Linear Regression from Text with Tensorflow

github : Linear Regression from Text with Tensorflow
data : data

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

Read the data file Linear Regression from Text with Tensorflow.ipynb
data
└── birth_life_2010.txt

birth_life_2010.txt

Country	Birth rate	Life expectancy
Vietnam	1.822	74.828243902
Vanuatu	3.869	70.819487805
…	…	…

DATA_FILE = "./data/birth_life_2010.txt"

def read_birth_file_data(filename):
    """
    Read in birth_life_2010.txt and return:
    data in the form of NumPy array
    n_samples: number of samples
    """        
    "readline from 1 row (except 0 row : category)"
    text = open(filename, 'r').readlines()[1:]   
    "Split each line with '\t'"
    data = [line[:-1].split('\t') for line in text]
    "Select the column 1 of birth"
    births = [float(line[1]) for line in data]
    "Select the column 2 of lifes"
    lifes = [float(line[2]) for line in data]
    "Zip birth & lifes"
    data = list(zip(births, lifes))
    "The number of samples"
    n_samples = len(data)
    "Transform data type from list to np.array"
    data = np.asarray(data, dtype=np.float32)
    
    return data, n_samples

Step 1 : Read in data from the .txt file

data, n_samples = read_birth_file_data(DATA_FILE)

Step 2: Create placeholders for X (birth rate) and Y (life expectancy)

X = tf.placeholder(tf.float32, name = "X")
Y = tf.placeholder(tf.float32, name = "Y")

Step 3 : create weight and bias, initialized to 0

w = tf.get_variable("weights", initializer=tf.constant(0.0))
b = tf.get_variable("bias", initializer=tf.constant(0.0))

Step 4 : build model to predict Y

hypothesis = w * X + b

Step 5 : use the squared error as the loss function

loss = tf.reduce_mean(tf.square(Y - hypothesis, name = 'loss'))

Step 6 : Using gradient descent with learning rate of 0.001 to minimize loss

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)

Stetp 7 : initialize the necessary variables, in this case, w and b

Stetp 8 : train the model for 100 epochs

Stetp 9 : output the values of w and b

see below code.

writer = tf.summary.FileWriter("./graphs/linear_Regression_Birth_Life", tf.get_default_graph())

with tf.Session() as sess:
    # Stetp 7 : initialize the necessary variables, in this case, w and b
    sess.run(tf.global_variables_initializer())
    
    # Stetp 8 : train the model for 100 epochs
    for i in range(100):
        total_loss = 0
        for x, y in data:
            # Session execute optimizer and fetch values of loss
            _, _loss = sess.run([optimizer, loss], feed_dict = {X:x, Y:y})
            total_loss += _loss
        print("Epoch {0} : {1}".format(i, total_loss / n_samples))
    
    # close the writer when you're done using it
    writer.close()
    
    # Step 9 : output the values of w and b
    w_out, b_out = sess.run([w, b])   

Epoch 0 : 1661.8637834631543
Epoch 1 : 956.3224148609137
Epoch 2 : 844.6737023980994
Epoch 3 : 750.7312486011339
Epoch 4 : 667.6598341012079
Epoch 5 : 594.1417715627896
Epoch 6 : 529.07878103068
Epoch 7 : 471.5004191489204
Epoch 8 : 420.5458626462441
Epoch 9 : 375.45530721966765
Epoch 10 : 335.5543025185697
Epoch 11 : 300.24629857978107
Epoch 12 : 269.00376475843336
Epoch 13 : 241.35957466852116
Epoch 14 : 216.90039135300015
Epoch 15 : 195.25972298129324
Epoch 16 : 176.1137693605349
Epoch 17 : 159.17551693441837
Epoch 18 : 144.1907111125557
Epoch 19 : 130.93503488078713
Epoch 20 : 119.20935661137888
Epoch 21 : 108.8379309807855
Epoch 22 : 99.66466760624593
Epoch 23 : 91.55177013029001
Epoch 24 : 84.37664046781751
Epoch 25 : 78.03217824997724
Epoch 26 : 72.42182927812989
Epoch 27 : 67.46136239485718
Epoch 28 : 63.07566952367442
Epoch 29 : 59.19874146522856
Epoch 30 : 55.77168446383194
Epoch 31 : 52.74269822355127
Epoch 32 : 50.065632780875376
Epoch 33 : 47.70006421631674
Epoch 34 : 45.61017902122909
Epoch 35 : 43.76379750625255
Epoch 36 : 42.13259221098116
Epoch 37 : 40.69221939330516
Epoch 38 : 39.420219863367905
Epoch 39 : 38.297008645340895
Epoch 40 : 37.305591759538146
Epoch 41 : 36.43066341609841
Epoch 42 : 35.658453942681234
Epoch 43 : 34.97724816803575
Epoch 44 : 34.37655378567349
Epoch 45 : 33.84671358035044
Epoch 46 : 33.379665882282545
Epoch 47 : 32.96800991297258
Epoch 48 : 32.60548541990942
Epoch 49 : 32.28618434173986
Epoch 50 : 32.004961317298495
Epoch 51 : 31.757531331044525
Epoch 52 : 31.53978877073019
Epoch 53 : 31.348356819100445
Epoch 54 : 31.180119247269193
Epoch 55 : 31.03225782010038
Epoch 56 : 30.902462910201574
Epoch 57 : 30.78859985760776
Epoch 58 : 30.688725355066556
Epoch 59 : 30.60122861903357
Epoch 60 : 30.524590178362192
Epoch 61 : 30.457532704476954
Epoch 62 : 30.398967422668726
Epoch 63 : 30.34777825418737
Epoch 64 : 30.303121465726413
Epoch 65 : 30.26424930739051
Epoch 66 : 30.230392129550456
Epoch 67 : 30.200964921590334
Epoch 68 : 30.175501555469697
Epoch 69 : 30.153343991707324
Epoch 70 : 30.134226098457216
Epoch 71 : 30.117758308603477
Epoch 72 : 30.103543774372174
Epoch 73 : 30.09139442229674
Epoch 74 : 30.0809388476427
Epoch 75 : 30.07208499982095
Epoch 76 : 30.06452690966084
Epoch 77 : 30.058150938555205
Epoch 78 : 30.05278219980139
Epoch 79 : 30.04828310612785
Epoch 80 : 30.04458791257593
Epoch 81 : 30.041550708114855
Epoch 82 : 30.039046437352113
Epoch 83 : 30.03704103724602
Epoch 84 : 30.03545715799831
Epoch 85 : 30.034288759106282
Epoch 86 : 30.03338805212261
Epoch 87 : 30.032769865304076
Epoch 88 : 30.032386838833535
Epoch 89 : 30.032150670733166
Epoch 90 : 30.032092865493677
Epoch 91 : 30.032186730024037
Epoch 92 : 30.03240725137661
Epoch 93 : 30.032643962397827
Epoch 94 : 30.033039376884087
Epoch 95 : 30.033435566514413
Epoch 96 : 30.033922631802085
Epoch 97 : 30.03442924663878
Epoch 98 : 30.0349335548615
Epoch 99 : 30.03552558278714

# plot the results
plt.plot(data[:,0], data[:,1], "bo", label = "Real Data")
plt.plot(data[:,0], data[:,0] * w_out + b_out, 'r', label = "Predicted data")
plt.legend()
plt.show()

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