In [1]:

    

import numpy as np
import tensorflow as tf


    

In [2]:

    

np.random.seed(101)
tf.set_random_seed(101)


    

In [3]:

    

rand_a = np.random.uniform(0, 100, (5, 5))


    

In [4]:

    

rand_a


    

    
Out[4]:





array([[ 51.63986277,  57.06675869,   2.84742265,  17.15216562,
         68.52769817],
       [ 83.38968626,  30.69662197,  89.36130797,  72.15438618,
         18.99389542],
       [ 55.42275911,  35.2131954 ,  18.18924027,  78.56017619,
         96.54832224],
       [ 23.23536618,   8.35614337,  60.35484223,  72.89927573,
         27.62388285],
       [ 68.53063288,  51.78674742,   4.84845374,  13.78692376,
         18.69674261]])

In [5]:

    

rand_b = np.random.uniform(0, 100, (5, 1))


    

In [6]:

    

rand_b


    

    
Out[6]:





array([[ 99.43179012],
       [ 52.06653967],
       [ 57.87895355],
       [ 73.48190583],
       [ 54.19617722]])

In [7]:

    

a = tf.placeholder(tf.float32)


    

In [8]:

    

b = tf.placeholder(tf.float32)


    

In [9]:

    

add_op = a + b


    

In [10]:

    

mul_op = a * b


    

In [12]:

    

with tf.Session() as sess:
    
    add_result = sess.run(add_op, feed_dict={a: rand_a, b: rand_b})
    mult_result = sess.run(mul_op, feed_dict={a: rand_a, b: rand_b})
    print(add_result)
    print("\n")
    print(mult_result)


    

    




[[ 151.07165527  156.49855042  102.27921295  116.58396149  167.95948792]
 [ 135.45622253   82.76316071  141.42784119  124.22093201   71.06043243]
 [ 113.30171204   93.09214783   76.06819153  136.43911743  154.42727661]
 [  96.7172699    81.83804321  133.83674622  146.38117981  101.10578918]
 [ 122.72680664  105.98292542   59.04463196   67.98310089   72.89292145]]


[[ 5134.64404297  5674.25         283.12432861  1705.47070312
   6813.83154297]
 [ 4341.8125      1598.26696777  4652.73388672  3756.8293457    988.9463501 ]
 [ 3207.8112793   2038.10290527  1052.77416992  4546.98046875
   5588.11572266]
 [ 1707.37902832   614.02526855  4434.98876953  5356.77734375
   2029.85546875]
 [ 3714.09838867  2806.64379883   262.76763916   747.19854736
   1013.29199219]]

In [13]:

    

n_features = 10
n_dense_neurons = 3


    

In [14]:

    

x = tf.placeholder(tf.float32, (None, n_features))


    

In [17]:

    

W = tf.Variable(tf.random_normal([n_features, n_dense_neurons]))
b = tf.Variable(tf.ones([n_dense_neurons]))


    

In [20]:

    

xW = tf.matmul(x, W)


    

In [21]:

    

z = tf.add(xW, b)


    

In [22]:

    

a = tf.sigmoid(z)


    

In [23]:

    

init = tf.global_variables_initializer()


    

In [26]:

    

with tf.Session() as sess:
    
    sess.run(init)
    layer_out = sess.run(a, feed_dict={x: np.random.random([1, n_features])})


    

In [27]:

    

print(layer_out)


    

    




[[ 0.73805881  0.78257018  0.0646597 ]]

In [29]:

    

x_data = np.linspace(0, 10, 10) + np.random.uniform(-1.5, 1.5, 10)


    

In [33]:

    

import matplotlib.pyplot as plt
%matplotlib inline


    

In [34]:

    

y_label = np.linspace(0, 10, 10) + np.random.uniform(-1.5, 1.5, 10)


    

In [36]:

    

plt.plot(x_data, y_label, "*")


    

    
Out[36]:





[<matplotlib.lines.Line2D at 0x7fa52c544f60>]






    

In [38]:

    

np.random.rand(2)


    

    
Out[38]:





array([ 0.78452185,  0.68724204])

In [47]:

    

m = tf.Variable(0.78452185)
b = tf.Variable(0.68724204)


    

In [48]:

    

error = 0

for x, y in zip(x_data, y_label):
    y_hat = m*x + b
    error += (y - y_hat)**2 # squared error


    

In [49]:

    

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001) # gradient descent optimizer


    

In [50]:

    

train = optimizer.minimize(error)


    

In [53]:

    

init = tf.global_variables_initializer()


    

In [54]:

    

with tf.Session() as sess:
    
    sess.run(init)
    
    training_steps = 1
    
    for i in range(training_steps):
        sess.run(train)
        
    final_slope, final_intercept = sess.run([m, b])


    

In [55]:

    

x_test = np.linspace(-1, 11, 10)
y_pred_plot = final_slope * x_test + final_intercept


    

In [57]:

    

plt.plot(x_test, y_pred_plot, "r")
plt.plot(x_data, y_label, "*")


    

    
Out[57]:





[<matplotlib.lines.Line2D at 0x7fa527b43e10>]






    

In [ ]: