
##        [0.92135747],
##        [0.44885303],
##        [0.50281611]])
## 0.24179691176753304
## array([[0.65358225],
##        [0.92637173],
##        [0.44583999],
##        [0.49997418]])
## 0.23735821247856823
## array([[0.65523068],
##        [0.93120025],
##        [0.44294713],
##        [0.49719971]])
## 0.23322107375991394
## array([[0.65683487],
##        [0.93584964],
##        [0.44017015],
##        [0.49449034]])
## 0.22936442283019795
## array([[0.65839639],
##        [0.94032629],
##        [0.43750492],
##        [0.49184378]])
## 0.22576866043814003
## array([[0.65991675],
##        [0.94463634],
##        [0.43494745],
##        [0.48925782]])
## 0.22241555782322317
## array([[0.6613974 ],
##        [0.94878574],
##        [0.43249389],
##        [0.48673034]])
## 0.2192881608815812
## array([[0.66283975],
##        [0.95278021],
##        [0.43014053],
##        [0.48425927]])
## 0.21637070103327125
## array([[0.66424516],
##        [0.95662527],
##        [0.4278838 ],
##        [0.48184265]])
## 0.2136485123223162
## array([[0.66561494],
##        [0.96032623],
##        [0.42572024],
##        [0.47947855]])
## 0.211107954313686
## array([[0.66695033],
##        [0.96388825],
##        [0.42364652],
##        [0.47716515]])
## 0.20873634038185124
## array([[0.66825256],
##        [0.96731626],
##        [0.42165944],
##        [0.47490067]])
## 0.20652187101389435
## array([[0.66952281],
##        [0.97061504],
##        [0.4197559 ],
##        [0.47268338]])
## 0.20445357177652806
## array([[0.67076219],
##        [0.97378921],
##        [0.41793291],
##        [0.47051165]])
## 0.20252123562088542
## array([[0.67197181],
##        [0.97684319],
##        [0.41618761],
##        [0.46838387]])
## 0.20071536922175706
## array([[0.6731527 ],
##        [0.97978127],
##        [0.4145172 ],
##        [0.46629852]])
## 0.19902714306915797
## array([[0.6743059 ],
##        [0.98260759],
##        [0.41291902],
##        [0.4642541 ]])
## 0.19744834504983488
## array([[0.67543237],
##        [0.98532612],
##        [0.41139047],
##        [0.4622492 ]])
## 0.19597133727467597
## array([[0.67653305],
##        [0.98794071],
##        [0.40992908],
##        [0.46028243]])
## 0.1945890159250429
## array([[0.67760886],
##        [0.99045507],
##        [0.40853244],
##        [0.45835247]])
## 0.19329477390692573
## array([[0.67866068],
##        [0.99287276],
##        [0.40719823],
##        [0.45645802]])
## 0.19208246611657542
## array([[0.67968935],
##        [0.99519723],
##        [0.40592422],
##        [0.45459787]])
## 0.1909463771350025
## array([[0.68069568],
##        [0.99743182],
##        [0.40470826],
##        [0.45277081]])
## 0.1898811911814985
## array([[0.68168047],
##        [0.99957971],
##        [0.40354827],
##        [0.4509757 ]])
## 0.1888819641682149
## array([[0.68264447],
##        [1.001644  ],
##        [0.40244224],
##        [0.44921142]])
## 0.18794409770887743
## array([[0.68358841],
##        [1.00362768],
##        [0.40138824],
##        [0.44747691]])
## 0.18706331494498962
## array([[0.684513  ],
##        [1.00553361],
##        [0.40038442],
##        [0.44577113]])
## 0.18623563806243604
## array([[0.68541893],
##        [1.00736457],
##        [0.39942896],
##        [0.4440931 ]])
## 0.18545736738027782
## array([[0.68630683],
##        [1.00912324],
##        [0.39852015],
##        [0.44244185]])
## 0.18472506190180427
## array([[0.68717736],
##        [1.01081219],
##        [0.39765631],
##        [0.44081646]])
## 0.1840355212255889
## array([[0.68803112],
##        [1.01243391],
##        [0.39683582],
##        [0.43921603]])
## 0.1833857687214484
## array([[0.6888687 ],
##        [1.01399079],
##        [0.39605714],
##        [0.4376397 ]])
## 0.18277303588285496
## array([[0.68969066],
##        [1.01548516],
##        [0.39531876],
##        [0.43608665]])
## 0.18219474777353487
## array([[0.69049756],
##        [1.01691923],
##        [0.39461924],
##        [0.43455608]])
## 0.1816485094917415
## array([[0.69128992],
##        [1.01829517],
##        [0.39395718],
##        [0.4330472 ]])
## 0.18113209358103982
## array([[0.69206825],
##        [1.01961504],
##        [0.39333124],
##        [0.43155929]])
## 0.18064342832141456
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## 0.1801805868391455
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## 0.17514322959078243
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## 0.174925165426651
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## 0.1747142444922135
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## 0.1745099805035938
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## 0.17431192115933986
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## 0.17411964576417596
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## 0.17393276301891702
## [[0.70703636]
##  [1.03831925]
##  [0.38642735]
##  [0.40175037]]

lesson #13: tf.assign - assign one value to another, training tensor is only technical, so we could point that the assignment should be made in every iteration

let’s clear the code a little bit

learning_rate = 0.01
n_iter = 1000

X, y = get_data()
beta = tf.Variable(np.random.rand(4).reshape(4, 1))
gradient = 2 / 150 * tf.matmul(tf.transpose(X), tf.matmul(X, beta) - y)

_training = tf.assign(beta, beta - learning_rate * gradient)

mse = tf.reduce_mean(tf.square(y - tf.matmul(X, beta)))

init = tf.global_variables_initializer()
with tf.Session() as sess:
    init.run()
    for i in range(n_iter):
        _training.eval()
        if not i % 100:
            print(mse.eval())
    print(mse.eval())
    print(beta.eval())

## array([[0.53045532],
##        [0.63311981],
##        [0.4411504 ],
##        [0.83415131]])
## 0.9952767555831649
## array([[0.54483434],
##        [0.67875364],
##        [0.47586734],
##        [0.84219982]])
## array([[0.55333304],
##        [0.70648397],
##        [0.48642841],
##        [0.84231964]])
## array([[0.55916951],
##        [0.72603974],
##        [0.48650949],
##        [0.83904035]])
## array([[0.56376604],
##        [0.74172342],
##        [0.48213836],
##        [0.83435699]])
## array([[0.56775251],
##        [0.7554428 ],
##        [0.47596849],
##        [0.82914643]])
## array([[0.57140906],
##        [0.76804874],
##        [0.46916476],
##        [0.8237923 ]])
## array([[0.57486147],
##        [0.77992547],
##        [0.46223543],
##        [0.81846073]])
## array([[0.57816731],
##        [0.79125097],
##        [0.45539929],
##        [0.81322195]])
## array([[0.58135397],
##        [0.80211196],
##        [0.44874755],
##        [0.80810405]])
## array([[0.5844355 ],
##        [0.81255458],
##        [0.44231536],
##        [0.80311659]])
## array([[0.58741994],
##        [0.82260678],
##        [0.43611331],
##        [0.79826105]])
## array([[0.59031265],
##        [0.83228823],
##        [0.43014131],
##        [0.79353546]])
## array([[0.59311772],
##        [0.84161468],
##        [0.42439474],
##        [0.78893643]])
## array([[0.59583864],
##        [0.85059989],
##        [0.41886714],
##        [0.78446001]])
## array([[0.59847855],
##        [0.85925651],
##        [0.4135514 ],
##        [0.7801021 ]])
## array([[0.6010404 ],
##        [0.86759646],
##        [0.40844028],
##        [0.77585861]])
## array([[0.60352699],
##        [0.87563111],
##        [0.40352662],
##        [0.77172555]])
## array([[0.60594101],
##        [0.88337138],
##        [0.39880342],
##        [0.76769906]])
## array([[0.60828504],
##        [0.89082779],
##        [0.39426393],
##        [0.76377536]])
## array([[0.61056155],
##        [0.89801043],
##        [0.38990161],
##        [0.75995086]])
## array([[0.61277295],
##        [0.90492907],
##        [0.38571013],
##        [0.75622204]])
## array([[0.61492154],
##        [0.91159312],
##        [0.38168341],
##        [0.75258554]])
## array([[0.61700955],
##        [0.91801165],
##        [0.37781557],
##        [0.74903812]])
## array([[0.61903913],
##        [0.92419339],
##        [0.37410093],
##        [0.74557662]])
## array([[0.62101234],
##        [0.93014679],
##        [0.37053404],
##        [0.74219803]])
## array([[0.62293119],
##        [0.93587998],
##        [0.36710963],
##        [0.73889944]])
## array([[0.6247976 ],
##        [0.9414008 ],
##        [0.36382259],
##        [0.73567802]])
## array([[0.62661343],
##        [0.94671682],
##        [0.36066804],
##        [0.73253107]])
## array([[0.62838046],
##        [0.95183534],
##        [0.35764124],
##        [0.72945596]])
## array([[0.63010042],
##        [0.95676339],
##        [0.35473763],
##        [0.72645018]])
## array([[0.63177499],
##        [0.96150776],
##        [0.35195282],
##        [0.7235113 ]])
## array([[0.63340576],
##        [0.966075  ],
##        [0.34928256],
##        [0.72063695]])
## array([[0.6349943 ],
##        [0.97047141],
##        [0.34672275],
##        [0.71782489]])
## array([[0.63654208],
##        [0.97470308],
##        [0.34426946],
##        [0.71507293]])
## array([[0.63805055],
##        [0.97877588],
##        [0.34191888],
##        [0.71237896]])
## array([[0.6395211 ],
##        [0.98269548],
##        [0.33966734],
##        [0.70974095]])
## array([[0.64095508],
##        [0.98646732],
##        [0.33751131],
##        [0.70715695]])
## array([[0.64235376],
##        [0.99009669],
##        [0.33544736],
##        [0.70462507]])
## array([[0.64371841],
##        [0.99358865],
##        [0.3334722 ],
##        [0.70214348]])
## array([[0.64505023],
##        [0.99694811],
##        [0.33158268],
##        [0.69971043]])
## array([[0.64635036],
##        [1.00017978],
##        [0.32977571],
##        [0.69732422]])
## array([[0.64761993],
##        [1.00328822],
##        [0.32804835],
##        [0.69498323]])
## array([[0.64886003],
##        [1.00627783],
##        [0.32639776],
##        [0.69268587]])
## array([[0.65007168],
##        [1.00915283],
##        [0.32482118],
##        [0.69043063]])
## array([[0.65125589],
##        [1.01191732],
##        [0.32331597],
##        [0.68821604]])
## array([[0.65241363],
##        [1.01457524],
##        [0.32187958],
##        [0.68604069]])
## array([[0.65354583],
##        [1.01713037],
##        [0.32050954],
##        [0.68390321]])
## array([[0.65465339],
##        [1.0195864 ],
##        [0.31920347],
##        [0.6818023 ]])
## array([[0.65573719],
##        [1.02194685],
##        [0.3179591 ],
##        [0.67973667]])
## array([[0.65679804],
##        [1.02421513],
##        [0.3167742 ],
##        [0.67770512]])
## array([[0.65783677],
##        [1.02639453],
##        [0.31564665],
##        [0.67570646]])
## array([[0.65885415],
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##        [0.31457439],
##        [0.67373955]])
## array([[0.65985093],
##        [1.03049924],
##        [0.31355544],
##        [0.6718033 ]])
## array([[0.66082784],
##        [1.03243056],
##        [0.3125879 ],
##        [0.66989665]])
## array([[0.66178557],
##        [1.03428502],
##        [0.31166991],
##        [0.66801858]])
## array([[0.66272479],
##        [1.03606536],
##        [0.3107997 ],
##        [0.6661681 ]])
## array([[0.66364616],
##        [1.03777421],
##        [0.30997556],
##        [0.66434427]])
## array([[0.6645503 ],
##        [1.03941414],
##        [0.30919584],
##        [0.66254617]])
## array([[0.66543782],
##        [1.04098761],
##        [0.30845893],
##        [0.66077292]])
## array([[0.66630929],
##        [1.04249697],
##        [0.30776331],
##        [0.65902367]])
## array([[0.66716529],
##        [1.04394453],
##        [0.30710749],
##        [0.6572976 ]])
## array([[0.66800634],
##        [1.04533248],
##        [0.30649003],
##        [0.65559391]])
## array([[0.66883298],
##        [1.04666296],
##        [0.30590957],
##        [0.65391184]])
## array([[0.66964571],
##        [1.04793802],
##        [0.30536477],
##        [0.65225066]])
## array([[0.67044501],
##        [1.04915963],
##        [0.30485436],
##        [0.65060966]])
## array([[0.67123135],
##        [1.05032971],
##        [0.30437708],
##        [0.64898814]])
## array([[0.67200519],
##        [1.05145009],
##        [0.30393175],
##        [0.64738546]])
## array([[0.67276695],
##        [1.05252254],
##        [0.30351722],
##        [0.64580097]])
## array([[0.67351707],
##        [1.05354879],
##        [0.30313239],
##        [0.64423405]])
## array([[0.67425594],
##        [1.05453048],
##        [0.30277617],
##        [0.64268413]])
## array([[0.67498396],
##        [1.0554692 ],
##        [0.30244754],
##        [0.64115061]])
## array([[0.6757015 ],
##        [1.0563665 ],
##        [0.30214551],
##        [0.63963296]])
## array([[0.67640892],
##        [1.05722384],
##        [0.30186911],
##        [0.63813064]])
## array([[0.67710659],
##        [1.05804265],
##        [0.30161742],
##        [0.63664314]])
## array([[0.67779483],
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##        [0.30138955],
##        [0.63516997]])
## array([[0.67847397],
##        [1.05957018],
##        [0.30118463],
##        [0.63371064]])
## array([[0.67914433],
##        [1.0602815 ],
##        [0.30100184],
##        [0.6322647 ]])
## array([[0.67980621],
##        [1.06095951],
##        [0.30084037],
##        [0.6308317 ]])
## array([[0.68045991],
##        [1.06160541],
##        [0.30069946],
##        [0.62941122]])
## array([[0.68110569],
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##        [0.30057836],
##        [0.62800284]])
## array([[0.68174385],
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##        [0.30047634],
##        [0.62660616]])
## array([[0.68237463],
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##        [0.62522081]])
## array([[0.6829983 ],
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##        [0.30032685],
##        [0.62384641]])
## array([[0.68361509],
##        [1.06439208],
##        [0.30027805],
##        [0.6224826 ]])
## array([[0.68422525],
##        [1.06486806],
##        [0.30024572],
##        [0.62112904]])
## array([[0.684829  ],
##        [1.06531917],
##        [0.30022926],
##        [0.6197854 ]])
## array([[0.68542655],
##        [1.06574628],
##        [0.30022809],
##        [0.61845136]])
## array([[0.68601812],
##        [1.06615027],
##        [0.30024165],
##        [0.61712661]])
## array([[0.68660392],
##        [1.06653196],
##        [0.3002694 ],
##        [0.61581084]])
## array([[0.68718414],
##        [1.06689215],
##        [0.30031084],
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## array([[0.68775896],
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##        [0.30036545],
##        [0.61320514]])
## array([[0.68832858],
##        [1.06755109],
##        [0.30043276],
##        [0.61191467]])
## array([[0.68889316],
##        [1.0678513 ],
##        [0.3005123 ],
##        [0.61063209]])
## array([[0.68945289],
##        [1.06813294],
##        [0.30060362],
##        [0.60935716]])
## array([[0.69000791],
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##        [0.30070629],
##        [0.60808965]])
## array([[0.6905584 ],
##        [1.06864312],
##        [0.3008199 ],
##        [0.60682931]])
## array([[0.69110449],
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##        [0.30094404],
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##        [0.30107833],
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## array([[0.69218411],
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##        [0.30122239],
##        [0.60308919]])
## array([[0.69271791],
##        [1.06946837],
##        [0.30137586],
##        [0.60185542]])
## 0.19800431982332067
## array([[0.69324787],
##        [1.06963738],
##        [0.30153839],
##        [0.6006278 ]])
## array([[0.69377414],
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##        [0.30170966],
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## array([[0.69429682],
##        [1.0699343 ],
##        [0.30188932],
##        [0.59819022]])
## array([[0.69481605],
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##        [0.30207708],
##        [0.59697993]])
## array([[0.69533192],
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##        [0.30227264],
##        [0.59577506]])
## array([[0.69584456],
##        [1.07028421],
##        [0.30247569],
##        [0.59457547]])
## array([[0.69635407],
##        [1.07037719],
##        [0.30268597],
##        [0.593381  ]])
## array([[0.69686055],
##        [1.07045905],
##        [0.3029032 ],
##        [0.59219149]])
## array([[0.6973641 ],
##        [1.07053019],
##        [0.30312713],
##        [0.59100681]])
## array([[0.69786481],
##        [1.070591  ],
##        [0.3033575 ],
##        [0.58982681]])
## array([[0.69836278],
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##        [0.58865135]])
## array([[0.6988581 ],
##        [1.07068311],
##        [0.30383661],
##        [0.58748032]])
## array([[0.69935084],
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## array([[0.69984111],
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##        [0.30433869],
##        [0.585151  ]])
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##        [0.30459782],
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## array([[0.70081449],
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##        [0.30486206],
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## array([[0.70129775],
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##        [0.30513122],
##        [0.58168718]])
## array([[0.70177884],
##        [1.07074768],
##        [0.30540511],
##        [0.58054017]])
## array([[0.7022578 ],
##        [1.07073081],
##        [0.30568355],
##        [0.5793968 ]])
## array([[0.70273472],
##        [1.07070679],
##        [0.30596638],
##        [0.57825695]])
## array([[0.70320965],
##        [1.07067589],
##        [0.30625341],
##        [0.57712054]])
## array([[0.70368266],
##        [1.07063836],
##        [0.30654449],
##        [0.57598748]])
## array([[0.7041538 ],
##        [1.07059444],
##        [0.30683946],
##        [0.57485767]])
## array([[0.70462314],
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##        [0.30713817],
##        [0.57373104]])
## array([[0.70509072],
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##        [0.30744048],
##        [0.57260751]])
## array([[0.70555661],
##        [1.07042663],
##        [0.30774624],
##        [0.57148699]])
## array([[0.70602085],
##        [1.0703594 ],
##        [0.30805531],
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## array([[0.70648349],
##        [1.07028686],
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##        [0.5692547 ]])
## array([[0.70694458],
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## array([[0.7078623 ],
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##        [ 0.93185863],
##        [ 0.55279798],
##        [-0.07788827]])
## array([[ 0.99740545],
##        [ 0.93173711],
##        [ 0.55298957],
##        [-0.07839312]])
## array([[ 0.99766909],
##        [ 0.93161566],
##        [ 0.55318099],
##        [-0.07889752]])
## array([[ 0.99793258],
##        [ 0.9314943 ],
##        [ 0.55337224],
##        [-0.07940149]])
## array([[ 0.99819595],
##        [ 0.93137302],
##        [ 0.55356332],
##        [-0.07990502]])
## array([[ 0.99845917],
##        [ 0.93125182],
##        [ 0.55375423],
##        [-0.08040811]])
## array([[ 0.99872227],
##        [ 0.9311307 ],
##        [ 0.55394497],
##        [-0.08091077]])
## array([[ 0.99898523],
##        [ 0.93100965],
##        [ 0.55413554],
##        [-0.08141298]])
## array([[ 0.99924805],
##        [ 0.93088869],
##        [ 0.55432594],
##        [-0.08191476]])
## array([[ 0.99951074],
##        [ 0.93076781],
##        [ 0.55451617],
##        [-0.08241609]])
## array([[ 0.99977329],
##        [ 0.930647  ],
##        [ 0.55470623],
##        [-0.082917  ]])
## 0.12254257990769526
## [[ 0.99977329]
##  [ 0.930647  ]
##  [ 0.55470623]
##  [-0.082917  ]]

we can make this even better if we use different starting values in each run

learning_rate = 0.01
n_iter = 10000

X, y = get_data()
start_values = tf.random_uniform([4, 1], -1, 1, dtype="float64")
beta = tf.Variable(start_values, name='beta')
mse = tf.reduce_mean(tf.square(y - tf.matmul(X, beta)))

# gradient = 2 / 150 * tf.matmul(tf.transpose(X), tf.matmul(X, beta) - y)
gradient = tf.gradients(mse, [beta])[0]

_training = tf.assign(beta, beta - learning_rate * gradient)  # a tensor

init = tf.global_variables_initializer()
with tf.Session() as sess:
    init.run()
    for i in range(n_iter):
        _training.eval()
        if not i % 1000:
            print(mse.eval())
    print(mse.eval())
    print(beta.eval())

## array([[-0.25678106],
##        [ 0.53768761],
##        [ 0.79779736],
##        [ 0.73643841]])
## 1.7552803703945419
## array([[-0.24528373],
##        [ 0.57697096],
##        [ 0.80149495],
##        [ 0.73274816]])
## array([[-0.23660779],
##        [ 0.60749542],
##        [ 0.79471311],
##        [ 0.72571649]])
## array([[-0.22929344],
##        [ 0.6336804 ],
##        [ 0.78361514],
##        [ 0.71738129]])
## array([[-0.22269245],
##        [ 0.65749277],
##        [ 0.7709095 ],
##        [ 0.70863569]])
## array([[-0.21651479],
##        [ 0.67981665],
##        [ 0.75778064],
##        [ 0.6998679 ]])
## array([[-0.21062864],
##        [ 0.70105786],
##        [ 0.74474098],
##        [ 0.69124357]])
## array([[-0.20497212],
##        [ 0.72141063],
##        [ 0.73200678],
##        [ 0.68283036]])
## array([[-0.19951432],
##        [ 0.74097528],
##        [ 0.71966399],
##        [ 0.67465287]])
## array([[-0.19423815],
##        [ 0.75981017],
##        [ 0.70774137],
##        [ 0.66671692]])
## array([[-0.18913274],
##        [ 0.77795466],
##        [ 0.69624281],
##        [ 0.65902017]])
## array([[-0.18419006],
##        [ 0.79543924],
##        [ 0.68516153],
##        [ 0.6515569 ]])
## array([[-0.17940347],
##        [ 0.81229001],
##        [ 0.67448631],
##        [ 0.64432006]])
## array([[-0.17476704],
##        [ 0.82853067],
##        [ 0.66420431],
##        [ 0.63730215]])
## array([[-0.17027527],
##        [ 0.84418347],
##        [ 0.65430224],
##        [ 0.63049565]])
## array([[-0.16592295],
##        [ 0.85926955],
##        [ 0.64476688],
##        [ 0.62389317]])
## array([[-0.16170509],
##        [ 0.8738092 ],
##        [ 0.63558529],
##        [ 0.61748753]])
## array([[-0.1576169 ],
##        [ 0.88782192],
##        [ 0.62674494],
##        [ 0.61127177]])
## array([[-0.15365377],
##        [ 0.90132648],
##        [ 0.61823368],
##        [ 0.60523918]])
## array([[-0.14981125],
##        [ 0.914341  ],
##        [ 0.61003981],
##        [ 0.59938328]])
## array([[-0.14608506],
##        [ 0.92688293],
##        [ 0.60215202],
##        [ 0.59369781]])
## array([[-0.14247107],
##        [ 0.93896909],
##        [ 0.59455939],
##        [ 0.58817674]])
## array([[-0.1389653 ],
##        [ 0.95061572],
##        [ 0.58725142],
##        [ 0.58281424]])
## array([[-0.13556389],
##        [ 0.96183847],
##        [ 0.58021796],
##        [ 0.57760471]])
## array([[-0.13226316],
##        [ 0.97265244],
##        [ 0.57344922],
##        [ 0.57254274]])
## array([[-0.12905952],
##        [ 0.98307218],
##        [ 0.56693577],
##        [ 0.56762311]])
## array([[-0.12594953],
##        [ 0.99311173],
##        [ 0.5606685 ],
##        [ 0.56284078]])
## array([[-0.12292986],
##        [ 1.00278464],
##        [ 0.55463865],
##        [ 0.55819089]])
## array([[-0.11999731],
##        [ 1.01210395],
##        [ 0.54883774],
##        [ 0.55366877]])
## array([[-0.1171488 ],
##        [ 1.02108227],
##        [ 0.54325761],
##        [ 0.54926991]])
## array([[-0.11438133],
##        [ 1.02973173],
##        [ 0.5378904 ],
##        [ 0.54498994]])
## array([[-0.11169203],
##        [ 1.03806405],
##        [ 0.53272851],
##        [ 0.54082466]])
## array([[-0.10907814],
##        [ 1.04609053],
##        [ 0.52776461],
##        [ 0.53677004]])
## array([[-0.10653697],
##        [ 1.05382205],
##        [ 0.52299166],
##        [ 0.53282216]])
## array([[-0.10406595],
##        [ 1.06126913],
##        [ 0.51840283],
##        [ 0.52897725]])
## array([[-0.10166259],
##        [ 1.06844189],
##        [ 0.51399156],
##        [ 0.52523168]])
## array([[-0.09932449],
##        [ 1.07535011],
##        [ 0.50975152],
##        [ 0.52158196]])
## array([[-0.09704935],
##        [ 1.08200321],
##        [ 0.5056766 ],
##        [ 0.51802469]])
## array([[-0.09483492],
##        [ 1.08841027],
##        [ 0.5017609 ],
##        [ 0.51455662]])
## array([[-0.09267906],
##        [ 1.09458006],
##        [ 0.49799876],
##        [ 0.51117461]])
## array([[-0.0905797 ],
##        [ 1.10052104],
##        [ 0.49438469],
##        [ 0.50787563]])
## array([[-0.08853483],
##        [ 1.10624134],
##        [ 0.4909134 ],
##        [ 0.50465676]])
## array([[-0.08654252],
##        [ 1.11174885],
##        [ 0.4875798 ],
##        [ 0.50151517]])
## array([[-0.08460092],
##        [ 1.11705112],
##        [ 0.48437898],
##        [ 0.49844815]])
## array([[-0.08270822],
##        [ 1.12215549],
##        [ 0.4813062 ],
##        [ 0.49545307]])
## array([[-0.0808627 ],
##        [ 1.12706899],
##        [ 0.47835688],
##        [ 0.49252742]])
## array([[-0.07906268],
##        [ 1.13179843],
##        [ 0.47552663],
##        [ 0.48966874]])
## array([[-0.07730656],
##        [ 1.13635037],
##        [ 0.47281118],
##        [ 0.48687469]])
## array([[-0.07559279],
##        [ 1.14073113],
##        [ 0.47020644],
##        [ 0.48414299]])
## array([[-0.07391987],
##        [ 1.14494681],
##        [ 0.46770846],
##        [ 0.48147147]])
## array([[-0.07228635],
##        [ 1.14900329],
##        [ 0.46531342],
##        [ 0.47885801]])
## array([[-0.07069084],
##        [ 1.15290624],
##        [ 0.46301765],
##        [ 0.47630058]])
## array([[-0.069132  ],
##        [ 1.15666115],
##        [ 0.46081759],
##        [ 0.47379722]])
## array([[-0.06760853],
##        [ 1.16027327],
##        [ 0.45870984],
##        [ 0.47134602]])
## array([[-0.06611919],
##        [ 1.16374771],
##        [ 0.45669109],
##        [ 0.46894518]])
## array([[-0.06466277],
##        [ 1.16708936],
##        [ 0.45475816],
##        [ 0.46659293]])
## array([[-0.0632381 ],
##        [ 1.17030296],
##        [ 0.45290798],
##        [ 0.46428756]])
## array([[-0.06184407],
##        [ 1.17339308],
##        [ 0.4511376 ],
##        [ 0.46202745]])
## array([[-0.0604796 ],
##        [ 1.17636412],
##        [ 0.44944416],
##        [ 0.45981102]])
## array([[-0.05914364],
##        [ 1.17922031],
##        [ 0.44782491],
##        [ 0.45763674]])
## array([[-0.0578352 ],
##        [ 1.18196577],
##        [ 0.4462772 ],
##        [ 0.45550314]])
## array([[-0.05655329],
##        [ 1.18460442],
##        [ 0.44479846],
##        [ 0.45340881]])
## array([[-0.05529699],
##        [ 1.18714009],
##        [ 0.44338624],
##        [ 0.45135239]])
## array([[-0.05406539],
##        [ 1.18957644],
##        [ 0.44203815],
##        [ 0.44933255]])
## array([[-0.05285762],
##        [ 1.19191701],
##        [ 0.44075189],
##        [ 0.44734802]])
## array([[-0.05167286],
##        [ 1.19416522],
##        [ 0.43952526],
##        [ 0.44539759]])
## array([[-0.05051028],
##        [ 1.19632436],
##        [ 0.43835611],
##        [ 0.44348006]])
## array([[-0.04936911],
##        [ 1.19839762],
##        [ 0.43724239],
##        [ 0.44159431]])
## array([[-0.04824859],
##        [ 1.20038804],
##        [ 0.43618211],
##        [ 0.43973922]])
## array([[-0.04714801],
##        [ 1.20229859],
##        [ 0.43517336],
##        [ 0.43791375]])
## array([[-0.04606665],
##        [ 1.20413212],
##        [ 0.43421429],
##        [ 0.43611686]])
## array([[-0.04500385],
##        [ 1.20589138],
##        [ 0.43330312],
##        [ 0.43434757]])
## array([[-0.04395896],
##        [ 1.207579  ],
##        [ 0.43243814],
##        [ 0.43260494]])
## array([[-0.04293135],
##        [ 1.20919756],
##        [ 0.43161768],
##        [ 0.43088804]])
## array([[-0.0419204 ],
##        [ 1.2107495 ],
##        [ 0.43084015],
##        [ 0.42919599]])
## array([[-0.04092555],
##        [ 1.21223722],
##        [ 0.43010401],
##        [ 0.42752794]])
## array([[-0.03994623],
##        [ 1.213663  ],
##        [ 0.42940777],
##        [ 0.42588307]])
## array([[-0.03898189],
##        [ 1.21502906],
##        [ 0.42875   ],
##        [ 0.42426057]])
## array([[-0.03803201],
##        [ 1.21633752],
##        [ 0.42812933],
##        [ 0.4226597 ]])
## array([[-0.03709608],
##        [ 1.21759044],
##        [ 0.4275444 ],
##        [ 0.4210797 ]])
## array([[-0.03617363],
##        [ 1.2187898 ],
##        [ 0.42699395],
##        [ 0.41951987]])
## array([[-0.03526417],
##        [ 1.21993752],
##        [ 0.42647673],
##        [ 0.41797952]])
## array([[-0.03436726],
##        [ 1.22103544],
##        [ 0.42599155],
##        [ 0.41645799]])
## array([[-0.03348246],
##        [ 1.22208534],
##        [ 0.42553725],
##        [ 0.41495463]])
## array([[-0.03260934],
##        [ 1.22308893],
##        [ 0.42511272],
##        [ 0.41346885]])
## array([[-0.03174751],
##        [ 1.22404787],
##        [ 0.4247169 ],
##        [ 0.41200004]])
## array([[-0.03089657],
##        [ 1.22496376],
##        [ 0.42434873],
##        [ 0.41054762]])
## array([[-0.03005615],
##        [ 1.22583812],
##        [ 0.42400724],
##        [ 0.40911105]])
## array([[-0.02922587],
##        [ 1.22667246],
##        [ 0.42369146],
##        [ 0.40768979]])
## array([[-0.02840538],
##        [ 1.22746819],
##        [ 0.42340046],
##        [ 0.40628334]])
## array([[-0.02759436],
##        [ 1.22822669],
##        [ 0.42313334],
##        [ 0.40489119]])
## array([[-0.02679246],
##        [ 1.2289493 ],
##        [ 0.42288925],
##        [ 0.40351286]])
## array([[-0.02599939],
##        [ 1.2296373 ],
##        [ 0.42266735],
##        [ 0.4021479 ]])
## array([[-0.02521482],
##        [ 1.23029194],
##        [ 0.42246684],
##        [ 0.40079586]])
## array([[-0.02443847],
##        [ 1.23091439],
##        [ 0.42228694],
##        [ 0.39945632]])
## array([[-0.02367006],
##        [ 1.23150581],
##        [ 0.42212692],
##        [ 0.39812885]])
## array([[-0.02290931],
##        [ 1.23206732],
##        [ 0.42198604],
##        [ 0.39681306]])
## array([[-0.02215596],
##        [ 1.23259999],
##        [ 0.42186363],
##        [ 0.39550857]])
## array([[-0.02140976],
##        [ 1.23310484],
##        [ 0.421759  ],
##        [ 0.394215  ]])
## array([[-0.02067046],
##        [ 1.23358287],
##        [ 0.42167152],
##        [ 0.392932  ]])
## array([[-0.01993782],
##        [ 1.23403504],
##        [ 0.42160055],
##        [ 0.39165922]])
## array([[-0.01921163],
##        [ 1.23446229],
##        [ 0.42154551],
##        [ 0.39039633]])
## array([[-0.01849165],
##        [ 1.23486549],
##        [ 0.4215058 ],
##        [ 0.38914301]])
## array([[-0.01777768],
##        [ 1.23524552],
##        [ 0.42148088],
##        [ 0.38789895]])
## array([[-0.01706951],
##        [ 1.23560321],
##        [ 0.42147021],
##        [ 0.38666384]])
## array([[-0.01636695],
##        [ 1.23593935],
##        [ 0.42147326],
##        [ 0.38543741]])
## array([[-0.0156698 ],
##        [ 1.23625471],
##        [ 0.42148954],
##        [ 0.38421937]])
## array([[-0.01497789],
##        [ 1.23655005],
##        [ 0.42151856],
##        [ 0.38300946]])
## array([[-0.01429103],
##        [ 1.23682609],
##        [ 0.42155986],
##        [ 0.38180742]])
## array([[-0.01360907],
##        [ 1.2370835 ],
##        [ 0.42161298],
##        [ 0.38061299]])
## array([[-0.01293182],
##        [ 1.23732297],
##        [ 0.4216775 ],
##        [ 0.37942595]])
## array([[-0.01225914],
##        [ 1.23754514],
##        [ 0.421753  ],
##        [ 0.37824604]])
## array([[-0.01159086],
##        [ 1.23775062],
##        [ 0.42183907],
##        [ 0.37707307]])
## array([[-0.01092686],
##        [ 1.23794003],
##        [ 0.42193533],
##        [ 0.37590679]])
## array([[-0.01026697],
##        [ 1.23811393],
##        [ 0.4220414 ],
##        [ 0.37474702]])
## array([[-0.00961106],
##        [ 1.23827288],
##        [ 0.42215692],
##        [ 0.37359355]])
## array([[-0.00895901],
##        [ 1.23841743],
##        [ 0.42228155],
##        [ 0.37244618]])
## array([[-0.00831069],
##        [ 1.23854808],
##        [ 0.42241494],
##        [ 0.37130472]])
## array([[-0.00766596],
##        [ 1.23866535],
##        [ 0.42255677],
##        [ 0.37016901]])
## array([[-0.00702472],
##        [ 1.23876971],
##        [ 0.42270673],
##        [ 0.36903886]])
## array([[-0.00638685],
##        [ 1.23886163],
##        [ 0.42286452],
##        [ 0.36791411]])
## array([[-0.00575224],
##        [ 1.23894155],
##        [ 0.42302985],
##        [ 0.36679459]])
## array([[-0.00512078],
##        [ 1.23900991],
##        [ 0.42320244],
##        [ 0.36568016]])
## array([[-0.00449236],
##        [ 1.23906713],
##        [ 0.42338202],
##        [ 0.36457065]])
## array([[-0.0038669 ],
##        [ 1.23911361],
##        [ 0.42356832],
##        [ 0.36346592]])
## array([[-0.00324429],
##        [ 1.23914973],
##        [ 0.4237611 ],
##        [ 0.36236584]])
## array([[-0.00262445],
##        [ 1.23917588],
##        [ 0.42396011],
##        [ 0.36127027]])
## array([[-0.00200728],
##        [ 1.2391924 ],
##        [ 0.42416513],
##        [ 0.36017908]])
## array([[-0.0013927 ],
##        [ 1.23919965],
##        [ 0.42437592],
##        [ 0.35909214]])
## array([[-7.80624435e-04],
##        [ 1.23919797e+00],
##        [ 4.24592261e-01],
##        [ 3.58009339e-01]])
## array([[-1.70975676e-04],
##        [ 1.23918768e+00],
##        [ 4.24813954e-01],
##        [ 3.56930550e-01]])
## array([[4.36323595e-04],
##        [1.23916909e+00],
##        [4.25040793e-01],
##        [3.55855664e-01]])
## array([[1.04134748e-03],
##        [1.23914250e+00],
##        [4.25272582e-01],
##        [3.54784572e-01]])
## array([[0.00164417],
##        [1.2391082 ],
##        [0.42550913],
##        [0.35371717]])
## array([[0.00224485],
##        [1.23906647],
##        [0.42575027],
##        [0.35265336]])
## array([[0.00284347],
##        [1.23901759],
##        [0.42599581],
##        [0.35159303]])
## array([[0.00344008],
##        [1.2389618 ],
##        [0.42624559],
##        [0.35053611]])
## array([[0.00403475],
##        [1.23889937],
##        [0.42649944],
##        [0.34948249]])
## array([[0.00462754],
##        [1.23883054],
##        [0.42675721],
##        [0.34843209]])
## array([[0.0052185 ],
##        [1.23875554],
##        [0.42701875],
##        [0.34738482]])
## array([[0.00580769],
##        [1.23867458],
##        [0.42728391],
##        [0.34634061]])
## array([[0.00639517],
##        [1.23858791],
##        [0.42755256],
##        [0.34529936]])
## array([[0.00698098],
##        [1.23849571],
##        [0.42782455],
##        [0.34426102]])
## array([[0.00756517],
##        [1.23839819],
##        [0.42809975],
##        [0.3432255 ]])
## array([[0.0081478 ],
##        [1.23829555],
##        [0.42837804],
##        [0.34219274]])
## array([[0.00872891],
##        [1.23818798],
##        [0.4286593 ],
##        [0.34116266]])
## array([[0.00930854],
##        [1.23807565],
##        [0.42894341],
##        [0.3401352 ]])
## array([[0.00988674],
##        [1.23795875],
##        [0.42923026],
##        [0.3391103 ]])
## array([[0.01046355],
##        [1.23783743],
##        [0.42951974],
##        [0.3380879 ]])
## array([[0.01103901],
##        [1.23771187],
##        [0.42981173],
##        [0.33706793]])
## array([[0.01161315],
##        [1.23758222],
##        [0.43010615],
##        [0.33605034]])
## array([[0.01218602],
##        [1.23744863],
##        [0.4304029 ],
##        [0.33503508]])
## array([[0.01275765],
##        [1.23731124],
##        [0.43070187],
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##        [ 0.74280779],
##        [-0.61473582]])
## array([[ 1.60805339],
##        [ 0.71263782],
##        [ 0.74280159],
##        [-0.61472518]])
## array([[ 1.6080998 ],
##        [ 0.71262623],
##        [ 0.74279539],
##        [-0.61471453]])
## array([[ 1.60814621],
##        [ 0.71261464],
##        [ 0.74278919],
##        [-0.61470389]])
## array([[ 1.6081926 ],
##        [ 0.71260305],
##        [ 0.74278299],
##        [-0.61469325]])
## array([[ 1.60823899],
##        [ 0.71259146],
##        [ 0.7427768 ],
##        [-0.61468261]])
## array([[ 1.60828536],
##        [ 0.71257988],
##        [ 0.7427706 ],
##        [-0.61467197]])
## array([[ 1.60833173],
##        [ 0.71256829],
##        [ 0.74276441],
##        [-0.61466133]])
## array([[ 1.60837809],
##        [ 0.71255671],
##        [ 0.74275821],
##        [-0.6146507 ]])
## array([[ 1.60842444],
##        [ 0.71254514],
##        [ 0.74275202],
##        [-0.61464007]])
## array([[ 1.60847079],
##        [ 0.71253356],
##        [ 0.74274583],
##        [-0.61462944]])
## array([[ 1.60851712],
##        [ 0.71252199],
##        [ 0.74273964],
##        [-0.61461881]])
## array([[ 1.60856344],
##        [ 0.71251041],
##        [ 0.74273345],
##        [-0.61460818]])
## array([[ 1.60860976],
##        [ 0.71249885],
##        [ 0.74272727],
##        [-0.61459755]])
## array([[ 1.60865607],
##        [ 0.71248728],
##        [ 0.74272108],
##        [-0.61458693]])
## array([[ 1.60870237],
##        [ 0.71247571],
##        [ 0.7427149 ],
##        [-0.61457631]])
## array([[ 1.60874866],
##        [ 0.71246415],
##        [ 0.74270871],
##        [-0.61456569]])
## array([[ 1.60879494],
##        [ 0.71245259],
##        [ 0.74270253],
##        [-0.61455507]])
## array([[ 1.60884121],
##        [ 0.71244103],
##        [ 0.74269635],
##        [-0.61454445]])
## array([[ 1.60888747],
##        [ 0.71242947],
##        [ 0.74269016],
##        [-0.61453384]])
## array([[ 1.60893373],
##        [ 0.71241792],
##        [ 0.74268398],
##        [-0.61452323]])
## array([[ 1.60897997],
##        [ 0.71240637],
##        [ 0.74267781],
##        [-0.61451261]])
## array([[ 1.60902621],
##        [ 0.71239482],
##        [ 0.74267163],
##        [-0.61450201]])
## array([[ 1.60907244],
##        [ 0.71238327],
##        [ 0.74266545],
##        [-0.6144914 ]])
## array([[ 1.60911866],
##        [ 0.71237173],
##        [ 0.74265928],
##        [-0.61448079]])
## array([[ 1.60916487],
##        [ 0.71236018],
##        [ 0.7426531 ],
##        [-0.61447019]])
## array([[ 1.60921108],
##        [ 0.71234864],
##        [ 0.74264693],
##        [-0.61445959]])
## array([[ 1.60925727],
##        [ 0.7123371 ],
##        [ 0.74264076],
##        [-0.61444899]])
## array([[ 1.60930346],
##        [ 0.71232557],
##        [ 0.74263458],
##        [-0.61443839]])
## array([[ 1.60934963],
##        [ 0.71231403],
##        [ 0.74262841],
##        [-0.61442779]])
## array([[ 1.6093958 ],
##        [ 0.7123025 ],
##        [ 0.74262225],
##        [-0.6144172 ]])
## array([[ 1.60944196],
##        [ 0.71229097],
##        [ 0.74261608],
##        [-0.6144066 ]])
## array([[ 1.60948811],
##        [ 0.71227944],
##        [ 0.74260991],
##        [-0.61439601]])
## array([[ 1.60953425],
##        [ 0.71226792],
##        [ 0.74260374],
##        [-0.61438542]])
## array([[ 1.60958039],
##        [ 0.71225639],
##        [ 0.74259758],
##        [-0.61437483]])
## array([[ 1.60962651],
##        [ 0.71224487],
##        [ 0.74259142],
##        [-0.61436425]])
## array([[ 1.60967263],
##        [ 0.71223335],
##        [ 0.74258525],
##        [-0.61435366]])
## array([[ 1.60971874],
##        [ 0.71222184],
##        [ 0.74257909],
##        [-0.61434308]])
## array([[ 1.60976484],
##        [ 0.71221032],
##        [ 0.74257293],
##        [-0.6143325 ]])
## array([[ 1.60981093],
##        [ 0.71219881],
##        [ 0.74256677],
##        [-0.61432192]])
## array([[ 1.60985701],
##        [ 0.7121873 ],
##        [ 0.74256061],
##        [-0.61431134]])
## array([[ 1.60990308],
##        [ 0.71217579],
##        [ 0.74255446],
##        [-0.61430077]])
## array([[ 1.60994914],
##        [ 0.71216429],
##        [ 0.7425483 ],
##        [-0.61429019]])
## array([[ 1.6099952 ],
##        [ 0.71215278],
##        [ 0.74254214],
##        [-0.61427962]])
## array([[ 1.61004125],
##        [ 0.71214128],
##        [ 0.74253599],
##        [-0.61426905]])
## array([[ 1.61008728],
##        [ 0.71212978],
##        [ 0.74252984],
##        [-0.61425848]])
## array([[ 1.61013331],
##        [ 0.71211828],
##        [ 0.74252369],
##        [-0.61424792]])
## array([[ 1.61017934],
##        [ 0.71210679],
##        [ 0.74251753],
##        [-0.61423735]])
## array([[ 1.61022535],
##        [ 0.7120953 ],
##        [ 0.74251138],
##        [-0.61422679]])
## array([[ 1.61027135],
##        [ 0.71208381],
##        [ 0.74250524],
##        [-0.61421623]])
## array([[ 1.61031735],
##        [ 0.71207232],
##        [ 0.74249909],
##        [-0.61420567]])
## array([[ 1.61036333],
##        [ 0.71206083],
##        [ 0.74249294],
##        [-0.61419511]])
## array([[ 1.61040931],
##        [ 0.71204935],
##        [ 0.7424868 ],
##        [-0.61418456]])
## array([[ 1.61045528],
##        [ 0.71203786],
##        [ 0.74248065],
##        [-0.614174  ]])
## array([[ 1.61050124],
##        [ 0.71202638],
##        [ 0.74247451],
##        [-0.61416345]])
## array([[ 1.61054719],
##        [ 0.71201491],
##        [ 0.74246837],
##        [-0.6141529 ]])
## array([[ 1.61059313],
##        [ 0.71200343],
##        [ 0.74246222],
##        [-0.61414235]])
## array([[ 1.61063907],
##        [ 0.71199196],
##        [ 0.74245608],
##        [-0.6141318 ]])
## array([[ 1.61068499],
##        [ 0.71198049],
##        [ 0.74244995],
##        [-0.61412126]])
## array([[ 1.61073091],
##        [ 0.71196902],
##        [ 0.74244381],
##        [-0.61411071]])
## array([[ 1.61077682],
##        [ 0.71195755],
##        [ 0.74243767],
##        [-0.61410017]])
## array([[ 1.61082272],
##        [ 0.71194609],
##        [ 0.74243153],
##        [-0.61408963]])
## array([[ 1.61086861],
##        [ 0.71193463],
##        [ 0.7424254 ],
##        [-0.61407909]])
## array([[ 1.61091449],
##        [ 0.71192317],
##        [ 0.74241927],
##        [-0.61406856]])
## array([[ 1.61096037],
##        [ 0.71191171],
##        [ 0.74241313],
##        [-0.61405802]])
## array([[ 1.61100623],
##        [ 0.71190025],
##        [ 0.742407  ],
##        [-0.61404749]])
## array([[ 1.61105209],
##        [ 0.7118888 ],
##        [ 0.74240087],
##        [-0.61403696]])
## array([[ 1.61109794],
##        [ 0.71187735],
##        [ 0.74239474],
##        [-0.61402643]])
## array([[ 1.61114378],
##        [ 0.7118659 ],
##        [ 0.74238861],
##        [-0.6140159 ]])
## array([[ 1.61118961],
##        [ 0.71185445],
##        [ 0.74238249],
##        [-0.61400538]])
## array([[ 1.61123543],
##        [ 0.711843  ],
##        [ 0.74237636],
##        [-0.61399485]])
## array([[ 1.61128125],
##        [ 0.71183156],
##        [ 0.74237024],
##        [-0.61398433]])
## array([[ 1.61132705],
##        [ 0.71182012],
##        [ 0.74236411],
##        [-0.61397381]])
## array([[ 1.61137285],
##        [ 0.71180868],
##        [ 0.74235799],
##        [-0.61396329]])
## array([[ 1.61141863],
##        [ 0.71179725],
##        [ 0.74235187],
##        [-0.61395278]])
## array([[ 1.61146441],
##        [ 0.71178581],
##        [ 0.74234575],
##        [-0.61394226]])
## array([[ 1.61151018],
##        [ 0.71177438],
##        [ 0.74233963],
##        [-0.61393175]])
## array([[ 1.61155595],
##        [ 0.71176295],
##        [ 0.74233351],
##        [-0.61392124]])
## array([[ 1.6116017 ],
##        [ 0.71175152],
##        [ 0.74232739],
##        [-0.61391073]])
## array([[ 1.61164745],
##        [ 0.7117401 ],
##        [ 0.74232127],
##        [-0.61390022]])
## array([[ 1.61169318],
##        [ 0.71172867],
##        [ 0.74231516],
##        [-0.61388971]])
## array([[ 1.61173891],
##        [ 0.71171725],
##        [ 0.74230904],
##        [-0.61387921]])
## array([[ 1.61178463],
##        [ 0.71170583],
##        [ 0.74230293],
##        [-0.61386871]])
## array([[ 1.61183034],
##        [ 0.71169442],
##        [ 0.74229682],
##        [-0.61385821]])
## array([[ 1.61187604],
##        [ 0.711683  ],
##        [ 0.74229071],
##        [-0.61384771]])
## array([[ 1.61192173],
##        [ 0.71167159],
##        [ 0.7422846 ],
##        [-0.61383721]])
## array([[ 1.61196742],
##        [ 0.71166018],
##        [ 0.74227849],
##        [-0.61382671]])
## array([[ 1.61201309],
##        [ 0.71164877],
##        [ 0.74227238],
##        [-0.61381622]])
## array([[ 1.61205876],
##        [ 0.71163737],
##        [ 0.74226627],
##        [-0.61380573]])
## array([[ 1.61210442],
##        [ 0.71162596],
##        [ 0.74226017],
##        [-0.61379524]])
## array([[ 1.61215007],
##        [ 0.71161456],
##        [ 0.74225406],
##        [-0.61378475]])
## array([[ 1.61219571],
##        [ 0.71160316],
##        [ 0.74224796],
##        [-0.61377426]])
## array([[ 1.61224135],
##        [ 0.71159176],
##        [ 0.74224185],
##        [-0.61376378]])
## array([[ 1.61228697],
##        [ 0.71158037],
##        [ 0.74223575],
##        [-0.6137533 ]])
## array([[ 1.61233259],
##        [ 0.71156898],
##        [ 0.74222965],
##        [-0.61374281]])
## array([[ 1.6123782 ],
##        [ 0.71155759],
##        [ 0.74222355],
##        [-0.61373234]])
## array([[ 1.61242379],
##        [ 0.7115462 ],
##        [ 0.74221745],
##        [-0.61372186]])
## array([[ 1.61246938],
##        [ 0.71153481],
##        [ 0.74221136],
##        [-0.61371138]])
## array([[ 1.61251497],
##        [ 0.71152343],
##        [ 0.74220526],
##        [-0.61370091]])
## array([[ 1.61256054],
##        [ 0.71151204],
##        [ 0.74219917],
##        [-0.61369044]])
## array([[ 1.6126061 ],
##        [ 0.71150066],
##        [ 0.74219307],
##        [-0.61367996]])
## array([[ 1.61265166],
##        [ 0.71148929],
##        [ 0.74218698],
##        [-0.6136695 ]])
## array([[ 1.61269721],
##        [ 0.71147791],
##        [ 0.74218089],
##        [-0.61365903]])
## array([[ 1.61274275],
##        [ 0.71146654],
##        [ 0.7421748 ],
##        [-0.61364856]])
## array([[ 1.61278828],
##        [ 0.71145517],
##        [ 0.74216871],
##        [-0.6136381 ]])
## array([[ 1.6128338 ],
##        [ 0.7114438 ],
##        [ 0.74216262],
##        [-0.61362764]])
## array([[ 1.61287931],
##        [ 0.71143243],
##        [ 0.74215653],
##        [-0.61361718]])
## array([[ 1.61292482],
##        [ 0.71142107],
##        [ 0.74215044],
##        [-0.61360672]])
## 0.09692997599157475
## [[ 1.61292482]
##  [ 0.71142107]
##  [ 0.74215044]
##  [-0.61360672]]

lesson #14: it is better to use tensorflow starting values, as they change in every run
lesson #15: you can calculate gradient manually by yourself, but you can use numerical algorithms implemented in tf.gradients

learning_rate = 0.01
n_iter = 10000

X, y = get_data()
start_values = tf.random_uniform([4, 1], -1, 1, dtype="float64")
beta = tf.Variable(start_values, name='beta')
mse = tf.reduce_mean(tf.square(y - tf.matmul(X, beta)))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
_training = optimizer.minimize(mse)  # an operation - a new class of objects

init = tf.global_variables_initializer()
with tf.Session() as sess:
    init.run()
    for i in range(n_iter):
        _training.run()  # operations are being run, not evaluated
        if not i % 1000:
            print(mse.eval())
    print(mse.eval())
    print(beta.eval())

## 2.6204000855136056
## 0.13014588344236225
## 0.11622223011839658
## 0.10937107733488295
## 0.10517362858270994
## 0.10238336420658276
## 0.10048206649326662
## 0.09917741448530833
## 0.09828043627335356
## 0.09766341326768656
## 0.09723925652263762
## [[ 1.55903915]
##  [ 0.72482874]
##  [ 0.74956348]
##  [-0.62650289]]

lesson #16: an optimizer knows, that it can change variables, not constants
lesson #17: operations (like optimizer) are run, not evaluated

you should always use get_variable() insetad of Variable (interesting) link to stackoverflow discussion

learning_rate = 0.01
n_iter = 1000

X_train, y_train = get_data(tensorflow=False)

X = tf.placeholder("float64", shape=(None, 4))  # placeholder -
y = tf.placeholder("float64", shape=(None, 1))

start_values = tf.random_uniform([4, 1], -1, 1, dtype="float64")
beta = tf.Variable(start_values, name='beta')
mse = tf.reduce_mean(tf.square(y - tf.matmul(X, beta)))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
_training = optimizer.minimize(mse)

batch_indexes = np.arange(150).reshape(5, 30)

init = tf.global_variables_initializer()
with tf.Session() as sess:
    init.run()
    for i in range(n_iter):
        for batch_index in batch_indexes:
            _training.run(feed_dict={X: X_train[batch_index],
                                     y: y_train[batch_index]})
        if not i % 100:
            print(mse.eval(feed_dict={X: X_train, y: y_train}))
    print(mse.eval(feed_dict={X: X_train, y: y_train}), "- final score")
    print(beta.eval())

## 0.742123306386937
## 0.19518194205230144
## 0.15395061199146623
## 0.1336995771439441
## 0.12285024335747513
## 0.11640419792328738
## 0.1121632849266807
## 0.10913047712895052
## 0.10683080177073852
## 0.10502121493834225
## 0.10357853172917712 - final score
## [[ 1.02499974]
##  [ 0.86227821]
##  [ 0.79821793]
##  [-0.69286654]]

lesson #18: in mini-batch processing it is comfortable to use placeholders

learning_rate = 0.01

iris = load_iris()
X_np, y_np = iris.data, iris.target

ohe = OneHotEncoder(sparse=False)
y_all = ohe.fit_transform(y_np.reshape(len(y_np), 1))

## /usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.
## If you want the future behaviour and silence this warning, you can specify "categories='auto'".
## In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.
##   warnings.warn(msg, FutureWarning)

x = tf.placeholder(tf.float64, shape=(4, None))
y = tf.placeholder(tf.float64, shape=(3, None))

W = tf.Variable(tf.random_uniform([3, 4], -1, 1, dtype="float64"))
b = tf.Variable(tf.random_uniform([3, 1], -1, 1, dtype="float64"))

mult = tf.matmul(W, x) + b  # broadcasting just like in numpy
y_hat = tf.nn.softmax(mult, axis=0)

error = tf.reduce_mean(tf.square(y - y_hat))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
_training = optimizer.minimize(error)

init = tf.global_variables_initializer()

batches = np.arange(150).reshape(5, 30)

with tf.Session() as sess:
    sess.run(init)
    for i in range(1000):
        for batch in batches:
            _training.run(feed_dict={x: X_np[batch].transpose(),
                                     y: y_all[batch].transpose()})
        if not i % 100:
            print(error.eval(feed_dict={x: X_np.transpose(),
                                        y: y_all.transpose()}))
    preds = y_hat \
        .eval(feed_dict={x: X_np.transpose()}) \
        .transpose()

## 0.3880018730054755
## 0.3345971477352128
## 0.29423670530461216
## 0.2816090851003603
## 0.27804029498630306
## 0.27488690787130776
## 0.2718202331435672
## 0.2686893196600712
## 0.26495882613353183
## 0.2576848064244091

def calculate_accuracy(preds):
    preds_max = np.amax(preds, axis=1)

    max_indexes = []
    for pred, pred_max in zip(preds, preds_max):
        prediction = np.where(pred == pred_max)[0][0]
        max_indexes.append(prediction)

    preds_cat = np.array(max_indexes)
    return(accuracy_score(y_np, preds_cat))

calculate_accuracy(preds)  # maybe overfitting?

## 0.3333333333333333

lesson #19: tf.reshape is NOT the same as tf.transpose
lesson #20: tf.nn.softmax works on rows, not columns. Oh, that’s nice. You can provide “axis” parameter in this function
lesson #21: in tensorflow you will find broadcasting, just like in numpy
name_scope

with tf.name_scope("constants"):
    a = tf.constant(10, name='a')
    b = tf.constant(12, name='b')

and variable scope

with tf.variable_scope("variables"):
    c = tf.constant(20, name='c')
    d = tf.constant(22, name='d')

learning_rate = 0.01

iris = load_iris()
X_np, y_np = iris.data, iris.target

ohe = OneHotEncoder(sparse=False)
y_all = ohe.fit_transform(y_np.reshape(len(y_np), 1))

## /usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.
## If you want the future behaviour and silence this warning, you can specify "categories='auto'".
## In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.
##   warnings.warn(msg, FutureWarning)

x = tf.placeholder(tf.float64, shape=(None, 4), name='x')
y = tf.placeholder(tf.float64, shape=(None, 3), name='y')

W0 = tf.Variable(tf.random_uniform([4, 3], -1, 1, dtype=tf.float64), name='W0')
b0 = tf.Variable(tf.random_uniform([1, 3], -1, 1, dtype=tf.float64), name='b0')  # will broadcast
h = tf.nn.softmax(tf.matmul(x, W0) + b0)

W1 = tf.Variable(tf.random_uniform([3, 3], -1, 1, dtype=tf.float64), name='W1')
b1 = tf.Variable(tf.random_uniform([1, 3], -1, 1, dtype=tf.float64), name='b1')
y_hat = tf.nn.softmax(tf.matmul(h, W1) + b1)

error = tf.reduce_mean(tf.square(y - y_hat))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
_training = optimizer.minimize(error)

init = tf.global_variables_initializer()

batches = np.arange(150).reshape(5, 30)

with tf.Session() as sess:
    sess.run(init)
    for i in range(10000):
        for batch in batches:
            _training.run(feed_dict={x: X_np[batch], y: y_all[batch]})
        if not i % 1000:
            print(error.eval(feed_dict={x: X_np, y: y_all}))
    preds = y_hat.eval(feed_dict={x: X_np})

## 0.24988389200331465
## 0.11514194429910157
## 0.09434482524508238
## 0.07947238407976823
## 0.06856297568535995
## 0.06036078572432309
## 0.05402163226061575
## 0.04902279682058436
## 0.045016865910717296
## 0.041758938023450216

print(calculate_accuracy(preds))  # maybe overfitting?

## 0.9666666666666667

lesson #21: deeper neaural networks converge much more slowly

learning_rate = 0.01

iris = load_iris()
X_np, y_np = iris.data, iris.target

ohe = OneHotEncoder(sparse=False)
y_all = ohe.fit_transform(y_np.reshape(len(y_np), 1))

## /usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.
## If you want the future behaviour and silence this warning, you can specify "categories='auto'".
## In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.
##   warnings.warn(msg, FutureWarning)

x = tf.placeholder(tf.float64, shape=(None, 4), name='x')
y = tf.placeholder(tf.float64, shape=(None, 3), name='y')

with tf.variable_scope('layer1'):
    W0 = tf.Variable(tf.random_uniform([4, 3], -1, 1, dtype=tf.float64))
    b0 = tf.Variable(tf.random_uniform([1, 3], -1, 1, dtype=tf.float64))
    h = tf.nn.softmax(tf.matmul(x, W0) + b0)

with tf.variable_scope('layer2'):
    W1 = tf.Variable(tf.random_uniform([3, 3], -1, 1, dtype=tf.float64))
    b1 = tf.Variable(tf.random_uniform([1, 3], -1, 1, dtype=tf.float64))
    y_hat = tf.nn.softmax(tf.matmul(h, W1) + b1)

with tf.variable_scope('training'):
    error = tf.reduce_mean(tf.square(y - y_hat))
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
    _training = optimizer.minimize(error)

init = tf.global_variables_initializer()

batches = np.arange(150).reshape(5, 30)

with tf.Session() as sess:
    sess.run(init)
    for i in range(10000):
        for batch in batches:
            _training.run(feed_dict={x: X_np[batch], y: y_all[batch]})
        if not i % 1000:
            print(error.eval(feed_dict={x: X_np, y: y_all}))
    preds = y_hat.eval(feed_dict={x: X_np})

## 0.21975959109830537
## 0.11904729192377209
## 0.10227167131210398
## 0.08539185212415172
## 0.0729722627404747
## 0.06374164899760802
## 0.05665011390083331
## 0.051085784459838114
## 0.04665066048407872
## 0.04306578070666341

print(calculate_accuracy(preds))  # maybe overfitting?

## 0.9666666666666667

learning_rate = 0.01

iris = load_iris()
X_np, y_np = iris.data, iris.target

ohe = OneHotEncoder(sparse=False)
y_all = ohe.fit_transform(y_np.reshape(len(y_np), 1))

## /usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.
## If you want the future behaviour and silence this warning, you can specify "categories='auto'".
## In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.
##   warnings.warn(msg, FutureWarning)

x = tf.placeholder(tf.float64, shape=(None, 4), name='x')
y = tf.placeholder(tf.float64, shape=(None, 3), name='y')

def neural_layer(scope_name, x, input_size, output_size, func):
    with tf.variable_scope(scope_name):
        W_shape = [input_size, output_size]
        b_shape = [1, output_size]
        W = tf.Variable(tf.random_uniform(W_shape, -1, 1, dtype=tf.float64))
        b = tf.Variable(tf.random_uniform(b_shape, -1, 1, dtype=tf.float64))
        z = func(tf.matmul(x, W) + b)
    return z

h = neural_layer('layer1', x, 4, 3, tf.nn.relu)
y_hat = neural_layer('layer1', h, 3, 3, tf.nn.softmax)

with tf.variable_scope('training'):
    error = tf.reduce_mean(tf.square(y - y_hat))
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
    _training = optimizer.minimize(error)

init = tf.global_variables_initializer()

batches = np.arange(150).reshape(5, 30)

with tf.Session() as sess:
    sess.run(init)
    for i in range(10000):
        for batch in batches:
            _training.run(feed_dict={x: X_np[batch], y: y_all[batch]})
        if not i % 1000:
            print(error.eval(feed_dict={x: X_np, y: y_all}))
    preds = y_hat.eval(feed_dict={x: X_np})

## 0.2280080222473059
## 0.11025433869761539
## 0.06512683185010822
## 0.04186447027793009
## 0.03162009244973491
## 0.025917314681702063
## 0.0224913869009272
## 0.020198069180976715
## 0.018524208429439644
## 0.017234432524310606

print(calculate_accuracy(preds))  # maybe overfitting?

## 0.9733333333333334