also try hingeloss - as it would be anaolgous to using SVM
add morph features in fc layer
size/ bounding box features
feed features into labelspread
Find the problematic classes (low probs's - massive uncertainty), or high log loss for training data. Augment those particlar classes.
t0 Log loss: 1.33067
classify everything and then aggreage the results of which images are just augmentations of the others and then take the average prediction probabilities
I SCREWED UP... rotation around 45 degrees leaves black background
In [11]:
import numpy as np
import matplotlib.pyplot as plt
import time
import tools.my_io as my_io
%matplotlib inline
import caffe
# Set the right path to your model definition file, pretrained model weights,
# and the image you would like to classify.
MODEL_FILE = '/media/raid_arr/data/ndsb/config/deploy_cnn_v3_maxout_supersparse.prototxt'
PRETRAINED = '/media/raid_arr/data/ndsb/models/zoomed_out_vanilla_smallmaxout/simple_fold0_iter_3000.caffemodel'
MEAN_VALUE = 23
IMAGE_FILE = '/afs/ee.cooper.edu/user/t/a/tam8/data/ndsb/train/acantharia_protist/100224.jpg'
VALIDATION_DB = '/media/raid_arr/tmp/test0_norm_lmdb'
In [7]:
# Loading From Database
print 'Loading data...'
tic = time.time()
data = my_io.load_lmdb(VALIDATION_DB)
print "Done in %.2f s." % (time.time() - tic)
val_files_all, images, labels = zip(*data)
test_labels = labels
Loading data...
Done in 1.62 s.
In [8]:
# Grab 1 image from file
input_image = caffe.io.load_image(IMAGE_FILE, color=False)
In [9]:
image_dims = images[0].shape[:2]
# image_dims = (57, 57)
print image_dims
net = caffe.Classifier(MODEL_FILE, PRETRAINED,
mean=np.array([MEAN_VALUE]),
raw_scale=1.0, # 255 if load from caffe.io, 1.0 if load from my_io lmdb
image_dims=image_dims,)
# gpu=True)
# caffe.set_phase_test()
caffe.set_mode_gpu()
(64, 64)
In [123]:
np.where(test_labels == 113)
Out[123]:
(array([ 52, 98, 130, 324, 348, 410, 417, 536, 538, 721, 723,
738, 896, 939, 961, 1353, 1374, 1429, 1475, 1507, 1689, 1696,
1727, 1761, 1877, 1950, 1963, 2004, 2014, 2111, 2206, 2413, 2463,
2480, 2557, 2649, 2652, 2657, 2670, 2693, 2787, 2812, 2841, 2880,
2887, 2974, 2983, 2993, 3057, 3150, 3223, 3367, 3372, 3439, 3542,
3580, 3599, 3850, 3873, 3997, 4235, 4239, 4251, 4361, 4576, 4778,
4835, 5060, 5068, 5114, 5169, 5176, 5185, 5194, 5232, 5249, 5262,
5287, 5341, 5382, 5573, 5596, 5646, 5747, 5992, 5994, 6030, 6060]),)
In [137]:
# %%time
n = 5262
# im = caffe.io.load_image(test_files[n], color=False)
im_path = val_files_all[n]
# im_loaded = caffe.io.load_image(im_path, color=False)
# im = images[n].astype(np.float32)/255.0
# im = im_loaded
im = images[n]
print im.shape
print le.inverse_transform(test_labels[n])
tic = time.time()
prediction = net.predict([im])
print "Prediction in %.2f s." % (time.time() - tic)
print 'prediction shape:', prediction[0].shape
plt.plot(prediction[0])
print 'predicted class:', prediction[0].argmax()
print 'true class:', test_labels[n]
test_labels[n]
print 'logloss:', -np.log(prediction[0][test_labels[n]])
plt.figure()
plt.imshow(np.squeeze(im), interpolation='none', cmap='gray')
(64, 64, 1)
tunicate_doliolid
Prediction in 0.03 s.
prediction shape: (121,)
predicted class: 113
true class: 113
logloss: 0.00962584
Out[137]:
<matplotlib.image.AxesImage at 0x7fbed0f10f90>
In [140]:
prediction[0][114]
Out[140]:
0.0091936793
In [142]:
start = time.time()
prediction = net.predict(images)
print "Done in %.2f s." % (time.time() - start)
Done in 113.67 s.
In [19]:
ll_ii = np.log(prediction[range(len(test_labels)), test_labels])
ll = -np.mean(np.log(prediction[range(len(test_labels)), test_labels]))
print 'Log loss:', ll
Log loss: 0.943667
In [1]:
from tools.le import le
test_labels = np.array(test_labels)
ll_bylabel = np.zeros(len(set(test_labels)))
labels = sorted(set(test_labels))
for l in labels:
# ll_bylabel[l] = -np.mean(ll_ii[test_labels==l])
ll_bylabel[l] = -np.sum(ll_ii[test_labels==l])/len(labels)
q = np.c_[labels, ll_bylabel]
qq = q[(-q[:,1]).argsort()]
qqq = np.c_[qq[:, ::-1], le.inverse_transform(qq[:,0].astype(int))]
print qqq
# print ll_bylabel
# print np.argmax(ll_bylabel), np.max(ll_bylabel), le.inverse_transform(np.argmax(ll_bylabel))
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-1-f9c795cf9ace> in <module>()
1 from tools.le import le
----> 2 test_labels = np.array(test_labels)
3 ll_bylabel = np.zeros(len(set(test_labels)))
4 labels = sorted(set(test_labels))
5 for l in labels:
NameError: name 'np' is not defined
In [108]:
z = le.inverse_transform(sorted(set(labels)))
zz = [qq for qq in z if 'copepod' in qq]
zz
Out[108]:
['copepod_calanoid',
'copepod_calanoid_eggs',
'copepod_calanoid_eucalanus',
'copepod_calanoid_flatheads',
'copepod_calanoid_frillyAntennae',
'copepod_calanoid_large',
'copepod_calanoid_large_side_antennatucked',
'copepod_calanoid_octomoms',
'copepod_calanoid_small_longantennae',
'copepod_cyclopoid_copilia',
'copepod_cyclopoid_oithona',
'copepod_cyclopoid_oithona_eggs',
'copepod_other']
In [81]:
guess = np.argmax(prediction, axis=1)
# guess_arr = np.c_[test_files, guess]
# np.savetxt('./guess_labels/guess1.txt', guess_arr, fmt='%s', delimiter='\t')
Out[81]:
6115
In [79]:
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from tools.le import le
conf_arr = confusion_matrix(test_labels, guess)
acc = accuracy_score(test_labels, guess)
print acc
d = np.diagonal(conf_arr.copy())
print np.sum(conf_arr)
np.fill_diagonal(conf_arr, 0) # gets rid of correct predictions
pt = np.sum(conf_arr, axis=1) # total predictions
names = np.array(le.inverse_transform(range(121)))
ind = pt.argsort()
np.c_[pt[ind], d[ind], names[ind], np.arange(121)[ind]]
0.722812755519
6115
Out[79]:
array([['0', '3', 'hydromedusae_typeE', '76'],
['0', '8', 'tornaria_acorn_worm_larvae', '107'],
['1', '5', 'echinopluteus', '45'],
['2', '4', 'copepod_cyclopoid_copilia', '23'],
['2', '8', 'shrimp_caridean', '93'],
['2', '6', 'ctenophore_lobate', '31'],
['2', '3', 'hydromedusae_narco_dark', '63'],
['2', '1', 'ephyra', '46'],
['2', '0', 'heteropod', '56'],
['2', '0', 'fish_larvae_deep_body', '50'],
['2', '21', 'protist_star', '86'],
['2', '13', 'acantharia_protist_halo', '2'],
['2', '1', 'acantharia_protist_big_center', '1'],
['2', '0', 'hydromedusae_haliscera_small_sideview', '61'],
['3', '7', 'copepod_calanoid_octomoms', '21'],
['3', '13', 'echinoderm_larva_pluteus_typeC', '40'],
['3', '0', 'hydromedusae_other', '66'],
['3', '4', 'hydromedusae_h15', '59'],
['3', '20', 'fish_larvae_myctophids', '53'],
['3', '15', 'copepod_calanoid_small_longantennae', '22'],
['3', '1', 'hydromedusae_liriope', '62'],
['3', '0', 'pteropod_theco_dev_seq', '88'],
['3', '12', 'tunicate_salp_chains', '117'],
['3', '10', 'pteropod_triangle', '89'],
['3', '8', 'trichodesmium_multiple', '109'],
['3', '0', 'invertebrate_larvae_other_A', '78'],
['3', '13', 'chordate_type1', '13'],
['4', '9', 'copepod_calanoid_frillyAntennae', '18'],
['4', '0', 'appendicularian_fritillaridae', '4'],
['4', '24', 'euphausiids', '47'],
['4', '104', 'echinoderm_larva_seastar_brachiolaria', '43'],
['4', '14', 'echinoderm_larva_pluteus_urchin', '41'],
['4', '1', 'invertebrate_larvae_other_B', '79'],
['4', '54', 'copepod_calanoid_large', '19'],
['4', '4', 'echinoderm_larva_pluteus_brittlestar', '38'],
['4', '15', 'echinoderm_larva_pluteus_early', '39'],
['4', '1', 'stomatopod', '106'],
['4', '9', 'hydromedusae_typeF', '77'],
['4', '21', 'hydromedusae_solmundella', '73'],
['4', '0', 'fish_larvae_very_thin_body', '55'],
['4', '1', 'siphonophore_physonect_young', '105'],
['4', '8', 'hydromedusae_typeD_bell_and_tentacles', '75'],
['5', '26', 'shrimp_sergestidae', '94'],
['5', '5', 'amphipods', '3'],
['5', '2', 'fish_larvae_leptocephali', '51'],
['5', '3', 'euphausiids_young', '48'],
['5', '0', 'copepod_other', '26'],
['5', '18', 'ctenophore_cestid', '28'],
['5', '22', 'polychaete', '81'],
['5', '1', 'trochophore_larvae', '112'],
['5', '1', 'siphonophore_partial', '103'],
['5', '78', 'hydromedusae_shapeA', '68'],
['6', '3', 'hydromedusae_typeD', '74'],
['6', '5', 'decapods', '32'],
['6', '0', 'siphonophore_other_parts', '102'],
['6', '73', 'artifacts', '8'],
['7', '19', 'hydromedusae_aglaura', '57'],
['7', '39', 'hydromedusae_haliscera', '60'],
['7', '64', 'tunicate_partial', '115'],
['8', '1', 'ctenophore_cydippid_no_tentacles', '29'],
['8', '12', 'copepod_calanoid_eucalanus', '16'],
['8', '14', 'copepod_calanoid_large_side_antennatucked', '20'],
['9', '8', 'fish_larvae_medium_body', '52'],
['9', '66', 'protist_fuzzy_olive', '83'],
['9', '20', 'jellies_tentacles', '80'],
['9', '27', 'siphonophore_calycophoran_sphaeronectes', '99'],
['9', '4', 'fish_larvae_thin_body', '54'],
['9', '7', 'hydromedusae_sideview_big', '71'],
['10', '38', 'tunicate_salp', '116'],
['10', '94', 'diatom_chain_string', '36'],
['10', '16', 'siphonophore_physonect', '104'],
['11', '4', 'hydromedusae_bell_and_tentacles', '58'],
['11', '9', 'echinoderm_seacucumber_auricularia_larva', '44'],
['11', '0', 'ctenophore_cydippid_tentacles', '30'],
['11', '1', 'siphonophore_calycophoran_sphaeronectes_stem', '100'],
['11', '32', 'siphonophore_calycophoran_abylidae', '96'],
['11', '0', 'shrimp-like_other', '92'],
['12', '15', 'siphonophore_calycophoran_rocketship_adult', '97'],
['12', '10', 'pteropod_butterfly', '87'],
['14', '83', 'siphonophore_calycophoran_rocketship_young', '98'],
['14', '63', 'echinoderm_larva_seastar_bipinnaria', '42'],
['14', '21', 'shrimp_zoea', '95'],
['14', '128', 'trichodesmium_bowtie', '108'],
['15', '20', 'copepod_calanoid_eggs', '15'],
['15', '7', 'protist_dark_center', '82'],
['15', '126', 'hydromedusae_solmaris', '72'],
['16', '222', 'copepod_cyclopoid_oithona_eggs', '25'],
['16', '124', 'appendicularian_s_shape', '5'],
['17', '15', 'radiolarian_colony', '91'],
['17', '161', 'acantharia_protist', '0'],
['17', '32', 'appendicularian_straight', '7'],
['18', '16', 'artifacts_edge', '9'],
['18', '50', 'hydromedusae_narco_young', '64'],
['20', '160', 'copepod_cyclopoid_oithona', '24'],
['20', '16', 'copepod_calanoid_flatheads', '17'],
['20', '21', 'crustacean_other', '27'],
['21', '9', 'hydromedusae_shapeB', '70'],
['21', '6', 'hydromedusae_narcomedusae', '65'],
['22', '28', 'siphonophore_calycophoran_sphaeronectes_young', '101'],
['23', '35', 'radiolarian_chain', '90'],
['23', '15', 'hydromedusae_partial_dark', '67'],
['23', '32', 'hydromedusae_shapeA_sideview_small', '69'],
['26', '110', 'trichodesmium_tuft', '111'],
['27', '369', 'trichodesmium_puff', '110'],
['28', '97', 'protist_noctiluca', '84'],
['29', '55', 'tunicate_doliolid_nurse', '114'],
['30', '77', 'appendicularian_slight_curve', '6'],
['30', '70', 'diatom_chain_tube', '37'],
['31', '4', 'unknown_sticks', '119'],
['31', '57', 'tunicate_doliolid', '113'],
['41', '96', 'copepod_calanoid', '14'],
['44', '59', 'fecal_pellet', '49'],
['45', '118', 'chaetognath_non_sagitta', '10'],
['48', '25', 'detritus_blob', '33'],
['50', '29', 'detritus_filamentous', '34'],
['50', '14', 'unknown_blobs_and_smudges', '118'],
['53', '130', 'detritus_other', '35'],
['57', '82', 'chaetognath_sagitta', '12'],
['73', '162', 'protist_other', '85'],
['79', '6', 'unknown_unclassified', '120'],
['95', '292', 'chaetognath_other', '11']],
dtype='|S45')
In [102]:
import pandas as pd
conf_arr_raw = confusion_matrix(test_labels, guess)
conf_arr_norm = conf_arr_raw.astype(float)/np.tile(conf_arr_raw.sum(axis=1),(121,1)).T
pd.set_option('display.max_columns', 122)
conf_df = pd.DataFrame(conf_arr_raw)
conf_df
Out[102]:
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0
0
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
2
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
10
0
0
0
0
0
0
0
4
1
120
0
0
0
0
0
2
4
5
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
2
1
0
0
1
0
1
1
5
0
0
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3
0
2
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0
0
0
0
0
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1
0
0
0
0
0
0
0
0
0
1
0
2
0
0
1
0
3
2
0
1
1
0
0
0
0
0
0
1
2
1
1
0
3
0
1
1
2
0
1
0
0
0
1
1
0
4
1
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1
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1
0
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2
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2
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6
In [78]:
conf_arr_raw = confusion_matrix(test_labels, guess)
conf_arr.sum()
Out[78]:
6115
In [265]:
# http://arxiv.org/pdf/1410.0736v3.pdf
import pickle
pickle.dump((test_labels, prediction), open('./tools/naive_prediction.p', 'wb'))
prob_conf = np.zeros((121, 121))
for l in range(121):
inds = np.squeeze(np.array(np.where(test_labels == l)))
class_conf = prediction[inds, :].mean(axis=0)
prob_conf[l, :] = class_conf
F = prob_conf
D = (1-F)
np.fill_diagonal(D, 0)
D_p = 0.5*(D+D.T)
In [263]:
from sklearn.cluster import AffinityPropagation as AP
clst = AP(damping=0.98, # damping determines # of clusters
max_iter=500,
convergence_iter=15,
affinity='euclidean',
verbose=False)
clst.fit(D_p)
print 'Number of cluster:', len(clst.cluster_centers_)
membership = np.c_[range(121), clst.labels_]
fine_to_coarse = dict(membership)
coarse_to_fine = {l: [] for l in clst.labels_}
for k, v in fine_to_coarse.items():
coarse_to_fine[v].append(k)
Number of cluster: 27
Out[263]:
{0: [0, 1, 63, 112],
1: [4, 5, 6],
2: [10, 11, 12, 51],
3: [14, 15, 16, 17, 18, 19, 22],
4: [21, 24, 25, 73],
5: [39, 40, 41],
6: [42, 43, 78],
7: [23, 31, 46, 76, 91],
8: [3, 47, 48, 92],
9: [34, 37, 49, 55, 111, 119],
10: [50, 52, 53, 54, 106],
11: [58, 59, 75],
12: [38, 57, 64],
13: [60, 68, 71, 74],
14: [29, 61, 69, 70],
15: [62, 65, 72],
16: [8, 36, 45, 80],
17: [2, 83, 84, 85, 86],
18: [32, 93, 95],
19: [44, 66, 96, 102],
20: [30, 98, 99, 100, 101, 105],
21: [28, 81, 90, 94, 97, 104, 117],
22: [82, 108, 109, 110],
23: [107, 113, 114, 116],
24: [9, 56, 67, 103, 115],
25: [13, 20, 26, 27, 33, 35, 77, 118],
26: [7, 79, 87, 88, 89, 120]}
In [112]:
from collections import OrderedDict
specialists_d = OrderedDict([
('chaetognath', [
'chaetognath_non_sagitta',
'chaetognath_other',
'chaetognath_sagitta']),
('copepod', [
'copepod_calanoid',
'copepod_calanoid_eggs',
'copepod_calanoid_eucalanus',
'copepod_calanoid_flatheads',
'copepod_calanoid_frillyAntennae',
'copepod_calanoid_large',
'copepod_calanoid_large_side_antennatucked',
'copepod_calanoid_octomoms',
'copepod_calanoid_small_longantennae',
'copepod_cyclopoid_copilia',
'copepod_cyclopoid_oithona',
'copepod_cyclopoid_oithona_eggs',
'copepod_other']),
('tunicate_doliolid', [
'tunicate_doliolid',
'tunicate_doliolid_nurse']),
])
sp_member_d = {}
for p, c in specialists_d.items():
for m in c:
sp_member_d[m] = p
In [113]:
sp_member_d
Out[113]:
{'chaetognath_non_sagitta': 'chaetognath',
'chaetognath_other': 'chaetognath',
'chaetognath_sagitta': 'chaetognath',
'copepod_calanoid': 'copepod',
'copepod_calanoid_eggs': 'copepod',
'copepod_calanoid_eucalanus': 'copepod',
'copepod_calanoid_flatheads': 'copepod',
'copepod_calanoid_frillyAntennae': 'copepod',
'copepod_calanoid_large': 'copepod',
'copepod_calanoid_large_side_antennatucked': 'copepod',
'copepod_calanoid_octomoms': 'copepod',
'copepod_calanoid_small_longantennae': 'copepod',
'copepod_cyclopoid_copilia': 'copepod',
'copepod_cyclopoid_oithona': 'copepod',
'copepod_cyclopoid_oithona_eggs': 'copepod',
'copepod_other': 'copepod',
'tunicate_doliolid': 'tunicate_doliolid',
'tunicate_doliolid_nurse': 'tunicate_doliolid'}
Content source: JasonTam/ndsb2015
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