In [1]:

    

import keras


    

    




Using TensorFlow backend.

In [2]:

    

import numpy as np


    

In [3]:

    

x = np.array(range(100))


    

In [4]:

    

x


    

    
Out[4]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])

In [5]:

    

y = x * 2 + 1


    

In [6]:

    

y


    

    
Out[6]:





array([  1,   3,   5,   7,   9,  11,  13,  15,  17,  19,  21,  23,  25,
        27,  29,  31,  33,  35,  37,  39,  41,  43,  45,  47,  49,  51,
        53,  55,  57,  59,  61,  63,  65,  67,  69,  71,  73,  75,  77,
        79,  81,  83,  85,  87,  89,  91,  93,  95,  97,  99, 101, 103,
       105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129,
       131, 133, 135, 137, 139, 141, 143, 145, 147, 149, 151, 153, 155,
       157, 159, 161, 163, 165, 167, 169, 171, 173, 175, 177, 179, 181,
       183, 185, 187, 189, 191, 193, 195, 197, 199])

In [7]:

    

model = keras.models.Sequential()


    

In [8]:

    

model.add(keras.layers.Dense(1, input_shape = (1,)))


    

In [9]:

    

model.compile('Adam', 'mse')


    

In [10]:

    

model.fit(x, y, epochs=1000, verbose=0)


    

    
Out[10]:





<keras.callbacks.History at 0x11b58ea58>

In [11]:

    

x[:2]


    

    
Out[11]:





array([0, 1])

In [12]:

    

y[:2]


    

    
Out[12]:





array([1, 3])

In [13]:

    

print('Targets : ', y[2:5])
print('Predictions : ', model.predict(np.array([2,3,4])).flatten())


    

    




Targets :  [5 7 9]
Predictions :  [ 6.13070774  8.00952244  9.88833714]

In [14]:

    

x.shape


    

    
Out[14]:





(100,)

In [15]:

    

np.array(4).shape


    

    
Out[15]:





()

In [16]:

    

np.array([4]).shape


    

    
Out[16]:





(1,)

In [ ]: