lab 008-lab. manipulate tensor
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# lab-08-tensor_manipulation.ipynb
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# Tensor Manipulation
# https://www.tensorflow.org/api_guides/python/array_ops
import tensorflow as tf
import numpy as np
import pprint
tf.set_random_seed(777)
pprint_object=pprint.PrettyPrinter(indent=4)
interactive_session_object=tf.InteractiveSession()
# @
one_d_array=np.array([0.,1.,2.,3.,4.,5.,6.])
pprint_object.pprint(one_d_array)
# array([0., 1., 2., 3., 4., 5., 6.])
# This shows rank(dimension)
print(one_d_array.ndim)
# 1
# This shows shape
print(one_d_array.shape)
# (7,)
print(one_d_array[0],one_d_array[1],one_d_array[-1])
# 0.0 1.0 6.0
print(one_d_array[2:5],one_d_array[4:-1])
# [ 2. 3. 4.] [ 4. 5.]
print(one_d_array[:2],one_d_array[3:])
# [ 0. 1.] [ 3. 4. 5. 6.]
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# 2D Array
two_d_array=np.array([[1.,2.,3.],[4.,5.,6.],[7.,8.,9.],[10.,11.,12.]])
pprint_object.pprint(two_d_array)
# array([[ 1., 2., 3.],
# [ 4., 5., 6.],
# [ 7., 8., 9.],
# [ 10., 11., 12.]])
# rank
print(two_d_array.ndim)
# 2
# shape
print(two_d_array.shape)
# (4,3)
# @
# Shape,Rank,Axis
constant_node=tf.constant([1,2,3,4])
# rank:1,shape: (4,)
constant_node=tf.constant([[1,2],
[3,4]])
# rank:2 [[, shape: (2,2)
# If rank is 2, shape is (?1,?2)
# ?2: number of elements in deepest square bracket
# [1,2]=X,[3,4]=Y,so,(2,2)
constant_node=tf.constant([[[[1,2,3,4],[5,6,7,8],[9,10,11,12]],[[13,14,15,16],[17,18,19,20],[21,22,23,24]]]])
# rank: [[[[ 4
# [1,2,3,4]: 4
# [1,2,3,4],[5,6,7,8],[9,10,11,12]: 3
# [[1,2,3,4],[5,6,7,8],[9,10,11,12]],[[13,14,15,16],[17,18,19,20],[21,22,23,24]]: 2
# [[[1,2,3,4],[5,6,7,8],[9,10,11,12]],[[13,14,15,16],[17,18,19,20],[21,22,23,24]]]: 1
# shape=(1,2,3,4,)
# Let's talk about axis
# If rank is 4, axis is also 4
# axis starts with 0
# [1,2,3,4]: axis=3 or axis=-1
# Highest axis is deepest one
# axis=2
# [
# [1,2,3,4],
# [5,6,7,8],
# [9,10,11,12]
# ]
# axis=1
# [
# [
# [1,2,3,4],
# [5,6,7,8],
# [9,10,11,12]
# ],
# [
# [13,14,15,16],
# [17,18,19,20],
# [21,22,23,24]
# ]
# ]
# axis=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]
# ]
# ]
# ]
# [[[[1,2,3,4],[5,6,7,8],[9,10,11,12]],
# [[13,14,15,16],[17,18,19,20],[21,22,23,24]]]]
# @
# Matmul VS multiply
# rank: 2, shape: (1,2)
matrix1_node=tf.constant([[3.,3.]])
# rank: 2,shape: (2,1)
matrix2_node=tf.constant([[2.],[2.]])
# (1,2)*(2,1)=(1,1)
tf.matmul(matrix1_node,matrix2_node).eval()
# @
# Broadcasting(you should use it carefully)
# rank: 2, shape: (1,2)
matrix1_node=tf.constant([[3.,3.]])
# rank: 2, shape: (2,1)
matrix2_node=tf.constant([[2.],[2.]])
# We implicitly use broadcasting
# [1,2]+[2,1]
(matrix1_node+matrix2_node).eval()
# array([[ 5., 5.],
# [ 5., 5.]],dtype=float32)
matrix1_node=tf.constant([[3.,3.]])
matrix2_node=tf.constant([[2.,2.]])
(matrix1_node+matrix2_node).eval()
# array([[ 5., 5.]],dtype=float32)
# @
# Random values for variable initializations
tf.random_normal([3]).eval()
# array([ 2.20866942,-0.73225045, 0.33533147],dtype=float32)
tf.random_uniform([2]).eval()
# array([ 0.08186948, 0.42999184],dtype=float32)
tf.random_uniform([2,3]).eval()
# array([[ 0.43535876, 0.76933432, 0.65130949],
# [ 0.90863407, 0.06278825, 0.85073185]],dtype=float32)
# @
# Reduce mean/sum
tf.reduce_mean([1,2],axis=0).eval()
# 1
x=[[1.,2.],
[3.,4.]]
tf.reduce_mean(x).eval()
# 2.5
# You find mean of column data
tf.reduce_mean(x,axis=0).eval()
# 1+3/2,2+4/6
# array([ 2., 3.],dtype=float32)
tf.reduce_mean(x,axis=1).eval()
# array([ 1.5, 3.5],dtype=float32)
tf.reduce_mean(x,axis=-1).eval()
# array([ 1.5, 3.5],dtype=float32)
tf.reduce_sum(x).eval()
# 10.0
tf.reduce_sum(x,axis=0).eval()
# array([ 4., 6.],dtype=float32)
tf.reduce_sum(x,axis=-1).eval()
# array([ 3., 7.],dtype=float32)
tf.reduce_mean(tf.reduce_sum(x,axis=-1)).eval()
# 5.0
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# Argmax with axis
x=[[0,1,2],
[2,1,0]]
tf.argmax(x,axis=0).eval()
# array([1,0,0])
tf.argmax(x,axis=1).eval()
# array([2,0])
tf.argmax(x,axis=-1).eval()
# array([2,0])
# @
# Reshape, squeeze, expand_dims
# rank: 3,shape=(2,2,3)
constant_node=np.array([[[0,1,2],
[3,4,5]],
[[6,7,8],
[9,10,11]]])
# I want to reshape with
# rank: 2
# deepest: 3
# you write whatever for remainders like -1
tf.reshape(constant_node,shape=[-1,3]).eval()
# array([[ 0, 1, 2],
# [ 3, 4, 5],
# [ 6, 7, 8],
# [ 9,10,11]])
# I want 3 rank,
# deepest is 3,
# then, 1 group
tf.reshape(constant_node,shape=[-1,1,3]).eval()
# array([[[ 0, 1, 2]],
# [[ 3, 4, 5]],
# [[ 6, 7, 8]],
# [[ 9,10,11]]])
tf.squeeze([[0],[1],[2]]).eval()
# array([0,1,2],dtype=int32)
tf.expand_dims([0,1,2],1).eval()
# array([[0],
# [1],
# [2]],dtype=int32)
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# One hot automatically increases 1 dimension
tf.one_hot([[0],[1],[2],[0]],depth=3).eval()
# array([[[ 1., 0., 0.]],
# [[ 0., 1., 0.]],
# [[ 0., 0., 1.]],
# [[ 1., 0., 0.]]],dtype=float32)
constant_node=tf.one_hot([[0],[1],[2],[0]],depth=3)
tf.reshape(constant_node,shape=[-1,3]).eval()
# array([[ 1., 0., 0.],
# [ 0., 1., 0.],
# [ 0., 0., 1.],
# [ 1., 0., 0.]],dtype=float32)
# @
# casting
tf.cast([1.8,2.2,3.3,4.9],tf.int32).eval()
# array([1,2,3,4],dtype=int32)
tf.cast([True,False,1==1,0==1],tf.int32).eval()
# array([1,0,1,0],dtype=int32)
# @
# Stack
x=[1,4]
y=[2,5]
z=[3,6]
# Pack along first dim.
tf.stack([x,y,z]).eval()
# array([[1,4],
# [2,5],
# [3,6]],dtype=int32)
tf.stack([x,y,z],axis=1).eval()
# array([[1,2,3],
# [4,5,6]],dtype=int32)
# @
# Ones like and Zeros like
x=[[0,1,2],
[2,1,0]]
tf.ones_like(x).eval()
# array([[1,1,1],
# [1,1,1]],dtype=int32)
tf.zeros_like(x).eval()
# array([[0,0,0],
# [0,0,0]],dtype=int32)
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# Zip
for x,y in zip([1,2,3],[4,5,6]):
print(x,y)
# (x,y)=((1,4),(2,5),(3,6))
# 1 4
# 2 5
# 3 6
for x,y,z in zip([1,2,3],[4,5,6],[7,8,9]):
print(x,y,z)
# 1 4 7
# 2 5 8
# 3 6 9
# @
# Transpose
constant_node=np.array([[[0,1,2],[3,4,5]],[[6,7,8],[9,10,11]]])
pprint_object.pprint(constant_node.shape)
# (2,2,3)
pprint_object.pprint(constant_node)
# array([[[ 0, 1, 2],
# [ 3, 4, 5]],
# [[ 6, 7, 8],
# [ 9,10,11]]])
t1=tf.transpose(constant_node,[1,0,2])
pprint_object.pprint(interactive_session_object.run(t1).shape)
# (2,2,3)
pprint_object.pprint(interactive_session_object.run(t1))
# array([[[ 0, 1, 2],
# [ 6, 7, 8]],
# [[ 3, 4, 5],
# [ 9,10,11]]])
constant_node=tf.transpose(t1,[1,0,2])
pprint_object.pprint(interactive_session_object.run(constant_node).shape)
# (2,2,3)
pprint_object.pprint(interactive_session_object.run(constant_node))
# array([[[ 0, 1, 2],
# [ 3, 4, 5]],
# [[ 6, 7, 8],
# [ 9,10,11]]])
t2=tf.transpose(constant_node,[1,2,0])
pprint_object.pprint(interactive_session_object.run(t2).shape)
# (2,3,2)
pprint_object.pprint(interactive_session_object.run(t2))
# array([[[ 0, 6],
# [ 1, 7],
# [ 2, 8]],
# [[ 3, 9],
# [ 4,10],
# [ 5,11]]])
constant_node=tf.transpose(t2,[2,0,1])
pprint_object.pprint(interactive_session_object.run(constant_node).shape)
# (2,2,3)
pprint_object.pprint(interactive_session_object.run(constant_node))
# array([[[ 0, 1, 2],
# [ 3, 4, 5]],
# [[ 6, 7, 8],
# [ 9,10,11]]])
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