如何做图像分类的recall—precision曲线??

1个多月了还是没解决这个难题,
求大神指导。
初学者,没有变成基础,想做个图像分类的模型。
结构性数据很容易做pr曲线,
有y_test和y_pred可以得到recall和precision。
但是图像分类,
找了很久,没找到方法,或者各种错误。
这些图像是自己的数据,
分类不平衡,1:10,所以评价模型想用recall-precision。

train_generator = train_datagen.flow_from_directory(////本地路径
validation_generator = test_datagen.flow_from_directory(////本地路径
读取图像,
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy')
metrics这里函数虽然可以自制,但是想不到方法来定义y_pred和y_test,网上找了很多教程也是各种报错,

model.fit_generator(
train_generator,
steps_per_epoch=22 // batch_size,
epochs=50,
进行模型训练,

求各位大神指导,
怎么可以做出pr曲线

1个回答

多类分类最好用混淆矩阵,recall—precision曲线对两类好

weixin_44347319
C医生 是2分类,关键是没有图像分类的pr曲线的python 代码
11 个月之前 回复
lcyappleniuniu
red cedar apple 混淆矩阵特别明了
11 个月之前 回复
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Problem Description Rancher Joel has a tract of land in the shape of a convex quadrilateral that the wants to divide among his sons Al, Bob, Chas and Dave, who wish to continue ranching on their portions, and his daughter Emily, who wishes to grow vegetables on her portion. The center of the tract is most suitable for vegetable farming so Joel decides to divide the land by drawing lines from each corner (A, B, C, D in counter clockwise order) to the center of an opposing side (respectively A', B', C' and D') Each son would receive one of the triangular sections and Emily would receive the central quadrilateral section. As shown in the figure, Al's tract is to be bounded by the line from A to B, the line from A to the midpoint of BC and the line from B to the midpoint of CD' Bob&s ract is to be bounded by the line from B to C, the line from B to the midpoint of CD and the line from C to the midpoint of DA, and so on. Your job is to write a program that will help Rancher Joel determine the area of each child's tract and the length of the fence he will have to put around Emily's parcel to keep her brothers' cows out of her crops. For his problem, A will always be at (0, 0) and B will always be at (x, 0). Coordinates will be in rods (a rod is 16.5 feet).The returned areas should be in acres to 3 decimal places (an acre is 160 square rods) and the length of the fence should be in feet, rounded up to the next foot. Input The first line of input contains a single integer P( 1 <= P <= 1000),which is the number of data sets that follow. Each data set is a single line that contains of a decimal integer followed by five (5) space separated floating-point values. The first (integer) value is the data set number, N. The floating-point values are B.x, C.x, C.y, D.x and D.y in that order (where V.x indicates the x coordinate of V and V.y indicates the y coordinate of V). Recall that the y coordinate of B is always zero (0). The supplied coordinates will always specify a valid convex quadrilateral. Output For each data set there is a single line of output. It contains the data set number, N , followed by a single space followed by five(5) space separated floating-point values to three(3) decimal place accuracy, followed by a single space and a decimal integer! The floating-point values are the areas in acres of the properties of Al, Bob, Chas, Dave, and Emily respectively. The final integer is the length of fence in feet required to fence in Emily's property (rounded up to the next foot). Sample Input 3 1 200 250 150 -50 200 2 200 200 100 0 100 3 201.5 157.3 115.71 -44.2 115.71 Sample Output 1 35.000 54.136 75.469 54.167 54.666 6382 2 25.000 25.000 25.000 25.000 25.000 4589 3 29.144 29.144 29.144 29.144 29.144 4937
在训练Tensorflow模型(object_detection)时,训练在第一次评估后退出,怎么使训练继续下去?
当我进行ssd模型训练时,训练进行了10分钟,然后进入评估阶段,评估之后程序就自动退出了,没有看到误和警告,这是为什么,怎么让程序一直训练下去? 训练命令: ``` python object_detection/model_main.py --pipeline_config_path=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/pipeline.config --model_dir=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/saved_model --num_train_steps=50000 --alsologtostderr ``` 配置文件: ``` training exit after the first evaluation(only one evaluation) in Tensorflow model(object_detection) without error and waring System information What is the top-level directory of the model you are using:models/research/object_detection/ Have I written custom code (as opposed to using a stock example script provided in TensorFlow):NO OS Platform and Distribution (e.g., Linux Ubuntu 16.04):Windows-10(64bit) TensorFlow installed from (source or binary):conda install tensorflow-gpu TensorFlow version (use command below):1.13.1 Bazel version (if compiling from source):N/A CUDA/cuDNN version:cudnn-7.6.0 GPU model and memory:GeForce GTX 1060 6GB Exact command to reproduce:See below my command for training : python object_detection/model_main.py --pipeline_config_path=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/pipeline.config --model_dir=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/saved_model --num_train_steps=50000 --alsologtostderr This is my config : train_config { batch_size: 24 data_augmentation_options { random_horizontal_flip { } } data_augmentation_options { ssd_random_crop { } } optimizer { rms_prop_optimizer { learning_rate { exponential_decay_learning_rate { initial_learning_rate: 0.00400000018999 decay_steps: 800720 decay_factor: 0.949999988079 } } momentum_optimizer_value: 0.899999976158 decay: 0.899999976158 epsilon: 1.0 } } fine_tune_checkpoint: "D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/model.ckpt" from_detection_checkpoint: true num_steps: 200000 train_input_reader { label_map_path: "D:/gitcode/models/research/object_detection/idol/tf_label_map.pbtxt" tf_record_input_reader { input_path: "D:/gitcode/models/research/object_detection/idol/train/Iframe_??????.tfrecord" } } eval_config { num_examples: 8000 max_evals: 10 use_moving_averages: false } eval_input_reader { label_map_path: "D:/gitcode/models/research/object_detection/idol/tf_label_map.pbtxt" shuffle: false num_readers: 1 tf_record_input_reader { input_path: "D:/gitcode/models/research/object_detection/idol/eval/Iframe_??????.tfrecord" } ``` 窗口输出: (default) D:\gitcode\models\research>python object_detection/model_main.py --pipeline_config_path=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/pipeline.config --model_dir=D:/gitcode/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/saved_model --num_train_steps=50000 --alsologtostderr WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see: https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md https://github.com/tensorflow/addons If you depend on functionality not listed there, please file an issue. WARNING:tensorflow:Forced number of epochs for all eval validations to be 1. WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered eval_on_train_input_config.num_epochs = 0. Overwriting num_epochs to 1. WARNING:tensorflow:Estimator's model_fn (<function create_model_fn..model_fn at 0x0000027CBAB7BB70>) includes params argument, but params are not passed to Estimator. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\builders\dataset_builder.py:86: parallel_interleave (from tensorflow.contrib.data.python.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.data.experimental.parallel_interleave(...). WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\core\preprocessor.py:196: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: seed2 arg is deprecated.Use sample_distorted_bounding_box_v2 instead. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\builders\dataset_builder.py:158: batch_and_drop_remainder (from tensorflow.contrib.data.python.ops.batching) is deprecated and will be removed in a future version. Instructions for updating: Use tf.data.Dataset.batch(..., drop_remainder=True). WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\ops\losses\losses_impl.py:448: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\ops\array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. 2019-08-14 16:29:31.607841: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7845 pciBusID: 0000:04:00.0 totalMemory: 6.00GiB freeMemory: 4.97GiB 2019-08-14 16:29:31.621836: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0 2019-08-14 16:29:32.275712: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-08-14 16:29:32.283072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 2019-08-14 16:29:32.288675: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N 2019-08-14 16:29:32.293514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4714 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:04:00.0, compute capability: 6.1) WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\eval_util.py:796: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\object_detection-0.1-py3.7.egg\object_detection\utils\visualization_utils.py:498: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. Instructions for updating: tf.py_func is deprecated in TF V2. Instead, use tf.py_function, which takes a python function which manipulates tf eager tensors instead of numpy arrays. It's easy to convert a tf eager tensor to an ndarray (just call tensor.numpy()) but having access to eager tensors means tf.py_functions can use accelerators such as GPUs as well as being differentiable using a gradient tape. 2019-08-14 16:41:44.736212: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0 2019-08-14 16:41:44.741242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-08-14 16:41:44.747522: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 2019-08-14 16:41:44.751256: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N 2019-08-14 16:41:44.755548: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4714 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:04:00.0, compute capability: 6.1) WARNING:tensorflow:From C:\Users\qian\Anaconda3\envs\default\lib\site-packages\tensorflow\python\training\saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix. creating index... index created! creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=2.43s). Accumulating evaluation results... DONE (t=0.14s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.287 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.529 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.278 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.312 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.162 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.356 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.061 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.384 (default) D:\gitcode\models\research>
Typesetting 文字输出的实现方式
Problem Description Modern fonts are generally of two varieties: outline fonts, whose glyphs (the individual character shapes) are specified mathematically as a set of curves, and bitmap fonts, whose glyphs are specified as patterns of pixels. Fonts may also include embedded information such as kerning pairs (adjusting the spacing between certain pairs of glyphs, such as "AW", so that they appear spaced correctly), tracking hints (for managing inter-glyph spacing), antialiasing hints (smoothing of pixellated edges), and much more. To be sure, modern fonts are more than a simple collection of shapes, and displaying them properly is a common programming challenge. For this problem we will concern ourselves with bitmapped fonts and a simple form of typesetting called glyph packing. Essentially, the idea is to pack the glyphs as tightly as possible while maintaining at least one horizontal pixel of separation between glyphs. For example, consider the glyphs shown to the left below for the Roman characters "P" and "J". The figure to the right shows them after glyph packing. Note that they are as close as possible without touching horizontally. Here's another example. In this case, notice that the final glyph cannot be packed at all. After packing, pixels from distinct glyphs may be adjacent diagonally or vertically, but not horizontally. The following example shows that pixels may be adjacent diagonally. The "Love" test case in the example input section shows that they may be adjacent vertically. Glyph packing has the nice property that it's easy to build "fancy" glyphs into the font so that glyph packing creates special effects with no extra work. Look at the "Toy" example below. The same simple packing process has been applied to these glyphs as to the ones above, but the result is more dramatic: Glyph packing has a few caveats, however, one of which we must concern ourselves with for this problem. Consider the example on the left below where a glyph for a hyphen is followed by a glyph for an underscore. Based on our one horizontal pixel of separation rule, how would this pack? Clearly something more is needed, and that something more is hinting within the glyphs themselves. Recall that in actual practice, fonts contain kerning pairs, tracking hints, etc. For our purposes, our hinting will be limited to "invisible" pixels that count as a pixel for the purpose of packing, but not for display. The center image below represents invisible pixels as open dots instead of closed dots. Now the two glyphs can be properly packed, resulting in the output shown on the right. Now for the formal definition of a proper packing: (1) Glyphs are packed as close as possible without allowing any pixels from different glyphs to be immediately horizontally adjacent; (2) Given two glyphs, they may not be packed in such a way that any pixel of the leftmost glyph at a given height ends up positioned to the right of any pixel at the same height in the rightmost glyph. Condition (2) above is easily understood by visualizing two glyphs sitting side by side, separated by a small space. If you "squeeze" them together, condition (2) says that their pixels are not allowed to "pass through" one another. Consider the example to the left below. The center image is not the proper packing, because it violates condition (2) of the formal definition. The image on the right is the proper packing of these glyphs. Input The input for this problem is sets of glyphs to be packed. In a given test case, all glyphs are the same height, and an integer, N, on the first line of the test case specifies this height. The next N lines contain the glyphs to be packed. Empty pixels in a glyph are represented by a dot '.' character. Non-empty pixels are represented by a hash mark '#' for visible pixels, and a zero '0' for invisible pixels. Glyphs are separated by a single column of space characters. The input will always consist of more than one glyph, at least one of which will always contain at least one visible pixel. A glyph will always have at least one non-empty pixel in its leftmost and rightmost column, and every glyph will have at least one non-empty pixel at the same height as at least one other glyph in the input. The minimum dimension of a glyph is 1 × 1, the maximum dimension is 20 × 20, and the maximum number of glyphs that will appear in any test case is 20. Test cases continue until a value of zero is specified for N. Output For each test case, first output the number of that test case (starting with 1) on a line by itself. Then output the proper packing of the input glyphs, using the dot '.' character for empty pixels and for invisible pixels, and the hash mark '#' character for visible pixels. Omit leading and trailing empty columns (columns with no visible pixels) so that both the leftmost and rightmost output columns contain at least one visible pixel. Sample Input 8 ###. ...# #..# ...# #..# ...# ###. ...# #... ...# #... ...# #... #..# #... #### 8 ############# .... ............. ..#.......... .... ............. ..#.......... .##. .........#..# ..#.......... #..# .........#..# ..#.......... #..# .........#..# ..#.......... .##. ..........### ............. .... ............# ............. .... ############. 8 ############# ............. ..#.......... ............. ..#.......... .........#..# ..#.......... .........#..# ..#.......... .........#..# ..#.......... ..........### ............. ............# ............. ############. 5 0..0 0..0 0..0 0..0 #### 0..0 0..0 0..0 0..0 #### 5 #.... .###. #.... #...# #...# #...# #...# ....# .###. ....# 3 ### 0.0 ### #.# 0.0 #.# ### 0.0 ### 3 0.0 ### 0.0 0.0 #.# 0.0 0.0 ### 0.0 8 #.... .... ..... .... #.... .... ..... .... #.... .##. #...# .##. #.... #..# .#.#. #..# #.... #..# .#.#. #..# #.... #..# .#.#. ###. #.... .##. ..#.. #... ##### .... ..#.. .### 0 Sample Output 1 ###..# #..#.# #..#.# ###..# #....# #....# #.#..# #.#### 2 ############# ..#.......... ..#..##..#..# ..#.#..#.#..# ..#.#..#.#..# ..#..##...### ............# ############. 3 .....############# .......#.......... .......#.#..#..... .......#.#..#..... .......#.#..#..... .......#..###..... ............#..... ############...... 4 ......... ......... ####..... ......... .....#### 5 #......###. #.....#...# #...#.#...# #...#.....# .###......# 6 ###.....### #.#.....#.# ###.....### 7 ### #.# ### 8 #.............. #.............. #..##.#...#.##. #.#..#.#.#.#..# #.#..#.#.#.#..# #.#..#.#.#.###. #..##...#..#... #####...#...###
Typesetting 是怎么来实现的
Problem Description Modern fonts are generally of two varieties: outline fonts, whose glyphs (the individual character shapes) are specified mathematically as a set of curves, and bitmap fonts, whose glyphs are specified as patterns of pixels. Fonts may also include embedded information such as kerning pairs (adjusting the spacing between certain pairs of glyphs, such as "AW", so that they appear spaced correctly), tracking hints (for managing inter-glyph spacing), antialiasing hints (smoothing of pixellated edges), and much more. To be sure, modern fonts are more than a simple collection of shapes, and displaying them properly is a common programming challenge. For this problem we will concern ourselves with bitmapped fonts and a simple form of typesetting called glyph packing. Essentially, the idea is to pack the glyphs as tightly as possible while maintaining at least one horizontal pixel of separation between glyphs. For example, consider the glyphs shown to the left below for the Roman characters "P" and "J". The figure to the right shows them after glyph packing. Note that they are as close as possible without touching horizontally. Here's another example. In this case, notice that the final glyph cannot be packed at all. After packing, pixels from distinct glyphs may be adjacent diagonally or vertically, but not horizontally. The following example shows that pixels may be adjacent diagonally. The "Love" test case in the example input section shows that they may be adjacent vertically. Glyph packing has the nice property that it's easy to build "fancy" glyphs into the font so that glyph packing creates special effects with no extra work. Look at the "Toy" example below. The same simple packing process has been applied to these glyphs as to the ones above, but the result is more dramatic: Glyph packing has a few caveats, however, one of which we must concern ourselves with for this problem. Consider the example on the left below where a glyph for a hyphen is followed by a glyph for an underscore. Based on our one horizontal pixel of separation rule, how would this pack? Clearly something more is needed, and that something more is hinting within the glyphs themselves. Recall that in actual practice, fonts contain kerning pairs, tracking hints, etc. For our purposes, our hinting will be limited to "invisible" pixels that count as a pixel for the purpose of packing, but not for display. The center image below represents invisible pixels as open dots instead of closed dots. Now the two glyphs can be properly packed, resulting in the output shown on the right. Now for the formal definition of a proper packing: (1) Glyphs are packed as close as possible without allowing any pixels from different glyphs to be immediately horizontally adjacent; (2) Given two glyphs, they may not be packed in such a way that any pixel of the leftmost glyph at a given height ends up positioned to the right of any pixel at the same height in the rightmost glyph. Condition (2) above is easily understood by visualizing two glyphs sitting side by side, separated by a small space. If you "squeeze" them together, condition (2) says that their pixels are not allowed to "pass through" one another. Consider the example to the left below. The center image is not the proper packing, because it violates condition (2) of the formal definition. The image on the right is the proper packing of these glyphs. Input The input for this problem is sets of glyphs to be packed. In a given test case, all glyphs are the same height, and an integer, N, on the first line of the test case specifies this height. The next N lines contain the glyphs to be packed. Empty pixels in a glyph are represented by a dot '.' character. Non-empty pixels are represented by a hash mark '#' for visible pixels, and a zero '0' for invisible pixels. Glyphs are separated by a single column of space characters. The input will always consist of more than one glyph, at least one of which will always contain at least one visible pixel. A glyph will always have at least one non-empty pixel in its leftmost and rightmost column, and every glyph will have at least one non-empty pixel at the same height as at least one other glyph in the input. The minimum dimension of a glyph is 1 × 1, the maximum dimension is 20 × 20, and the maximum number of glyphs that will appear in any test case is 20. Test cases continue until a value of zero is specified for N. Output For each test case, first output the number of that test case (starting with 1) on a line by itself. Then output the proper packing of the input glyphs, using the dot '.' character for empty pixels and for invisible pixels, and the hash mark '#' character for visible pixels. Omit leading and trailing empty columns (columns with no visible pixels) so that both the leftmost and rightmost output columns contain at least one visible pixel. Sample Input 8 ###. ...# #..# ...# #..# ...# ###. ...# #... ...# #... ...# #... #..# #... #### 8 ############# .... ............. ..#.......... .... ............. ..#.......... .##. .........#..# ..#.......... #..# .........#..# ..#.......... #..# .........#..# ..#.......... .##. ..........### ............. .... ............# ............. .... ############. 8 ############# ............. ..#.......... ............. ..#.......... .........#..# ..#.......... .........#..# ..#.......... .........#..# ..#.......... ..........### ............. ............# ............. ############. 5 0..0 0..0 0..0 0..0 #### 0..0 0..0 0..0 0..0 #### 5 #.... .###. #.... #...# #...# #...# #...# ....# .###. ....# 3 ### 0.0 ### #.# 0.0 #.# ### 0.0 ### 3 0.0 ### 0.0 0.0 #.# 0.0 0.0 ### 0.0 8 #.... .... ..... .... #.... .... ..... .... #.... .##. #...# .##. #.... #..# .#.#. #..# #.... #..# .#.#. #..# #.... #..# .#.#. ###. #.... .##. ..#.. #... ##### .... ..#.. .### 0 Sample Output 1 ###..# #..#.# #..#.# ###..# #....# #....# #.#..# #.#### 2 ############# ..#.......... ..#..##..#..# ..#.#..#.#..# ..#.#..#.#..# ..#..##...### ............# ############. 3 .....############# .......#.......... .......#.#..#..... .......#.#..#..... .......#.#..#..... .......#..###..... ............#..... ############...... 4 ......... ......... ####..... ......... .....#### 5 #......###. #.....#...# #...#.#...# #...#.....# .###......# 6 ###.....### #.#.....#.# ###.....### 7 ### #.# ### 8 #.............. #.............. #..##.#...#.##. #.#..#.#.#.#..# #.#..#.#.#.#..# #.#..#.#.#.###. #..##...#..#... #####...#...###
How Many Fibs? 的问题
Problem Description Recall the definition of the Fibonacci numbers: f1 := 1 f2 := 2 fn := fn-1 + fn-2 (n >= 3) Given two numbers a and b, calculate how many Fibonacci numbers are in the range [a, b]. Input The input contains several test cases. Each test case consists of two non-negative integer numbers a and b. Input is terminated by a = b = 0. Otherwise, a <= b <= 10^100. The numbers a and b are given with no superfluous leading zeros. Output For each test case output on a single line the number of Fibonacci numbers fi with a <= fi <= b. Sample Input 10 100 1234567890 9876543210 0 0 Sample Output 5 4
bad input shape (60000, 2)
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