In [1]:

    

import tensorflow as tf


    

    




/home/minesh/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

In [2]:

    

W = tf.Variable([0.3])
b = tf.Variable([-0.3])
x = tf.placeholder(tf.float32)
linear_model = W * x + b


    

In [3]:

    

init = tf.global_variables_initializer()


    

In [4]:

    

sess = tf.Session()


    

In [5]:

    

sess.run(init)


    

In [6]:

    

print(sess.run(linear_model,{x:[1,2,3,4]}))


    

    




[0.         0.3        0.6        0.90000004]

In [7]:

    

y = tf.placeholder(tf.float32)


    

In [8]:

    

squared_deltas = tf.square(linear_model - y)
loss = tf.reduce_sum(squared_deltas)


    

In [9]:

    

print(sess.run(loss,{x:[1,2,3,4],y:[0,-1,-2,-3]}))


    

    




23.66

In [10]:

    

fixW = tf.assign(W,[-1])
fixb = tf.assign(b,[1])
sess.run([fixW,fixb])


    

    
Out[10]:





[array([-1.], dtype=float32), array([1.], dtype=float32)]

In [11]:

    

print(sess.run(loss,{x:[1,2,3,4],y:[0,-1,-2,-3]}))


    

    




0.0

In [12]:

    

optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)


    

In [13]:

    

sess.run(init)


    

In [14]:

    

for i in range(1000):
    sess.run(train,{x:[1,2,3,4],y:[0,-1,-2,-3]})
    if i % 100 ==0 :
        print(sess.run([W,b]))
        print("loss:",sess.run(loss,{x:[1,2,3,4],y:[0,-1,-2,-3]}))


    

    




[array([-0.21999997], dtype=float32), array([-0.45600003], dtype=float32)]
loss: 4.018144
[array([-0.84270465], dtype=float32), array([0.5375326], dtype=float32)]
loss: 0.14287975
[array([-0.9528499], dtype=float32), array([0.86137295], dtype=float32)]
loss: 0.012838208
[array([-0.98586655], dtype=float32), array([0.95844597], dtype=float32)]
loss: 0.0011535463
[array([-0.9957634], dtype=float32), array([0.98754394], dtype=float32)]
loss: 0.000103651124
[array([-0.9987301], dtype=float32), array([0.9962665], dtype=float32)]
loss: 9.312402e-06
[array([-0.99961936], dtype=float32), array([0.998881], dtype=float32)]
loss: 8.3645574e-07
[array([-0.9998859], dtype=float32), array([0.9996646], dtype=float32)]
loss: 7.514916e-08
[array([-0.9999658], dtype=float32), array([0.99989945], dtype=float32)]
loss: 6.753911e-09
[array([-0.9999897], dtype=float32), array([0.9999697], dtype=float32)]
loss: 6.12733e-10

In [15]:

    

tf.summary.histogram("W", W)
tf.summary.histogram("b", b)


    

    
Out[15]:





<tf.Tensor 'b:0' shape=() dtype=string>

In [16]:

    

writer = tf.summary.FileWriter("./my_graph_reg",sess.graph)
writer.close()


    

In [17]:

    

#sess.close()


    

In [20]:

    

!tensorboard --logdir=./my_graph_reg


    

    




/home/minesh/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
W0401 01:03:26.420831 Reloader tf_logging.py:121] Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events.  Overwriting the graph with the newest event.
W0401 01:03:26.421070 Reloader tf_logging.py:121] Found more than one metagraph event per run. Overwriting the metagraph with the newest event.
W0401 01:03:26.421443 Reloader tf_logging.py:121] Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events.  Overwriting the graph with the newest event.
W0401 01:03:26.421630 Reloader tf_logging.py:121] Found more than one metagraph event per run. Overwriting the metagraph with the newest event.
TensorBoard 1.5.1 at http://Immortal:6006 (Press CTRL+C to quit)
^C

In [19]:

    

# !rm -rf ./my_graph_reg


    

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