In [1]:

    

import numpy as np
import tensorflow as tf


    

In [4]:

    

a = np.array([1,2,3])


    

In [4]:

    

a


    

    
Out[4]:





array([1, 2, 3])

In [5]:

    

a.shape


    

    
Out[5]:





(3,)

In [7]:

    

b = np.array([[1,2,3],[4,5,6]])


    

In [8]:

    

b


    

    
Out[8]:





array([[1, 2, 3],
       [4, 5, 6]])

In [9]:

    

b.shape


    

    
Out[9]:





(2, 3)

In [10]:

    

a = np.zeros((2,2))


    

In [11]:

    

a


    

    
Out[11]:





array([[ 0.,  0.],
       [ 0.,  0.]])

In [12]:

    

b = np.ones((1,2))


    

In [13]:

    

b


    

    
Out[13]:





array([[ 1.,  1.]])

In [14]:

    

b.shape


    

    
Out[14]:





(1, 2)

In [15]:

    

b = np.array([1,1])


    

In [16]:

    

b


    

    
Out[16]:





array([1, 1])

In [17]:

    

b.shape


    

    
Out[17]:





(2,)

In [18]:

    

b = np.array([[1,1]])


    

In [19]:

    

b


    

    
Out[19]:





array([[1, 1]])

In [20]:

    

b.shape


    

    
Out[20]:





(1, 2)

In [21]:

    

a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])


    

In [22]:

    

a


    

    
Out[22]:





array([[ 1,  2,  3,  4],
       [ 5,  6,  7,  8],
       [ 9, 10, 11, 12]])

In [23]:

    

b = a[:2, 1:3] #righe 0 e 1, colonne 1 e 2


    

In [24]:

    

b


    

    
Out[24]:





array([[2, 3],
       [6, 7]])

In [25]:

    

a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])


    

In [26]:

    

row_r1 = a[1, :] #indexing reduces the rank


    

In [27]:

    

row_r1


    

    
Out[27]:





array([5, 6, 7, 8])

In [28]:

    

row_r2 = a[1:2, :] #slicing keep the same rank


    

In [29]:

    

row_r2


    

    
Out[29]:





array([[5, 6, 7, 8]])

In [34]:

    

col_r1 = a[:, 1] #second col


    

In [35]:

    

col_r1


    

    
Out[35]:





array([ 2,  6, 10])

In [36]:

    

col_r1.shape


    

    
Out[36]:





(3,)

In [38]:

    

col_r2 = a[:, 1:2]


    

In [39]:

    

col_r2


    

    
Out[39]:





array([[ 2],
       [ 6],
       [10]])

In [40]:

    

col_r2.shape


    

    
Out[40]:





(3, 1)

In [41]:

    

a = np.array([[1,2], [3, 4], [5, 6]])


    

In [42]:

    

a


    

    
Out[42]:





array([[1, 2],
       [3, 4],
       [5, 6]])

In [43]:

    

a.shape


    

    
Out[43]:





(3, 2)

In [47]:

    

print a[[0, 1, 2], [0, 1, 0]] #il primo set da' l'indice delle righe, il secondo delle colonne


    

    




[1 4 5]

In [50]:

    

x = np.array([[1,2],[3,4]], dtype=np.float64)


    

In [52]:

    

x.ndim


    

    
Out[52]:





2

In [53]:

    

a = np.array([0, 1, 2])


    

In [62]:

    

a


    

    
Out[62]:





array([0, 1, 2])

In [63]:

    

a.shape


    

    
Out[63]:





(3,)

In [64]:

    

a1 = np.tile(a, 2)


    

In [65]:

    

a1.shape


    

    
Out[65]:





(6,)

In [66]:

    

a2 = np.tile(a, (2, 2))


    

In [67]:

    

a2


    

    
Out[67]:





array([[0, 1, 2, 0, 1, 2],
       [0, 1, 2, 0, 1, 2]])

In [68]:

    

a2.shape


    

    
Out[68]:





(2, 6)

In [70]:

    

a.ndim


    

    
Out[70]:





1

In [71]:

    

a3 = np.tile(a, (2, 1, 2))


    

In [72]:

    

a3


    

    
Out[72]:





array([[[0, 1, 2, 0, 1, 2]],

       [[0, 1, 2, 0, 1, 2]]])

In [73]:

    

a3.ndim


    

    
Out[73]:





3

In [74]:

    

a3.shape


    

    
Out[74]:





(2, 1, 6)

In [78]:

    

b = np.array([[1, 2], [3, 4]])
b


    

    
Out[78]:





array([[1, 2],
       [3, 4]])

In [76]:

    

b.shape


    

    
Out[76]:





(2, 2)

In [79]:

    

b1 = np.tile(b, 2)


    

In [84]:

    

b1.shape


    

    
Out[84]:





(2, 4)

In [81]:

    

b2 = np.tile(b, (2, 1))


    

In [82]:

    

b2


    

    
Out[82]:





array([[1, 2],
       [3, 4],
       [1, 2],
       [3, 4]])

In [83]:

    

b2.shape


    

    
Out[83]:





(4, 2)

In [85]:

    

x = np.array([[[1],[2],[3]], [[4],[5],[6]]])


    

In [86]:

    

x


    

    
Out[86]:





array([[[1],
        [2],
        [3]],

       [[4],
        [5],
        [6]]])

In [87]:

    

x.shape


    

    
Out[87]:





(2, 3, 1)

In [5]:

    

x1 = np.array([[[1,2,3], [4,5,6]], [[1,2,3], [4,5,6]]])


    

In [6]:

    

x1


    

    
Out[6]:





array([[[1, 2, 3],
        [4, 5, 6]],

       [[1, 2, 3],
        [4, 5, 6]]])

In [7]:

    

x1.shape


    

    
Out[7]:





(2, 2, 3)

In [96]:

    

x1.ndim


    

    
Out[96]:





3

In [9]:

    

x1.shape[2]


    

    
Out[9]:





3

In [98]:

    

x1 = np.array([1, 2, 3, 4, 5])
x2 = np.array([5, 4, 3])


    

In [99]:

    

x1


    

    
Out[99]:





array([1, 2, 3, 4, 5])

In [100]:

    

x2


    

    
Out[100]:





array([5, 4, 3])

In [101]:

    

x1_new = x1[:, np.newaxis]


    

In [102]:

    

x1_new


    

    
Out[102]:





array([[1],
       [2],
       [3],
       [4],
       [5]])

In [104]:

    

x3 = x1_new + x2


    

In [106]:

    

x3


    

    
Out[106]:





array([[ 6,  5,  4],
       [ 7,  6,  5],
       [ 8,  7,  6],
       [ 9,  8,  7],
       [10,  9,  8]])

In [4]:

    

tf1 = tf.zeros([2, 3])


    

In [6]:

    

tf1.shape


    

    
Out[6]:





TensorShape([Dimension(2), Dimension(3)])

In [7]:

    

tf2 = tf.ones([2, 3,4])


    

In [8]:

    

tf2.shape


    

    
Out[8]:





TensorShape([Dimension(2), Dimension(3), Dimension(4)])

In [9]:

    

tf2


    

    
Out[9]:





<tf.Tensor 'ones:0' shape=(2, 3, 4) dtype=float32>

In [12]:

    

tf3 = tf.placeholder(tf.float32, shape=[None,3], name="train_inputs")


    

In [13]:

    

tf3


    

    
Out[13]:





<tf.Tensor 'train_inputs:0' shape=(?, 3) dtype=float32>

In [ ]: