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lesson1-breeds_4:/^guvskstikb^a02bad3d-6838-491e-b364-cc1c4aa2368
作者：我家小花儿 | 2024-03-30 21:56:35
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4:/^guvskstikb^a02bad3d-6838-491e-b364-cc1c4aa23683
Dogs breeds
本实例我是在jupter上运行的
使用的是fastai
%reload_ext autoreload
%autoreload 2
%matplotlib inline
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from fastai.imports import *
from fastai.torch_imports import *
from fastai.transforms import *
from fastai.conv_learner import *
from fastai.model import *
from fastai.dataset import *
from fastai.sgdr import *
from fastai.plots import *
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#torch.cuda.set_device(0)
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#这是在jupter的根目录下
PATH = "databreed/"
sz = 224
arch = resnext101_64
bs = 58
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label_csv = f'{PATH}labels1.csv'
n = len(list(open(label_csv))) - 1 # header is not counted (-1)
val_idxs = get_cv_idxs(n) # random 20% data for validation set
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n
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10
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len(val_idxs)
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2
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# If you haven't downloaded weights.tgz yet, download the file.
# http://forums.fast.ai/t/error-when-trying-to-use-resnext50/7555
# http://forums.fast.ai/t/lesson-2-in-class-discussion/7452/222
#!wget -O fastai/weights.tgz http://files.fast.ai/models/weights.tgz
#!tar xvfz fastai/weights.tgz -C fastai
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Initial exploration
!ls {PATH}
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labels1.csv test
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label_df = pd.read_csv(label_csv)
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label_df.head()
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| id | breed | |
|---|---|---|
| 0 | 000bec180eb18c7604dcecc8fe0dba07 | boston_bull |
| 1 | 001513dfcb2ffafc82cccf4d8bbaba97 | dingo |
| 2 | 001cdf01b096e06d78e9e5112d419397 | pekinese |
| 3 | 00214f311d5d2247d5dfe4fe24b2303d | bluetick |
| 4 | 0021f9ceb3235effd7fcde7f7538ed62 | golden_retriever |
label_df.pivot_table(index="breed", aggfunc=len).sort_values('id', ascending=False)
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| id | |
|---|---|
| breed | |
| basenji | 1 |
| bedlington_terrier | 1 |
| bluetick | 1 |
| borzoi | 1 |
| boston_bull | 1 |
| dingo | 1 |
| golden_retriever | 1 |
| pekinese | 1 |
| scottish_deerhound | 1 |
| shetland_sheepdog | 1 |
tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1)
data = ImageClassifierData.from_csv(PATH, 'train', f'{PATH}labels1.csv', test_name='test', # we need to specify where the test set is if you want to submit to Kaggle competitions
val_idxs=val_idxs, suffix='.jpg', tfms=tfms, bs=bs)
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fn = PATH + data.trn_ds.fnames[0]; fn
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'databreed/train/000bec180eb18c7604dcecc8fe0dba07.jpg'
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img = PIL.Image.open(fn); img
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img.size
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(500, 375)
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size_d = {k: PIL.Image.open(PATH + k).size for k in data.trn_ds.fnames}
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row_sz, col_sz = list(zip(*size_d.values()))
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row_sz = np.array(row_sz); col_sz = np.array(col_sz)
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row_sz[:5]
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array([500, 500, 400, 500, 500])
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plt.hist(row_sz);
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plt.hist(row_sz[row_sz < 1000])
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(array([1., 0., 0., 0., 0., 1., 1., 0., 0., 5.]),
array([231. , 257.9, 284.8, 311.7, 338.6, 365.5, 392.4, 419.3, 446.2, 473.1, 500. ]),
<a list of 10 Patch objects>)
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plt.hist(col_sz);
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plt.hist(col_sz[col_sz < 1000])
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(array([1., 0., 0., 0., 1., 3., 0., 0., 0., 3.]),
array([227. , 254.3, 281.6, 308.9, 336.2, 363.5, 390.8, 418.1, 445.4, 472.7, 500. ]),
<a list of 10 Patch objects>)
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len(data.trn_ds), len(data.test_ds)
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(8, 4)
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len(data.classes), data.classes[:5]
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(10, ['basenji', 'bedlington_terrier', 'bluetick', 'borzoi', 'boston_bull'])
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Initial model
def get_data(sz, bs): # sz: image size, bs: batch size tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1) data = ImageClassifierData.from_csv(PATH, 'train', f'{PATH}labels1.csv', test_name='test', val_idxs=val_idxs, suffix='.jpg', tfms=tfms, bs=bs) # http://forums.fast.ai/t/how-to-train-on-the-full-dataset-using-imageclassifierdata-from-csv/7761/13 # http://forums.fast.ai/t/how-to-train-on-the-full-dataset-using-imageclassifierdata-from-csv/7761/37 return data if sz > 300 else data.resize(340, 'tmp') # Reading the jpgs and resizing is slow for big images, so resizing them all to 340 first saves time #Source: # def resize(self, targ, new_path): # new_ds = [] # dls = [self.trn_dl,self.val_dl,self.fix_dl,self.aug_dl] # if self.test_dl: dls += [self.test_dl, self.test_aug_dl] # else: dls += [None,None] # t = tqdm_notebook(dls) # for dl in t: new_ds.append(self.resized(dl, targ, new_path)) # t.close() # return self.__class__(new_ds[0].path, new_ds, self.bs, self.num_workers, self.classes) #File: ~/fastai/courses/dl1/fastai/dataset.py
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Precompute
data = get_data(sz, bs)
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learn = ConvLearner.pretrained(arch, data, precompute=True)
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learn.fit(1e-2, 5)
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Augment
from sklearn import metrics
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data = get_data(sz, bs)
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learn = ConvLearner.pretrained(arch, data, precompute=True, ps=0.5)
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learn.fit(1e-2, 2)
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learn.precompute = False
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learn.fit(1e-2, 5, cycle_len=1)
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learn.save('224_pre')
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learn.load('224_pre')
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Increase size
# Starting training on small images for a few epochs, then switching to bigger images, and continuing training is an amazingly effective way to avoid overfitting. # http://forums.fast.ai/t/planet-classification-challenge/7824/96 # set_data doesn’t change the model at all. It just gives it new data to train with. learn.set_data(get_data(299, bs)) learn.freeze() #Source: # def set_data(self, data, precompute=False): # super().set_data(data) # if precompute: # self.unfreeze() # self.save_fc1() # self.freeze() # self.precompute = True # else: # self.freeze() #File: ~/fastai/courses/dl1/fastai/conv_learner.py
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HBox(children=(IntProgress(value=0, max=6), HTML(value='')))
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learn.summary()
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OrderedDict([('Conv2d-1', OrderedDict([('input_shape', [-1, 3, 224, 224]), ('output_shape', [-1, 64, 112, 112]), ('trainable', False), ('nb_params', 9408)])), ('BatchNorm2d-2', OrderedDict([('input_shape', [-1, 64, 112, 112]), ('output_shape', [-1, 64, 112, 112]), ('trainable', False), ('nb_params', 128)])), ('ReLU-3', OrderedDict([('input_shape', [-1, 64, 112, 112]), ('output_shape', [-1, 64, 112, 112]), ('nb_params', 0)])), ('MaxPool2d-4', OrderedDict([('input_shape', [-1, 64, 112, 112]), ('output_shape', [-1, 64, 56, 56]), ('nb_params', 0)])), ('Conv2d-5', OrderedDict([('input_shape', [-1, 64, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 16384)])), ('BatchNorm2d-6', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 512)])), ('ReLU-7', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('nb_params', 0)])), ('Conv2d-8', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 9216)])), ('BatchNorm2d-9', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 512)])), ('ReLU-10', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('nb_params', 0)])), ('Conv2d-11', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 65536)])), ('BatchNorm2d-12', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 512)])), ('Conv2d-13', OrderedDict([('input_shape', [-1, 64, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 16384)])), ('BatchNorm2d-14', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 512)])), ('ReLU-15', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('nb_params', 0)])), ('Conv2d-16', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 65536)])), ('BatchNorm2d-17', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 512)])), ('ReLU-18', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('nb_params', 0)])), ('Conv2d-19', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 9216)])), ('BatchNorm2d-20', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 512)])), ('ReLU-21', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('nb_params', 0)])), ('Conv2d-22', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 65536)])), ('BatchNorm2d-23', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 512)])), ('ReLU-24', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('nb_params', 0)])), ('Conv2d-25', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 65536)])), ('BatchNorm2d-26', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 512)])), ('ReLU-27', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('nb_params', 0)])), ('Conv2d-28', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 9216)])), ('BatchNorm2d-29', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 512)])), ('ReLU-30', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('nb_params', 0)])), ('Conv2d-31', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 65536)])), ('BatchNorm2d-32', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('trainable', False), ('nb_params', 512)])), ('ReLU-33', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 256, 56, 56]), ('nb_params', 0)])), ('Conv2d-34', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 512, 56, 56]), ('trainable', False), ('nb_params', 131072)])), ('BatchNorm2d-35', OrderedDict([('input_shape', [-1, 512, 56, 56]), ('output_shape', [-1, 512, 56, 56]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-36', OrderedDict([('input_shape', [-1, 512, 56, 56]), ('output_shape', [-1, 512, 56, 56]), ('nb_params', 0)])), ('Conv2d-37', OrderedDict([('input_shape', [-1, 512, 56, 56]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 36864)])), ('BatchNorm2d-38', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-39', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('nb_params', 0)])), ('Conv2d-40', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 262144)])), ('BatchNorm2d-41', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('Conv2d-42', OrderedDict([('input_shape', [-1, 256, 56, 56]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 131072)])), ('BatchNorm2d-43', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-44', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('nb_params', 0)])), ('Conv2d-45', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 262144)])), ('BatchNorm2d-46', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-47', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('nb_params', 0)])), ('Conv2d-48', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 36864)])), ('BatchNorm2d-49', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-50', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('nb_params', 0)])), ('Conv2d-51', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 262144)])), ('BatchNorm2d-52', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-53', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('nb_params', 0)])), ('Conv2d-54', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 262144)])), ('BatchNorm2d-55', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-56', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('nb_params', 0)])), ('Conv2d-57', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 36864)])), ('BatchNorm2d-58', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-59', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('nb_params', 0)])), ('Conv2d-60', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 262144)])), ('BatchNorm2d-61', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-62', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('nb_params', 0)])), ('Conv2d-63', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 262144)])), ('BatchNorm2d-64', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-65', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('nb_params', 0)])), ('Conv2d-66', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 36864)])), ('BatchNorm2d-67', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-68', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('nb_params', 0)])), ('Conv2d-69', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 262144)])), ('BatchNorm2d-70', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('trainable', False), ('nb_params', 1024)])), ('ReLU-71', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 512, 28, 28]), ('nb_params', 0)])), ('Conv2d-72', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 1024, 28, 28]), ('trainable', False), ('nb_params', 524288)])), ('BatchNorm2d-73', OrderedDict([('input_shape', [-1, 1024, 28, 28]), ('output_shape', [-1, 1024, 28, 28]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-74', OrderedDict([('input_shape', [-1, 1024, 28, 28]), ('output_shape', [-1, 1024, 28, 28]), ('nb_params', 0)])), ('Conv2d-75', OrderedDict([('input_shape', [-1, 1024, 28, 28]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 147456)])), ('BatchNorm2d-76', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-77', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-78', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-79', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('Conv2d-80', OrderedDict([('input_shape', [-1, 512, 28, 28]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 524288)])), ('BatchNorm2d-81', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-82', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-83', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-84', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-85', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-86', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 147456)])), ('BatchNorm2d-87', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-88', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-89', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-90', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-91', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-92', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-93', OrderedDict([('input_shape', [-1, 1024, 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('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 147456)])), ('BatchNorm2d-213', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-214', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-215', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-216', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-217', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-218', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-219', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-220', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-221', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 147456)])), ('BatchNorm2d-222', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-223', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-224', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-225', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-226', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-227', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-228', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-229', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-230', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 147456)])), ('BatchNorm2d-231', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-232', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-233', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-234', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-235', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-236', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-237', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-238', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-239', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 147456)])), ('BatchNorm2d-240', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-241', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-242', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-243', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-244', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-245', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-246', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-247', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-248', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 147456)])), ('BatchNorm2d-249', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-250', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-251', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-252', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-253', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-254', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-255', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-256', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-257', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 147456)])), ('BatchNorm2d-258', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-259', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-260', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-261', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-262', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-263', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-264', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-265', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-266', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 147456)])), ('BatchNorm2d-267', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-268', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-269', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-270', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-271', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-272', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-273', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-274', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-275', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 147456)])), ('BatchNorm2d-276', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-277', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-278', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 1048576)])), ('BatchNorm2d-279', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('trainable', False), ('nb_params', 2048)])), ('ReLU-280', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 1024, 14, 14]), ('nb_params', 0)])), ('Conv2d-281', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 2048, 14, 14]), ('trainable', False), ('nb_params', 2097152)])), ('BatchNorm2d-282', OrderedDict([('input_shape', [-1, 2048, 14, 14]), ('output_shape', [-1, 2048, 14, 14]), ('trainable', False), ('nb_params', 4096)])), ('ReLU-283', OrderedDict([('input_shape', [-1, 2048, 14, 14]), ('output_shape', [-1, 2048, 14, 14]), ('nb_params', 0)])), ('Conv2d-284', OrderedDict([('input_shape', [-1, 2048, 14, 14]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 589824)])), ('BatchNorm2d-285', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4096)])), ('ReLU-286', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('nb_params', 0)])), ('Conv2d-287', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4194304)])), ('BatchNorm2d-288', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4096)])), ('Conv2d-289', OrderedDict([('input_shape', [-1, 1024, 14, 14]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 2097152)])), ('BatchNorm2d-290', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4096)])), ('ReLU-291', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('nb_params', 0)])), ('Conv2d-292', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4194304)])), ('BatchNorm2d-293', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4096)])), ('ReLU-294', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('nb_params', 0)])), ('Conv2d-295', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 589824)])), ('BatchNorm2d-296', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4096)])), ('ReLU-297', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('nb_params', 0)])), ('Conv2d-298', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4194304)])), ('BatchNorm2d-299', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4096)])), ('ReLU-300', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('nb_params', 0)])), ('Conv2d-301', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4194304)])), ('BatchNorm2d-302', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4096)])), ('ReLU-303', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('nb_params', 0)])), ('Conv2d-304', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 589824)])), ('BatchNorm2d-305', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4096)])), ('ReLU-306', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('nb_params', 0)])), ('Conv2d-307', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4194304)])), ('BatchNorm2d-308', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('trainable', False), ('nb_params', 4096)])), ('ReLU-309', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 7, 7]), ('nb_params', 0)])), ('AdaptiveMaxPool2d-310', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 1, 1]), ('nb_params', 0)])), ('AdaptiveAvgPool2d-311', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 2048, 1, 1]), ('nb_params', 0)])), ('AdaptiveConcatPool2d-312', OrderedDict([('input_shape', [-1, 2048, 7, 7]), ('output_shape', [-1, 4096, 1, 1]), ('nb_params', 0)])), ('Flatten-313', OrderedDict([('input_shape', [-1, 4096, 1, 1]), ('output_shape', [-1, 4096]), ('nb_params', 0)])), ('BatchNorm1d-314', OrderedDict([('input_shape', [-1, 4096]), ('output_shape', [-1, 4096]), ('trainable', True), ('nb_params', 8192)])), ('Dropout-315', OrderedDict([('input_shape', [-1, 4096]), ('output_shape', [-1, 4096]), ('nb_params', 0)])), ('Linear-316', OrderedDict([('input_shape', [-1, 4096]), ('output_shape', [-1, 512]), ('trainable', True), ('nb_params', 2097664)])), ('ReLU-317', OrderedDict([('input_shape', [-1, 512]), ('output_shape', [-1, 512]), ('nb_params', 0)])), ('BatchNorm1d-318', OrderedDict([('input_shape', [-1, 512]), ('output_shape', [-1, 512]), ('trainable', True), ('nb_params', 1024)])), ('Dropout-319', OrderedDict([('input_shape', [-1, 512]), ('output_shape', [-1, 512]), ('nb_params', 0)])), ('Linear-320', OrderedDict([('input_shape', [-1, 512]), ('output_shape', [-1, 120]), ('trainable', True), ('nb_params', 61560)])), ('LogSoftmax-321', OrderedDict([('input_shape', [-1, 120]), ('output_shape', [-1, 120]), ('nb_params', 0)]))])
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learn.fit(1e-2, 3, cycle_len=1)
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HBox(children=(IntProgress(value=0, description='Epoch', max=3), HTML(value='')))
epoch trn_loss val_loss accuracy
0 0.303971 0.242417 0.921722
1 0.309993 0.239827 0.91683
2 0.288534 0.23499 0.919276
[array([0.23499]), 0.9192759310662629]
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Validation loss is much lower than training loss. This is a sign of underfitting. Cycle_len=1 may be too short. Let’s set cycle_mult=2 to find better parameter.
# When you are under fitting, it means cycle_len=1 is too short (learning rate is getting reset before it had the chance to zoom in properly).
learn.fit(1e-2, 3, cycle_len=1, cycle_mult=2) # 1+2+4 = 7 epochs
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HBox(children=(IntProgress(value=0, description='Epoch', max=7), HTML(value=''))) epoch trn_loss val_loss accuracy 0 0.267461 0.235228 0.924168 1 0.270705 0.230974 0.922211 2 0.240056 0.230974 0.923679 3 0.238908 0.232905 0.926125 4 0.223686 0.229831 0.923679 5 0.212009 0.227405 0.924168 6 0.199683 0.227282 0.926125 [array([0.22728]), 0.9261252481176895]
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Training loss and validation loss are getting closer and smaller. We are on right track.
log_preds, y = learn.TTA() # (5, 2044, 120), (2044,)
probs = np.mean(np.exp(log_preds),0)
accuracy_np(probs, y), metrics.log_loss(y, probs)
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(0.9315068493150684, 0.22650256548463946)
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len(data.val_ds.y), data.val_ds.y[:5]
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(2044, array([19, 15, 7, 99, 73]))
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learn.save('299_pre')
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learn.load('299_pre')
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learn.fit(1e-2, 1, cycle_len=2) # 1+1 = 2 epochs
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HBox(children=(IntProgress(value=0, description='Epoch', max=2), HTML(value='')))
epoch trn_loss val_loss accuracy
0 0.215887 0.227493 0.926614
1 0.21398 0.224618 0.926614
[array([0.22462]), 0.9266144826337549]
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learn.save('299_pre')
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log_preds, y = learn.TTA()
probs = np.mean(np.exp(log_preds),0)
accuracy_np(probs, y), metrics.log_loss(y, probs)
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(0.9334637964774951, 0.22243022015961378)
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This dataset is so similar to ImageNet dataset. Training convolution layers doesn’t help much. We are not going to unfreeze.
Create submission
https://youtu.be/9C06ZPF8Uuc?t=1905
data.classes
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['affenpinscher', 'afghan_hound', 'african_hunting_dog', 'airedale', 'american_staffordshire_terrier', 'appenzeller', 'australian_terrier', 'basenji', 'basset', 'beagle', 'bedlington_terrier', 'bernese_mountain_dog', 'black-and-tan_coonhound', 'blenheim_spaniel', 'bloodhound', 'bluetick', 'border_collie', 'border_terrier', 'borzoi', 'boston_bull', 'bouvier_des_flandres', 'boxer', 'brabancon_griffon', 'briard', 'brittany_spaniel', 'bull_mastiff', 'cairn', 'cardigan', 'chesapeake_bay_retriever', 'chihuahua', 'chow', 'clumber', 'cocker_spaniel', 'collie', 'curly-coated_retriever', 'dandie_dinmont', 'dhole', 'dingo', 'doberman', 'english_foxhound', 'english_setter', 'english_springer', 'entlebucher', 'eskimo_dog', 'flat-coated_retriever', 'french_bulldog', 'german_shepherd', 'german_short-haired_pointer', 'giant_schnauzer', 'golden_retriever', 'gordon_setter', 'great_dane', 'great_pyrenees', 'greater_swiss_mountain_dog', 'groenendael', 'ibizan_hound', 'irish_setter', 'irish_terrier', 'irish_water_spaniel', 'irish_wolfhound', 'italian_greyhound', 'japanese_spaniel', 'keeshond', 'kelpie', 'kerry_blue_terrier', 'komondor', 'kuvasz', 'labrador_retriever', 'lakeland_terrier', 'leonberg', 'lhasa', 'malamute', 'malinois', 'maltese_dog', 'mexican_hairless', 'miniature_pinscher', 'miniature_poodle', 'miniature_schnauzer', 'newfoundland', 'norfolk_terrier', 'norwegian_elkhound', 'norwich_terrier', 'old_english_sheepdog', 'otterhound', 'papillon', 'pekinese', 'pembroke', 'pomeranian', 'pug', 'redbone', 'rhodesian_ridgeback', 'rottweiler', 'saint_bernard', 'saluki', 'samoyed', 'schipperke', 'scotch_terrier', 'scottish_deerhound', 'sealyham_terrier', 'shetland_sheepdog', 'shih-tzu', 'siberian_husky', 'silky_terrier', 'soft-coated_wheaten_terrier', 'staffordshire_bullterrier', 'standard_poodle', 'standard_schnauzer', 'sussex_spaniel', 'tibetan_mastiff', 'tibetan_terrier', 'toy_poodle', 'toy_terrier', 'vizsla', 'walker_hound', 'weimaraner', 'welsh_springer_spaniel', 'west_highland_white_terrier', 'whippet', 'wire-haired_fox_terrier', 'yorkshire_terrier']
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data.test_ds.fnames
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log_preds, y = learn.TTA(is_test=True) # use test dataset rather than validation dataset
probs = np.mean(np.exp(log_preds),0)
#accuracy_np(probs, y), metrcs.log_loss(y, probs) # This does not make sense since test dataset has no labels
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probs.shape # (n_images, n_classes)
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(10357, 120)
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df = pd.DataFrame(probs)
df.columns = data.classes
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df.insert(0, 'id', [o[5:-4] for o in data.test_ds.fnames])
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df.head()
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| id | affenpinscher | afghan_hound | african_hunting_dog | airedale | american_staffordshire_terrier | appenzeller | australian_terrier | basenji | basset | ... | toy_poodle | toy_terrier | vizsla | walker_hound | weimaraner | welsh_springer_spaniel | west_highland_white_terrier | whippet | wire-haired_fox_terrier | yorkshire_terrier | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ab2520c527e61f197be228208af48191 | 7.957505e-08 | 2.723862e-08 | 2.435847e-08 | 1.173262e-07 | 2.351215e-08 | 8.401931e-06 | 1.372760e-06 | 6.317406e-08 | 3.063393e-08 | ... | 2.080939e-08 | 2.456473e-07 | 2.722122e-07 | 5.030101e-08 | 1.900935e-07 | 6.053991e-07 | 3.839476e-08 | 5.778787e-08 | 1.575098e-07 | 1.075539e-08 |
| 1 | 8ffc8a83bb9ac7884a9420c97b23940c | 9.668808e-08 | 2.355516e-08 | 2.087995e-07 | 6.298836e-08 | 3.269388e-08 | 2.796247e-07 | 2.439702e-08 | 2.535878e-06 | 1.824919e-06 | ... | 4.051576e-08 | 3.540100e-06 | 2.388073e-07 | 9.832689e-01 | 1.823956e-07 | 2.486797e-08 | 8.325348e-08 | 9.363868e-07 | 2.608415e-07 | 3.851193e-07 |
| 2 | 9f4bbcd8a5b189514d3098516983621a | 4.214103e-05 | 2.804878e-04 | 4.817631e-05 | 7.178330e-03 | 1.471457e-06 | 1.140446e-05 | 1.950280e-04 | 7.519415e-06 | 1.821058e-06 | ... | 5.793181e-05 | 9.164357e-05 | 1.187949e-04 | 6.772134e-06 | 5.031822e-05 | 4.772470e-05 | 6.114125e-06 | 2.762433e-05 | 5.382648e-04 | 2.682866e-05 |
| 3 | f77793be1597dd1ea50b22532b38bd23 | 2.568105e-07 | 2.491144e-07 | 7.142457e-07 | 1.466020e-06 | 3.212435e-05 | 8.274229e-08 | 3.600422e-08 | 6.044879e-08 | 1.201969e-07 | ... | 2.627351e-06 | 3.965855e-08 | 1.560448e-06 | 6.965169e-08 | 1.856623e-07 | 1.051336e-07 | 1.763770e-07 | 2.664481e-07 | 3.316928e-08 | 9.700193e-08 |
| 4 | f719b425410b6eb3e3132702150affd6 | 6.095974e-06 | 2.696717e-06 | 4.131879e-06 | 6.457446e-05 | 1.191631e-03 | 3.560664e-05 | 3.274512e-06 | 2.229157e-06 | 1.317608e-06 | ... | 2.345266e-06 | 1.053057e-05 | 2.322353e-05 | 4.169483e-05 | 1.918868e-05 | 5.647749e-06 | 5.437289e-06 | 5.297930e-06 | 3.867970e-06 | 5.011518e-06 |
5 rows × 121 columns
SUBM = f'{PATH}/subm/'
os.makedirs(SUBM, exist_ok=True)
df.to_csv(f'{SUBM}subm.gz', compression='gzip', index=False)
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FileLink(f'{SUBM}subm.gz')
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Individual prediction
fn = data.val_ds.fnames[0]
fn
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'train/000bec180eb18c7604dcecc8fe0dba07.jpg'
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Image.open(PATH + fn).resize((150, 150))
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# Method 1.
trn_tfms, val_tfms = tfms_from_model(arch, sz)
ds = FilesIndexArrayDataset([fn], np.array([0]), val_tfms, PATH)
dl = DataLoader(ds)
preds = learn.predict_dl(dl)
np.argmax(preds)
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19
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learn.data.classes[np.argmax(preds)]
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'boston_bull'
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# Method 2.
trn_tfms, val_tfms = tfms_from_model(arch, sz)
im = val_tfms(open_image(PATH + fn)) # open_image() returns numpy.ndarray
preds = learn.predict_array(im[None])
np.argmax(preds)
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19
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