1. 雲端計算

2. Tensorflow
Python3.5 while

3. Tensorflow

4. tensorflow結構

5. hello world from __future__ import print_function
import tensorflow as tf
try:
tf.contrib.eager.enable_eager_execution()
except ValueError:
pass # enable_eager_execution errors after its first call
tensor = tf.constant('Hello, world!')
tensor_value = tensor.numpy()
print(tensor_value)

6. tensors 張量 任意維度的陣列 scalar純量: 0-d array vector向量: 1-d array
‘hello’ or 5
vector向量: 1-d array
[2, 3, 5, 7, 11] or [5]
matrix矩陣: 2-d array
[[3.1, 8.2, 5.9][4.3, -2.7, 6.5]]

7. constant import tensorflow as tf x = tf.constant(5.2)
y = tf.Variable([5])
y = tf.Variable([0])
y = y.assign([5])
with tf.Session() as sess:
print(x.eval())

8. tf.add() from __future__ import print_function import tensorflow as tf
g = tf.Graph()
with g.as_default():
x = tf.constant(8, name="x_const")
y = tf.constant(5, name="y_const")
my_sum = tf.add(x, y, name="x_y_sum")
with tf.Session() as sess:
print(my_sum.eval())

9. Define a third scalar integer constant, z, and assign it a value of 4
Define a third scalar integer constant, z, and assign it a value of 4. Add z to my_sum to yield a new sum.
Solution:
import tensorflow as tf
g = tf.Graph()
with g.as_default():
x = tf.constant(8, name="x_const")
y = tf.constant(5, name="y_const")
my_sum = tf.add(x, y, name="x_y_sum")
z = tf.constant(4, name="z_const")
new_sum = tf.add(my_sum, z, name="x_y_z_sum")
with tf.Session() as sess:
print(new_sum.eval())

10. Vector Addition 向量加法 import tensorflow as tf
with tf.Session() as sess:
primes = tf.constant([2, 3, 5, 7, 11, 13], dtype=tf.int32)
print("primes:", primes.eval())
ones = tf.ones([6], dtype=tf.int32)
print("ones:", ones.eval())
just_beyond_primes = tf.add(primes, ones)
print("just_beyond_primes:", just_beyond_primes.eval())
twos = tf.constant([2, 2, 2, 2, 2, 2], dtype=tf.int32)
primes_doubled = primes * twos
print("primes_doubled:", primes_doubled.eval())

11. shape import tensorflow as tf with tf.Session() as sess:
# A scalar (0-D tensor).
scalar = tf.zeros([])
# A vector with 3 elements.
vector = tf.zeros([3])
# A matrix with 2 rows and 3 columns.
matrix = tf.zeros([2, 3])
print('scalar has shape', scalar.get_shape(), 'and value:\n', scalar.eval())
print('vector has shape', vector.get_shape(), 'and value:\n', vector.eval())
print('matrix has shape', matrix.get_shape(), 'and value:\n', matrix.eval())

12. matrix multiplication 矩陣乘法
import tensorflow as tf
with tf.Session() as sess:
# A 3x4 matrix (2-d tensor).
x = tf.constant([[5, 2, 4, 3], [5, 1, 6, -2], [-1, 3, -1, -2]], dtype=tf.int32)
# A 4x2 matrix (2-d tensor).
y = tf.constant([[2, 2], [3, 5], [4, 5], [1, 6]], dtype=tf.int32)
# Multiply `x` by `y`; result is 3x2 matrix.
matrix_multiply_result = tf.matmul(x, y)
print(‘3x4 matrix:’)
print(x.eval())
print(‘4x2 matrix:’)
print(y.eval())
print(matrix_multiply_result.eval())

13. reshaping import tensorflow as tf matrix = tf.constant(
[[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]],
dtype=tf.int32)
reshaped_2x8_matrix = tf.reshape(matrix, [2, 8])
reshaped_4x4_matrix = tf.reshape(matrix, [4, 4])
print("Original matrix (8x2):")
print(matrix.eval())
print("Reshaped matrix (2x8):")
print(reshaped_2x8_matrix.eval())
print("Reshaped matrix (4x4):")
print(reshaped_4x4_matrix.eval())

14. 驗收
a = tf.constant([5, 3, 2, 7, 1, 4])
b = tf.constant([4, 6, 3])

15. Python3.5 while

16. del remove()

17. pop()

18. sorted() 回傳一個排序好新的list，可用來排序任何的string, list, dictionary, tuple...
預設: 從小排到大 參數
reverse=True :從大排到小

19. 串列的串列
不換行

20. while

21. while…else…

22. break

23. continue

24. random

25. 驗收
Create 20 random value in a list
*use pop()

26. 驗收
Create 20 random value in a list
*use pop()