## TensorFlow linear regression model calculations

Beginner TensorFlow

Not much to say directly bonded to the code

Copy of the code, comments, their own understanding, welcomed the exchange

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

def normalize(X):
""""Normalize the array"""
mean = np.mean(X)
std = np.std(X);
X = (X-mean)/std
return X

#   http://c.biancheng.net/view/1906.html

X_train, Y_train boston.data = [:,. 5], boston.target # data samples assignment
N_SAMPLES = len (X_train) # reads the number of the training set
X-tf.placeholder = (tf.float32, name = ' X- ' ) # variable Definition model =====> model W * = X + Y B
the Y = tf.placeholder (tf.float32, name = ' the Y ' )
B = tf.Variable (0.0 )
W = tf.Variable ( 0.0 )
# define end

Y_hat = * W + X- B
loss = tf.square (the Y - Y_hat, name = ' loss ' ) # define loss function
= tf.train.GradientDescentOptimizer Optimizer (learning_rate = 0.01) .minimize (Loss) # optimal solution for the loss function, the present process is the key to solving the optimal operational function
init_op tf.global_variables_initializer = () # initialize
Total = []
tf.Session with () AS Sess: # use with the method calculates the tf.Session () returns the result is placed in Sess
sess.run (init_op)   # initialization run
Writer = tf.summary.FileWriter ( ' Graph ' , Sess .graph) # generates operation log file, the algorithm can be viewed constructed ===> to view: at the command line D: \ PythonProject \ TensorFlow> tensorboard --logdir = graph, graph Filewriter input parameters will return the URL of replication paste
for I in Range (100 ):
total_loss =0
for X, Y in ZIP (X_train, Y_train):
_, L = sess.run ([Optimizer, Loss], = {X-feed_dict: X, the Y: Y}) # calculated error value
total_loss = L + # this is "L" return error accumulates
total.append (total_loss / N_SAMPLES) # not explained
Print ( ' Epoch {0}: Loss. 1 {} ' .format (I, total_loss / N_SAMPLES))
writer.Close ()
b_value, w_value = sess.run ([B, W])
y_pred = X_train w_value * + b_value
Print ( ' the Done ' )
plt.plot(X_train, Y_train, 'bo',label='Real data')
plt.plot(X_train, Y_pred, 'r', label='Predicted Data')
plt.legend()
plt.show()
plt.plot(total)
plt.show()

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Origin www.cnblogs.com/JasssonNill-CNSDJN2011-2018/p/11334984.html
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