What does range mean in python?

The python range() function creates a list of integers and is generally used in for loops.

Function syntax

range(start, stop[, step])

Parameter description:

start: Counting starts from start. The default is to start from 0. For example, range(5) is equivalent to range(0, 5);

stop: counts to the end of stop, but does not include stop. For example: range (0, 5) is [0, 1, 2, 3, 4] without 5

step: step size, default is 1. For example: range(0, 5) is equivalent to range(0, 5, 1)

Example

>>>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 = os.listdir(data_dir) test_size = int(len(img_files)*0.2) test_indices = random.sample(range(len(img_files)),test_size) for i in range(len(img_files)): #img = scipy.misc.imread(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 = os.listdir(data_dir) for img in img_files: try: tmp= scipy.misc.imread(data_dir+"/"+img) x,y,z = tmp.shape 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: imgs.append((data_dir+"/"+img,coord)) except: print ("oops") test_size = min(10,int( len(imgs)*0.2)) random.shuffle(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 = random.sample(range(len(train_set)),batch_size) for i in range(len(img_indices)): index = img_indices[i] img = scipy.misc.imread(train_set[index]) if img.shape: img = crop_center(img,original_size,original_size) x_img = scipy.misc.imresize(img,(shrunk_size,shrunk_size)) x.append(x_img) y.append(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 np.arange((counter*batch_size),((counter+1)*batch_size))] #a1=tf.cast(counter*batch_size,tf.int32) #a2=tf.cast((counter+1)*batch_size,tf.int32) #window = [x for x in range(a1,a2)] #window = [x for x in np.arange(a1,a2)] #win2 = tf.cast(window,tf.int32) #win2 = tf.to_int32(window) #win2 = tf.to_int64(window) imgs = [train_set[q] for q in window] x = [scipy.misc.imresize(get_image(q,original_size),(shrunk_size,shrunk_size)) for q in imgs]#scipy.misc.imread(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]#scipy.misc.imread(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

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