在python中for i in range是什么意思-range在python中是什么意思

时间:2024-11-14 07:18:06

python range() 函数可创建一个整数列表,一般用在 for 循环中。

函数语法range(start, stop[, step])

参数说明:

start: 计数从 start 开始。默认是从 0 开始。例如range(5)等价于range(0, 5);

stop: 计数到 stop 结束,但不包括 stop。例如:range(0, 5) 是[0, 1, 2, 3, 4]没有5

step:步长,默认为1。例如:range(0, 5) 等价于 range(0, 5, 1)

实例

>>>range(10) # 从 0 开始到 10

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

>>> range(1, 11) # 从 1 开始到 11

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

>>> range(0, 30, 5) # 步长为 5

[0, 5, 10, 15, 20, 25]

>>> range(0, 10, 3) # 步长为 3

[0, 3, 6, 9]

>>> range(0, -10, -1) # 负数

[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]

>>> range(0)

[]

>>> range(1, 0)

[]

以下是 range 在 for 中的使用,循环出runoob 的每个字母:

>>>x = 'runoob'

>>> for i in range(len(x)) :

... print(x[i])

...

r

u

n

o

o

b

>>>

在tensorflow python 3.6的环境下,range函数中实参必须为int型,否则报错

def load_dataset(data_dir, img_size):

"""img_files = (data_dir)

test_size = int(len(img_files)*0.2)

test_indices = (range(len(img_files)),test_size)

for i in range(len(img_files)):

#img = (data_dir+img_files[i])

if i in test_indices:

test_set.append(data_dir+"/"+img_files[i])

else:

train_set.append(data_dir+"/"+img_files[i])

return"""

global train_set

global test_set

imgs = []

img_files = (data_dir)

for img in img_files:

try:

tmp= (data_dir+"/"+img)

x,y,z =

coords_x = x // img_size

coords_y = y // img_size

#coords_y = y / img_size

# coords_x = x / img_size

#print (coords_x)

coords = [ (q,r) for q in range(coords_x) for r in range(coords_y) ]

for coord in coords:

((data_dir+"/"+img,coord))

except:

print ("oops")

test_size = min(10,int( len(imgs)*0.2))

(imgs)

test_set = imgs[:test_size]

train_set = imgs[test_size:][:200]

return

def get_batch(batch_size,original_size,shrunk_size):

global batch_index

"""img_indices = (range(len(train_set)),batch_size)

for i in range(len(img_indices)):

index = img_indices[i]

img = (train_set[index])

if :

img = crop_center(img,original_size,original_size)

x_img = (img,(shrunk_size,shrunk_size))

(x_img)

(img)"""

max_counter = len(train_set)/batch_size

counter = batch_index % max_counter

#counter = tf.to_int32(batch_index % max_counter)

window = [x for x in range(int(counter*batch_size),int((counter+1)*batch_size))]

#window = [x for x in range(tf.to_int32(counter*batch_size),tf.to_int32((counter+1)*batch_size))]

#window = [x for x in ((counter*batch_size),((counter+1)*batch_size))]

#a1=(counter*batch_size,tf.int32)

#a2=((counter+1)*batch_size,tf.int32)

#window = [x for x in range(a1,a2)]

#window = [x for x in (a1,a2)]

#win2 = (window,tf.int32)

#win2 = tf.to_int32(window)

#win2 = tf.to_int64(window)

imgs = [train_set[q] for q in window]

x = [(get_image(q,original_size),(shrunk_size,shrunk_size)) for q in imgs]#(q[0])[q[1][0]*original_size:(q[1][0]+1)*original_size,q[1][1]*original_size:(q[1][1]+1)*original_size].resize(shrunk_size,shrunk_size) for q in imgs]

y = [get_image(q,original_size) for q in imgs]#(q[0])[q[1][0]*original_size:(q[1][0]+1)*original_size,q[1][1]*original_size:(q[1][1]+1)*original_size] for q in imgs]

batch_index = (batch_index+1)%max_counter

以上就是range在python中是什么意思的详细内容,更多请关注php中文网其它相关文章!