## 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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