In [38]:

    

import tensorflow as tf
import matplotlib.pyplot as plt


    

In [39]:

    

%matplotlib inline


    

In [40]:

    

interactive_sesssion=tf.InteractiveSession()


    

In [41]:

    

input_value = tf.constant(0.5,name="input_value")


    

In [42]:

    

weight = tf.Variable(1.0,name="weight")


    

In [43]:

    

expected_output = tf.constant(0.0,name="expected_output")


    

In [44]:

    

model = tf.multiply(input_value, weight,"model")


    

In [45]:

    

loss_function = (model - expected_output)**2


    

In [46]:

    

optim = tf.train.GradientDescentOptimizer(learning_rate=0.025)


    

In [47]:

    

train_step=optim.minimize(loss_function)


    

In [48]:

    

for value in [input_value,weight,expected_output,model,loss_function]:     
    tf.summary.scalar(value.op.name,value)


    

In [49]:

    

summaries = tf.summary.merge_all()


    

In [50]:

    

summary_writer = tf.summary.FileWriter('log_simple_stats',interactive_sesssion.graph)


    

In [51]:

    

init_op=tf.global_variables_initializer()


    

In [52]:

    

interactive_sesssion.run(init_op)


    

In [53]:

    

scatter_output_x=[]
scatter_output_y=[]


    

In [54]:

    

for i in range(500):
    interactive_sesssion.run(train_step)
    loss=interactive_sesssion.run(loss_function)
    w=interactive_sesssion.run(weight)
    print("loss is %f. weight is %f"%(loss,w))
    scatter_output_x.append(i)
    scatter_output_y.append(w)
    summary_writer.add_summary(interactive_sesssion.run(summaries),i)


    

    




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loss is 0.000001. weight is 0.002298
loss is 0.000001. weight is 0.002270
loss is 0.000001. weight is 0.002241
loss is 0.000001. weight is 0.002213
loss is 0.000001. weight is 0.002186
loss is 0.000001. weight is 0.002158
loss is 0.000001. weight is 0.002131
loss is 0.000001. weight is 0.002105
loss is 0.000001. weight is 0.002078
loss is 0.000001. weight is 0.002052
loss is 0.000001. weight is 0.002027
loss is 0.000001. weight is 0.002001
loss is 0.000001. weight is 0.001976
loss is 0.000001. weight is 0.001952
loss is 0.000001. weight is 0.001927
loss is 0.000001. weight is 0.001903
loss is 0.000001. weight is 0.001879
loss is 0.000001. weight is 0.001856

In [55]:

    

plt.scatter(scatter_output_x,scatter_output_y)


    

    
Out[55]:





<matplotlib.collections.PathCollection at 0x7fb62075b5c0>






    

In [ ]: