In [400]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import makemodel
%matplotlib inline
In [401]:
import getresults
In [402]:
sel = ['base_lr_exp','weight_decay_exp','momentum','solver','loss_gap']
params = open("priority").read().rstrip().split()
defaults = makemodel.getdefaults()
params = defaults.keys()
res = getresults.getres('35.196.158.205','optimize','opt2',5,'priority.old',params)
In [403]:
res[:10]
Out[403]:
rmse_mean
top_mean
R_mean
auc_mean
Rtop_mean
rmse_std
top_std
R_std
auc_std
Rtop_std
serial_min
serial_max
conv4_norm
loss_pseudohuber
jitter
conv5_width
conv2_width
pool4_size
conv1_stride
conv5_norm
conv3_size
pool4_type
balanced
ranklossneg
stratify_affinity_step
fc_pose_hidden2
conv1_norm
conv3_norm
conv2_size
pool2_type
loss_penalty
fc_pose_init
conv1_size
conv2_stride
fc_pose_hidden
loss_gap
conv1_width
conv2_init
pool1_type
conv4_width
pool2_size
conv3_init
fc_affinity_func
stratify_affinity
conv3_width
fc_affinity_init
conv5_size
conv4_stride
pool1_size
loss_delta
pool3_size
weight_decay_exp
fc_pose_func2
fc_pose_func
conv1_func
conv2_norm
ranklossmult
conv5_func
conv3_func
pool3_type
pool5_size
fc_affinity_hidden
conv5_init
conv3_stride
fc_affinity_func2
solver
fc_affinity_hidden2
conv5_stride
base_lr_exp
stratify_receptor
conv4_size
momentum
conv1_init
conv4_func
pool5_type
conv2_func
resolution
conv4_init
0
6.713507
0.163237
-0.061112
0.507221
-0.009966
0.091949
0.003888
0.126287
0.017459
0.020781
NaN
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none
1
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1
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3
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1
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1
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0.0
32
xavier
MAX
0
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xavier
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xavier
3
1
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4.0
2
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ReLU
ReLU
ReLU
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ReLU
ReLU
MAX
0
0
xavier
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ReLU
SGD
0
1
-10.000000
1
3
0.900000
xavier
ReLU
MAX
ReLU
0.5
xavier
1
6.689660
0.171873
0.014303
0.503954
0.002274
0.060950
0.005421
0.083898
0.009705
0.014813
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none
1
0.0
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0
1
none
3
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1
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3
MAX
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1
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32
xavier
MAX
0
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xavier
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0
128
xavier
3
1
2
4.0
2
0.000000
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
SGD
0
1
-10.000000
1
3
0.900000
xavier
ReLU
MAX
ReLU
0.5
xavier
2
2.140198
0.190871
0.054286
0.559895
0.010319
0.017856
0.009279
0.024068
0.014516
0.004571
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
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none
none
3
MAX
0.0
xavier
3
1
0
0.0
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
2
4.0
2
-2.844410
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
SGD
0
1
-6.403725
1
3
0.900000
xavier
ReLU
MAX
ReLU
0.5
xavier
3
2.046162
0.201776
0.134296
0.600150
0.027105
0.040417
0.009681
0.055948
0.019470
0.011733
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
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none
none
3
MAX
0.0
xavier
3
1
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0.0
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
2
4.0
2
-10.000000
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
SGD
0
1
-5.000000
1
3
0.000000
xavier
ReLU
MAX
ReLU
0.5
xavier
4
1.769426
0.507377
0.476866
0.788841
0.242262
0.018329
0.021433
0.019431
0.013198
0.019522
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
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none
none
3
MAX
0.0
xavier
3
1
0
5.0
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
2
4.0
2
-10.000000
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
Adam
0
1
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1
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0.168839
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ReLU
MAX
ReLU
0.5
xavier
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1.768672
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2
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ReLU
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ReLU
MAX
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1
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3
0.283696
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ReLU
MAX
ReLU
0.5
xavier
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ReLU
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ReLU
MAX
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xavier
1
ReLU
SGD
0
1
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1
3
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xavier
ReLU
MAX
ReLU
0.5
xavier
7
1.786111
0.492968
0.460157
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0.226988
0.023583
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NaN
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none
1
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1
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3
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1
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none
none
3
MAX
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xavier
3
1
0
5.0
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
2
4.0
2
-10.000000
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
Adam
0
1
-5.000000
1
3
0.648182
xavier
ReLU
MAX
ReLU
0.5
xavier
8
1.829790
0.299433
0.424435
0.608110
0.127096
0.024214
0.015246
0.024120
0.021901
0.010002
NaN
NaN
none
1
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1
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3
MAX
1
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1
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none
none
3
MAX
0.0
xavier
3
1
0
0.0
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
2
4.0
2
-10.000000
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
SGD
0
1
-5.000000
1
3
0.900000
xavier
ReLU
MAX
ReLU
0.5
xavier
9
1.840619
0.294794
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NaN
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3
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3
1
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xavier
MAX
0
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0
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xavier
3
1
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4.0
2
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ReLU
ReLU
ReLU
none
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ReLU
MAX
0
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SGD
0
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1
3
0.900000
xavier
ReLU
MAX
ReLU
0.5
xavier
In [404]:
len(res)
Out[404]:
502
In [405]:
c = sns.color_palette()
for (i,n) in enumerate(params):
plt.figure()
plt.errorbar(res[n],res.Rtop_mean,yerr=res.Rtop_std,fmt='o',color=c[i%len(c)])
plt.title(n)
In [406]:
res
Out[406]:
rmse_mean
top_mean
R_mean
auc_mean
Rtop_mean
rmse_std
top_std
R_std
auc_std
Rtop_std
serial_min
serial_max
conv4_norm
loss_pseudohuber
jitter
conv5_width
conv2_width
pool4_size
conv1_stride
conv5_norm
conv3_size
pool4_type
balanced
ranklossneg
stratify_affinity_step
fc_pose_hidden2
conv1_norm
conv3_norm
conv2_size
pool2_type
loss_penalty
fc_pose_init
conv1_size
conv2_stride
fc_pose_hidden
loss_gap
conv1_width
conv2_init
pool1_type
conv4_width
pool2_size
conv3_init
fc_affinity_func
stratify_affinity
conv3_width
fc_affinity_init
conv5_size
conv4_stride
pool1_size
loss_delta
pool3_size
weight_decay_exp
fc_pose_func2
fc_pose_func
conv1_func
conv2_norm
ranklossmult
conv5_func
conv3_func
pool3_type
pool5_size
fc_affinity_hidden
conv5_init
conv3_stride
fc_affinity_func2
solver
fc_affinity_hidden2
conv5_stride
base_lr_exp
stratify_receptor
conv4_size
momentum
conv1_init
conv4_func
pool5_type
conv2_func
resolution
conv4_init
0
6.713507e+00
0.163237
-0.061112
0.507221
-0.009966
9.194883e-02
0.003888
0.126287
0.017459
0.020781
NaN
NaN
none
1
0.000000
0
64
0
1
none
3
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1
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3
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0.000000
xavier
3
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32
xavier
MAX
0
2
xavier
ReLU
0
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xavier
3
1
2
4.000000
2
-10.000000
ReLU
ReLU
ReLU
none
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ReLU
ReLU
MAX
0
0
xavier
1
ReLU
SGD
0
1
-10.000000
1
3
9.000000e-01
xavier
ReLU
MAX
ReLU
0.5
xavier
1
6.689660e+00
0.171873
0.014303
0.503954
0.002274
6.094957e-02
0.005421
0.083898
0.009705
0.014813
NaN
NaN
none
1
0.000000
0
64
0
1
none
3
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1
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none
none
3
MAX
0.000000
xavier
3
1
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0.000000
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
2
4.000000
2
0.000000
ReLU
ReLU
ReLU
none
0.000000
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
SGD
0
1
-10.000000
1
3
9.000000e-01
xavier
ReLU
MAX
ReLU
0.5
xavier
2
2.140198e+00
0.190871
0.054286
0.559895
0.010319
1.785640e-02
0.009279
0.024068
0.014516
0.004571
NaN
NaN
none
1
0.000000
0
64
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1
none
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1
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3
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xavier
3
1
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0.000000
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
2
4.000000
2
-2.844410
ReLU
ReLU
ReLU
none
0.000000
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
SGD
0
1
-6.403725
1
3
9.000000e-01
xavier
ReLU
MAX
ReLU
0.5
xavier
3
2.046162e+00
0.201776
0.134296
0.600150
0.027105
4.041741e-02
0.009681
0.055948
0.019470
0.011733
NaN
NaN
none
1
0.000000
0
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1
none
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1
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none
3
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xavier
3
1
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0.000000
32
xavier
MAX
0
2
xavier
ReLU
0
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xavier
3
1
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4.000000
2
-10.000000
ReLU
ReLU
ReLU
none
0.000000
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
SGD
0
1
-5.000000
1
3
0.000000e+00
xavier
ReLU
MAX
ReLU
0.5
xavier
4
1.769426e+00
0.507377
0.476866
0.788841
0.242262
1.832867e-02
0.021433
0.019431
0.013198
0.019522
NaN
NaN
none
1
0.000000
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xavier
MAX
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ReLU
MAX
0
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xavier
1
ReLU
Adam
0
1
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1
3
1.688390e-01
xavier
ReLU
MAX
ReLU
0.5
xavier
5
1.768672e+00
0.502048
0.488201
0.778817
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ReLU
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0
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ReLU
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0
1
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1
3
2.836956e-01
xavier
ReLU
MAX
ReLU
0.5
xavier
6
2.041885e+00
0.223933
0.097614
0.612821
0.021986
3.125887e-02
0.008074
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0.010064
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none
1
0.000000
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3
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xavier
MAX
0
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xavier
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0
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xavier
3
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ReLU
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ReLU
ReLU
MAX
0
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1
ReLU
SGD
0
1
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1
3
6.037905e-01
xavier
ReLU
MAX
ReLU
0.5
xavier
7
1.786111e+00
0.492968
0.460157
0.781292
0.226988
2.358302e-02
0.024405
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NaN
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1
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xavier
MAX
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0
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xavier
3
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4.000000
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ReLU
ReLU
ReLU
none
0.000000
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
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0
1
-5.000000
1
3
6.481820e-01
xavier
ReLU
MAX
ReLU
0.5
xavier
8
1.829790e+00
0.299433
0.424435
0.608110
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2.421427e-02
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xavier
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ReLU
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ReLU
ReLU
MAX
0
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xavier
1
ReLU
SGD
0
1
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1
3
9.000000e-01
xavier
ReLU
MAX
ReLU
0.5
xavier
9
1.840619e+00
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0.413911
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ReLU
MAX
0
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xavier
1
ReLU
SGD
0
1
-5.000000
1
3
9.000000e-01
xavier
ReLU
MAX
ReLU
0.5
xavier
10
1.831601e+00
0.295485
0.423350
0.586281
0.125299
2.615563e-02
0.018780
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NaN
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none
1
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xavier
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3
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4.000000
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ReLU
ReLU
ReLU
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ReLU
ReLU
MAX
0
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xavier
1
ReLU
SGD
0
1
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1
3
9.000000e-01
xavier
ReLU
MAX
ReLU
0.5
xavier
11
1.976953e+00
0.212830
0.238952
0.605170
0.050512
2.562374e-02
0.010493
0.043841
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ReLU
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ReLU
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ReLU
SGD
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1
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xavier
ReLU
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ReLU
0.5
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4.000000
2
0.000000
ReLU
ReLU
ReLU
none
0.000000
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
Adam
0
1
0.000000
1
3
1.110223e-16
xavier
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MAX
ReLU
0.5
xavier
501
8.547340e+02
0.485320
-0.008742
0.496206
-0.004243
1.707887e+03
0.000000
0.169238
0.019198
0.082134
NaN
NaN
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1
0.000000
0
64
0
1
none
3
MAX
1
0
1
0
none
none
3
MAX
0.000000
xavier
3
1
0
4.942285
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
2
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2
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ReLU
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MAX
0
0
xavier
1
ReLU
SGD
0
1
0.000000
1
3
5.253142e-01
xavier
ReLU
MAX
ReLU
0.5
xavier
In [ ]:
In [407]:
sns.swarmplot(res.Rtop_mean)
Out[407]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f73ab99bad0>
In [ ]:
In [408]:
len(res)
Out[408]:
502
Identify rows that differ from default in only one dimension
In [ ]:
In [ ]:
In [409]:
goodrows = []
catnames = []
catvals = []
for (i,row) in res.iterrows():
diff = 0
catname = 'default'
catval = 'default'
for (k,v) in defaults.iteritems():
if k in row:
if row[k] != v:
diff += 1
print k,row[k],v
catname = k
catval = row[k]
if diff <= 1:
goodrows.append(i)
catnames.append(catname)
catvals.append(catval)
weight_decay_exp -10.0 -3
base_lr_exp -10.0 -2
weight_decay_exp 0.0 -3
base_lr_exp -10.0 -2
weight_decay_exp -2.84440970809 -3
base_lr_exp -6.40372481959 -2
weight_decay_exp -10.0 -3
base_lr_exp -5.0 -2
momentum 0.0 0.9
loss_gap 5.0 0
weight_decay_exp -10.0 -3
solver Adam SGD
base_lr_exp -5.0 -2
momentum 0.168839040615 0.9
weight_decay_exp -10.0 -3
solver Adam SGD
base_lr_exp -5.0 -2
momentum 0.283695631553 0.9
loss_gap 5.0 0
weight_decay_exp -10.0 -3
base_lr_exp -5.0 -2
momentum 0.60379050869 0.9
loss_gap 5.0 0
weight_decay_exp -10.0 -3
solver Adam SGD
base_lr_exp -5.0 -2
momentum 0.648182021315 0.9
weight_decay_exp -10.0 -3
base_lr_exp -5.0 -2
weight_decay_exp -8.37195457525 -3
base_lr_exp -5.0 -2
weight_decay_exp -6.17658463436 -3
base_lr_exp -5.0 -2
weight_decay_exp -5.85892303321 -3
base_lr_exp -5.0 -2
momentum 0.228619533688 0.9
base_lr_exp -5.0 -2
weight_decay_exp -1.53213699711 -3
base_lr_exp -5.0 -2
momentum 0.0 0.9
weight_decay_exp 0.0 -3
solver Adam SGD
base_lr_exp -5.0 -2
momentum 0.0 0.9
weight_decay_exp 0.0 -3
base_lr_exp -5.0 -2
momentum 0.296841598262 0.9
weight_decay_exp 0.0 -3
solver Adam SGD
base_lr_exp -5.0 -2
momentum 0.486359723864 0.9
weight_decay_exp -3.10148500124 -3
base_lr_exp -4.81001349926 -2
weight_decay_exp 0.0 -3
base_lr_exp -4.80261479471 -2
base_lr_exp -4.375 -2
weight_decay_exp -10.0 -3
base_lr_exp -4.24797032671 -2
weight_decay_exp -1.93048805127 -3
base_lr_exp -3.93422254681 -2
weight_decay_exp -4.47727073334 -3
solver Adam SGD
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momentum 0.0 0.9
base_lr_exp -3.75 -2
weight_decay_exp -7.7099722836 -3
base_lr_exp -3.73299691771 -2
weight_decay_exp -4.96700660855 -3
base_lr_exp -3.42737232088 -2
weight_decay_exp -10.0 -3
base_lr_exp -3.41846728834 -2
momentum 0.0 0.9
weight_decay_exp -3.07921406318 -3
base_lr_exp -3.38012436659 -2
loss_penalty 0.152219440282 0
loss_gap 1.19460560112 0
weight_decay_exp -7.41236546163 -3
base_lr_exp -3.28289185875 -2
momentum 0.667540268835 0.9
loss_gap 5.0 0
weight_decay_exp 0.0 -3
base_lr_exp -3.22999308227 -2
momentum 1.0 0.9
weight_decay_exp -10.0 -3
base_lr_exp -3.13751514985 -2
jitter 0.746124 0.0
conv5_width 64 0
conv2_width 256 64
conv1_stride 2 1
conv5_norm LRN none
conv3_size 5 3
pool4_type AVE MAX
fc_pose_hidden2 128 0
conv1_norm LRN none
pool2_type AVE MAX
loss_penalty 1.07772827148 0
conv1_size 1 3
conv2_stride 3 1
loss_gap 4.15054321289 0
conv1_width 4 32
conv2_init positive_unitball xavier
conv4_width 64 0
conv3_init radial.5 xavier
fc_affinity_func leaky ReLU
conv3_width 256 128
fc_affinity_init msra xavier
conv5_size 5 3
loss_delta 4.70141601562 4
weight_decay_exp -8.94256591797 -3
fc_pose_func ELU ReLU
ranklossmult 0.210784912109 0
conv5_func Sigmoid ReLU
pool3_type AVE MAX
fc_affinity_hidden 256 0
conv5_init radial.5 xavier
conv3_stride 2 1
fc_affinity_func2 TanH ReLU
fc_affinity_hidden2 256 0
conv5_stride 4 1
base_lr_exp -3.13552856445 -2
stratify_receptor 0 1
conv4_size 1 3
momentum 0.739288330078 0.9
conv1_init radial.5 xavier
conv4_func ELU ReLU
conv4_init positive_unitball xavier
base_lr_exp -3.125 -2
weight_decay_exp -10.0 -3
base_lr_exp -3.07643620714 -2
momentum 0.184595803088 0.9
conv4_norm LRN none
jitter 0.543976 0.0
conv5_width 16 0
conv2_width 128 64
conv1_stride 2 1
conv5_norm LRN none
conv3_size 5 3
pool4_type AVE MAX
balanced 0 1
stratify_affinity_step 2 1
fc_pose_hidden2 32 0
conv1_norm BatchNorm none
pool2_type AVE MAX
loss_penalty 0.272064208984 0
fc_pose_init msra xavier
fc_pose_hidden 2048 0
loss_gap 2.24136352539 0
conv1_width 128 32
conv2_init radial.5 xavier
pool1_type AVE MAX
conv4_width 1 0
conv3_init radial.5 xavier
fc_affinity_func TanH ReLU
conv3_width 4 128
fc_affinity_init uniform xavier
loss_delta 7.25610351562 4
pool3_size 0 2
weight_decay_exp -5.18280029297 -3
fc_pose_func2 ELU ReLU
conv1_func leaky ReLU
conv2_norm LRN none
ranklossmult 0.0574645996094 0
conv5_func TanH ReLU
pool3_type AVE MAX
pool5_size 4 0
fc_affinity_hidden 32 0
conv5_init gaussian xavier
conv5_stride 4 1
base_lr_exp -2.95486450195 -2
stratify_receptor 0 1
conv4_size 5 3
momentum 0.865264892578 0.9
conv1_init positive_unitball xavier
conv4_func TanH ReLU
pool5_type AVE MAX
conv2_func ELU ReLU
conv4_init msra xavier
weight_decay_exp -10.0 -3
base_lr_exp -2.89536646566 -2
momentum 0.471774804698 0.9
weight_decay_exp -8.22329287803 -3
base_lr_exp -2.82673660223 -2
weight_decay_exp -3.12488231009 -3
solver Adam SGD
base_lr_exp -2.73973207759 -2
momentum 1.0 0.9
jitter 0.311462 0.0
conv5_width 2 0
conv2_width 16 64
pool4_size 4 0
conv1_stride 2 1
conv5_norm LRN none
conv3_size 1 3
balanced 0 1
stratify_affinity_step 2 1
fc_pose_hidden2 2048 0
conv1_norm LRN none
loss_penalty 4.12872314453 0
conv1_size 5 3
fc_pose_hidden 512 0
loss_gap 0.849304199219 0
conv1_width 256 32
conv4_width 16 0
conv3_init radial.5 xavier
fc_affinity_func TanH ReLU
conv3_width 4 128
fc_affinity_init msra xavier
conv5_size 7 3
pool1_size 4 2
loss_delta 3.58251953125 4
pool3_size 8 2
weight_decay_exp -3.29406738281 -3
fc_pose_func2 Sigmoid ReLU
fc_pose_func TanH ReLU
conv1_func TanH ReLU
ranklossmult 0.00836181640625 0
conv5_func Sigmoid ReLU
conv3_func TanH ReLU
pool3_type AVE MAX
pool5_size 2 0
fc_affinity_hidden 32 0
conv5_init msra xavier
fc_affinity_func2 TanH ReLU
fc_affinity_hidden2 128 0
base_lr_exp -2.73284912109 -2
conv4_size 7 3
momentum 0.696716308594 0.9
conv1_init msra xavier
conv4_func Sigmoid ReLU
pool5_type AVE MAX
weight_decay_exp -3.30869902846 -3
base_lr_exp -2.67985292234 -2
momentum 1.0 0.9
weight_decay_exp 0.0 -3
base_lr_exp -2.67404489058 -2
weight_decay_exp -2.88852471967 -3
base_lr_exp -2.58389435126 -2
weight_decay_exp -4.32652271743 -3
base_lr_exp -2.58239978141 -2
momentum 0.70322197946 0.9
loss_gap 5.0 0
weight_decay_exp -6.99431217128 -3
base_lr_exp -2.55489768001 -2
momentum 0.768053381967 0.9
loss_pseudohuber 0 1
loss_gap 0.201299979641 0
weight_decay_exp -10.0 -3
base_lr_exp -2.54765175399 -2
momentum 0.811797692569 0.9
weight_decay_exp -3.54679141955 -3
base_lr_exp -2.53635404797 -2
momentum 0.819170025669 0.9
weight_decay_exp -3.6158830769 -3
base_lr_exp -2.5339560278 -2
momentum 0.102443793354 0.9
loss_penalty 5.0 0
weight_decay_exp -8.71246937282 -3
base_lr_exp -2.53300655112 -2
momentum 0.78483188217 0.9
weight_decay_exp -3.15894856876 -3
base_lr_exp -2.52814567177 -2
weight_decay_exp -8.01067218154 -3
solver Adam SGD
base_lr_exp -2.52565743893 -2
momentum 0.786030957885 0.9
conv1_stride 2 1
balanced 0 1
conv2_init positive_unitball xavier
pool2_size 0 2
pool1_size 0 2
pool3_size 8 2
conv3_func ELU ReLU
pool3_type AVE MAX
base_lr_exp -2.5 -2
fc_pose_hidden 64 0
conv3_init positive_unitball xavier
stratify_affinity 1 0
pool1_size 0 2
pool3_size 0 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
fc_affinity_hidden2 32 0
base_lr_exp -2.5 -2
base_lr_exp -2.5 -2
weight_decay_exp -3.64626967295 -3
base_lr_exp -2.48688908979 -2
weight_decay_exp -4.17816422103 -3
base_lr_exp -2.4428468893 -2
weight_decay_exp -10.0 -3
solver Adam SGD
base_lr_exp -2.44093287371 -2
momentum 1.0 0.9
weight_decay_exp -9.04778603153 -3
base_lr_exp -2.4283184542 -2
momentum 0.0 0.9
weight_decay_exp -5.20016885729 -3
base_lr_exp -2.41121312878 -2
weight_decay_exp -10.0 -3
base_lr_exp -2.38437485194 -2
momentum 0.662369729118 0.9
weight_decay_exp -7.948131875 -3
solver Adam SGD
base_lr_exp -2.38165621472 -2
momentum 0.120771027007 0.9
loss_penalty 0.441984965352 0
weight_decay_exp -7.68993250634 -3
base_lr_exp -2.3590546107 -2
momentum 0.789279683036 0.9
weight_decay_exp -10.0 -3
base_lr_exp -2.31727080178 -2
conv4_norm LRN none
jitter 0.260529 0.0
conv5_width 512 0
conv2_width 128 64
conv1_stride 4 1
conv5_norm BatchNorm none
conv3_size 7 3
balanced 0 1
ranklossneg 1 0
stratify_affinity_step 4 1
fc_pose_hidden2 32 0
conv2_size 7 3
loss_penalty 3.36410522461 0
fc_pose_init msra xavier
conv1_size 7 3
conv2_stride 2 1
fc_pose_hidden 1024 0
loss_gap 3.46817016602 0
conv1_width 1024 32
conv2_init radial.5 xavier
conv4_width 256 0
pool2_size 8 2
conv3_init radial xavier
fc_affinity_func TanH ReLU
stratify_affinity 1 0
conv3_width 1 128
fc_affinity_init msra xavier
conv5_size 7 3
conv4_stride 3 1
loss_delta 2.98852539062 4
weight_decay_exp -5.34149169922 -3
fc_pose_func2 ELU ReLU
fc_pose_func ELU ReLU
conv2_norm BatchNorm none
ranklossmult 0.213470458984 0
conv5_func leaky ReLU
conv3_func leaky ReLU
pool5_size 2 0
conv5_init radial.5 xavier
conv3_stride 3 1
fc_affinity_func2 TanH ReLU
fc_affinity_hidden2 512 0
conv5_stride 3 1
base_lr_exp -2.23831176758 -2
stratify_receptor 0 1
conv4_size 1 3
momentum 0.942169189453 0.9
conv1_init uniform xavier
conv2_func TanH ReLU
resolution 1.0 0.5
conv4_init radial xavier
weight_decay_exp -4.01774373978 -3
base_lr_exp -2.22916734285 -2
weight_decay_exp -5.86653511689 -3
base_lr_exp -2.21513299179 -2
weight_decay_exp -8.78969164086 -3
base_lr_exp -2.18729737151 -2
loss_gap 5.0 0
weight_decay_exp -10.0 -3
base_lr_exp -2.04583649846 -2
momentum 0.839659011561 0.9
weight_decay_exp -6.08365221401 -3
base_lr_exp -2.04315566325 -2
momentum 0.2644678749 0.9
weight_decay_exp -4.34018172459 -3
base_lr_exp -2.02452231046 -2
weight_decay_exp -10.0 -3
weight_decay_exp -8.75 -3
weight_decay_exp -7.5 -3
weight_decay_exp -6.25 -3
weight_decay_exp -5.0 -3
weight_decay_exp -3.75 -3
momentum 0.0 0.9
momentum 0.125 0.9
pool4_type AVE MAX
ranklossneg 1 0
stratify_affinity_step 2 1
loss_gap 0.625 0
pool2_size 0 2
pool1_size 0 2
pool3_size 8 2
fc_pose_func ELU ReLU
pool3_type AVE MAX
conv5_init gaussian xavier
conv5_stride 3 1
conv4_size 1 3
momentum 0.125 0.9
momentum 0.25 0.9
momentum 0.375 0.9
momentum 0.5 0.9
momentum 0.625 0.9
fc_pose_hidden 2048 0
conv1_width 128 32
pool1_type AVE MAX
conv3_init positive_unitball xavier
fc_affinity_func ELU ReLU
fc_affinity_init msra xavier
conv5_size 7 3
conv4_stride 2 1
pool1_size 0 2
fc_pose_func TanH ReLU
conv3_func leaky ReLU
pool3_type AVE MAX
conv3_stride 3 1
fc_affinity_func2 Sigmoid ReLU
momentum 0.75 0.9
conv4_init gaussian xavier
momentum 0.75 0.9
momentum 0.875 0.9
solver Adam SGD
loss_pseudohuber 0 1
conv3_size 5 3
fc_pose_hidden 64 0
conv3_init radial xavier
conv4_stride 2 1
pool1_size 0 2
conv1_func TanH ReLU
pool3_type AVE MAX
conv5_init msra xavier
conv1_init radial xavier
loss_pseudohuber 0 1
loss_delta 0.0 4
loss_delta 1.0 4
loss_delta 2.0 4
loss_delta 3.0 4
pool2_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
pool1_size 0 2
pool3_size 0 2
pool3_type AVE MAX
fc_affinity_hidden 4096 0
pool2_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
pool2_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
pool1_size 0 2
pool3_size 0 2
pool1_type AVE MAX
pool2_size 0 2
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 0 2
pool3_size 8 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
pool1_type AVE MAX
pool2_size 8 2
pool1_size 0 2
pool3_size 0 2
pool3_type AVE MAX
pool1_type AVE MAX
pool2_size 8 2
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
pool3_size 8 2
pool3_type AVE MAX
pool1_type AVE MAX
pool2_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
pool1_size 4 2
pool3_type AVE MAX
pool2_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
pool1_size 4 2
pool3_size 4 2
pool3_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
pool1_size 4 2
pool3_size 0 2
pool3_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
pool1_size 4 2
pool3_size 4 2
pool3_type AVE MAX
pool1_type AVE MAX
pool2_size 8 2
pool1_size 4 2
pool3_size 0 2
pool3_type AVE MAX
pool2_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
pool1_size 8 2
pool3_type AVE MAX
fc_affinity_hidden 4096 0
pool2_type AVE MAX
pool1_type AVE MAX
pool1_size 8 2
pool3_size 8 2
pool2_type AVE MAX
pool1_type AVE MAX
pool2_size 4 2
pool1_size 8 2
pool3_size 0 2
pool2_type AVE MAX
pool1_type AVE MAX
pool2_size 8 2
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool2_type AVE MAX
pool1_type AVE MAX
pool2_size 8 2
fc_affinity_func TanH ReLU
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
pool1_size 8 2
pool3_size 4 2
pool3_type AVE MAX
fc_affinity_hidden 4096 0
pool1_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool1_type AVE MAX
pool2_size 0 2
pool1_size 8 2
pool3_size 8 2
pool1_type AVE MAX
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool1_type AVE MAX
pool2_size 8 2
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool2_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
pool2_type AVE MAX
pool2_size 0 2
pool1_size 0 2
pool3_size 4 2
pool2_type AVE MAX
pool2_size 8 2
fc_affinity_func TanH ReLU
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
conv1_size 5 3
conv1_width 64 32
pool2_size 0 2
conv3_init positive_unitball xavier
fc_affinity_func ELU ReLU
pool1_size 0 2
pool3_size 8 2
fc_pose_func ELU ReLU
conv2_norm LRN none
pool3_type AVE MAX
conv5_stride 4 1
pool2_size 0 2
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
conv3_stride 2 1
conv1_norm LRN none
fc_pose_hidden 64 0
pool2_size 0 2
conv3_init positive_unitball xavier
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
pool4_type AVE MAX
fc_pose_init uniform xavier
fc_pose_hidden 64 0
pool2_size 0 2
conv3_init positive_unitball xavier
pool1_size 0 2
pool3_size 8 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
stratify_receptor 0 1
fc_pose_hidden 1024 0
pool2_size 0 2
conv3_init positive_unitball xavier
stratify_affinity 1 0
conv3_width 64 128
fc_affinity_init msra xavier
pool1_size 0 2
pool3_size 8 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
conv4_size 7 3
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 0 2
pool3_size 8 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
pool2_size 0 2
pool1_size 0 2
pool3_size 4 2
pool2_size 0 2
pool1_size 0 2
pool3_size 8 2
pool1_size 0 2
conv1_func TanH ReLU
pool3_type AVE MAX
conv1_init gaussian xavier
conv2_width 32 64
conv1_size 1 3
pool1_size 0 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
conv5_init positive_unitball xavier
fc_pose_hidden 64 0
conv3_init positive_unitball xavier
fc_affinity_init msra xavier
pool1_size 0 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
fc_pose_hidden 64 0
conv3_init positive_unitball xavier
pool1_size 0 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
conv2_stride 4 1
fc_pose_hidden 64 0
conv3_init positive_unitball xavier
pool1_size 0 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
conv1_stride 2 1
fc_pose_hidden 64 0
conv3_init positive_unitball xavier
pool1_size 0 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
conv5_norm LRN none
conv1_norm LRN none
fc_pose_hidden 256 0
conv1_width 8 32
conv3_init positive_unitball xavier
pool1_size 0 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
conv4_norm LRN none
ranklossneg 1 0
conv3_norm LRN none
fc_pose_hidden 1024 0
conv3_init gaussian xavier
fc_affinity_func TanH ReLU
pool1_size 0 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
fc_affinity_func2 ELU ReLU
stratify_receptor 0 1
conv1_init radial.5 xavier
conv2_func leaky ReLU
conv4_init uniform xavier
fc_pose_hidden 64 0
pool1_size 0 2
pool3_size 8 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
conv4_size 1 3
pool4_type AVE MAX
pool1_size 0 2
conv1_func ELU ReLU
conv5_func leaky ReLU
pool4_type AVE MAX
pool1_size 0 2
pool4_type AVE MAX
conv5_size 5 3
pool1_size 0 2
pool1_size 0 2
balanced 0 1
conv3_norm BatchNorm none
fc_pose_hidden 64 0
conv3_init positive_unitball xavier
conv4_stride 3 1
pool1_size 0 2
fc_pose_func TanH ReLU
resolution 1.0 0.5
pool2_size 4 2
pool1_size 0 2
pool3_size 0 2
pool2_size 4 2
pool1_size 0 2
pool2_size 8 2
fc_affinity_func TanH ReLU
pool1_size 0 2
pool3_size 8 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
pool2_size 8 2
pool1_size 0 2
pool3_size 8 2
fc_affinity_hidden 256 0
pool2_type AVE MAX
pool2_type AVE MAX
pool2_size 8 2
pool3_size 0 2
pool3_type AVE MAX
pool2_type AVE MAX
pool2_size 8 2
pool3_size 8 2
pool2_size 0 2
pool3_size 0 2
pool3_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool3_size 8 2
pool3_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool3_size 8 2
fc_pose_func Sigmoid ReLU
pool3_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool3_size 8 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
conv5_width 128 0
conv2_width 32 64
conv2_size 1 3
conv4_width 64 0
pool2_size 0 2
conv3_width 64 128
pool5_size 2 0
conv4_size 1 3
pool2_size 0 2
conv5_width 128 0
conv2_size 1 3
conv1_width 128 32
conv4_width 64 0
pool2_size 0 2
pool5_size 2 0
conv4_size 1 3
pool3_type AVE MAX
pool3_size 0 2
fc_affinity_func ELU ReLU
fc_pose_func ELU ReLU
fc_affinity_func2 ELU ReLU
fc_pose_func2 ELU ReLU
conv1_func ELU ReLU
conv2_func ELU ReLU
conv3_func ELU ReLU
conv1_norm BatchNorm none
conv1_norm LRN none
conv2_norm BatchNorm none
conv2_norm LRN none
conv3_norm BatchNorm none
conv3_norm LRN none
conv1_width 0 32
conv1_width 1 32
conv1_width 2 32
conv1_width 4 32
conv1_width 8 32
conv1_width 16 32
conv2_width 0 64
conv2_width 1 64
conv2_width 2 64
conv2_width 4 64
conv2_width 8 64
conv2_width 16 64
conv2_width 32 64
conv3_width 0 128
conv3_width 1 128
conv3_width 2 128
conv3_width 4 128
conv3_width 8 128
conv3_width 16 128
conv3_width 32 128
conv3_width 64 128
conv1_size 1 3
conv2_size 1 3
conv3_size 1 3
pool4_type AVE MAX
conv4_size 1 3
conv4_func ELU ReLU
conv4_norm BatchNorm none
conv4_norm LRN none
pool5_type AVE MAX
conv5_size 1 3
conv5_func ELU ReLU
conv5_norm BatchNorm none
conv5_norm LRN none
stratify_receptor 0 1
fc_affinity_init gaussian xavier
fc_affinity_init msra xavier
fc_affinity_init positive_unitball xavier
fc_affinity_init uniform xavier
fc_pose_init gaussian xavier
fc_pose_init msra xavier
fc_pose_init positive_unitball xavier
fc_pose_init uniform xavier
conv1_init gaussian xavier
conv1_init msra xavier
conv1_init positive_unitball xavier
conv1_init radial xavier
conv1_init radial.5 xavier
conv1_init uniform xavier
conv2_init gaussian xavier
conv2_init msra xavier
conv2_init positive_unitball xavier
conv2_init radial xavier
conv2_init radial.5 xavier
conv2_init uniform xavier
conv3_init gaussian xavier
conv3_init msra xavier
conv3_init positive_unitball xavier
conv3_init radial xavier
conv3_init radial.5 xavier
conv3_init uniform xavier
conv4_init gaussian xavier
conv4_init msra xavier
conv4_init positive_unitball xavier
conv4_init radial xavier
conv4_init radial.5 xavier
conv4_init uniform xavier
conv5_init gaussian xavier
conv5_init msra xavier
conv5_init positive_unitball xavier
conv5_init radial xavier
conv5_init radial.5 xavier
conv5_init uniform xavier
balanced 0 1
jitter 0.125 0.0
jitter 0.25 0.0
jitter 0.375 0.0
jitter 0.5 0.0
jitter 0.625 0.0
jitter 0.75 0.0
jitter 0.875 0.0
jitter 1.0 0.0
stratify_affinity_step 2 1
stratify_affinity_step 4 1
stratify_affinity 1 0
ranklossneg 1 0
ranklossmult 0.125 0
ranklossmult 0.25 0
ranklossmult 0.375 0
ranklossmult 0.5 0
ranklossmult 0.625 0
ranklossmult 0.75 0
ranklossmult 0.875 0
ranklossmult 1.0 0
resolution 1.0 0.5
conv5_stride 2 1
conv5_stride 3 1
conv5_stride 4 1
conv5_width 1 0
conv5_width 2 0
conv5_width 4 0
conv5_width 8 0
conv5_width 16 0
conv5_width 32 0
conv5_width 64 0
conv5_width 128 0
conv5_width 256 0
conv5_width 512 0
conv5_width 1024 0
conv5_func Sigmoid ReLU
conv5_func TanH ReLU
conv5_func leaky ReLU
conv5_size 5 3
conv5_size 7 3
pool5_size 2 0
pool5_size 4 0
pool5_size 8 0
conv4_stride 2 1
conv4_stride 3 1
conv4_stride 4 1
conv4_width 1 0
conv4_width 2 0
conv4_width 4 0
conv4_width 8 0
conv4_width 16 0
conv4_width 32 0
conv4_width 64 0
conv4_width 128 0
conv4_width 256 0
conv4_width 512 0
conv4_width 1024 0
conv4_func Sigmoid ReLU
conv4_func TanH ReLU
conv4_func leaky ReLU
conv4_size 5 3
conv4_size 7 3
pool4_size 2 0
pool4_size 4 0
pool4_size 8 0
conv3_stride 2 1
conv3_stride 3 1
conv3_stride 4 1
conv2_stride 2 1
conv2_stride 3 1
conv2_stride 4 1
conv1_stride 2 1
conv1_stride 3 1
conv1_stride 4 1
conv3_size 5 3
conv3_size 7 3
conv2_size 5 3
conv2_size 7 3
conv1_size 5 3
conv1_size 7 3
conv3_width 256 128
conv3_width 512 128
conv3_width 1024 128
conv2_width 128 64
conv2_width 256 64
conv2_width 512 64
conv2_width 1024 64
conv1_width 64 32
conv1_width 128 32
conv1_width 256 32
conv1_width 512 32
conv1_width 1024 32
conv3_func Sigmoid ReLU
conv3_func TanH ReLU
conv3_func leaky ReLU
conv2_func Sigmoid ReLU
conv2_func TanH ReLU
conv2_func leaky ReLU
conv1_func Sigmoid ReLU
conv1_func TanH ReLU
conv1_func leaky ReLU
fc_pose_func2 Sigmoid ReLU
fc_pose_func2 TanH ReLU
fc_pose_func2 leaky ReLU
fc_pose_hidden2 32 0
fc_pose_hidden2 64 0
fc_pose_hidden2 128 0
fc_pose_hidden2 256 0
fc_pose_hidden2 512 0
fc_pose_hidden2 1024 0
fc_pose_hidden2 2048 0
fc_pose_hidden2 4096 0
fc_affinity_func2 Sigmoid ReLU
fc_affinity_func2 TanH ReLU
fc_affinity_func2 leaky ReLU
fc_affinity_hidden2 32 0
fc_affinity_hidden2 64 0
fc_affinity_hidden2 128 0
fc_affinity_hidden2 256 0
fc_affinity_hidden2 512 0
fc_affinity_hidden2 1024 0
fc_affinity_hidden2 2048 0
fc_affinity_hidden2 4096 0
fc_pose_func Sigmoid ReLU
fc_pose_func TanH ReLU
fc_pose_func leaky ReLU
fc_pose_hidden 32 0
fc_pose_hidden 64 0
fc_pose_hidden 128 0
fc_pose_hidden 256 0
fc_pose_hidden 512 0
fc_pose_hidden 2048 0
fc_affinity_func Sigmoid ReLU
fc_affinity_func TanH ReLU
fc_affinity_func leaky ReLU
fc_affinity_hidden 32 0
fc_affinity_hidden 64 0
fc_affinity_hidden 128 0
fc_affinity_hidden 256 0
fc_affinity_hidden 512 0
fc_affinity_hidden 1024 0
pool3_size 4 2
pool3_size 8 2
pool2_size 4 2
pool2_size 8 2
fc_pose_init msra xavier
fc_pose_hidden 32 0
conv2_init radial.5 xavier
pool2_size 8 2
conv3_init radial.5 xavier
fc_affinity_init msra xavier
pool3_size 8 2
fc_pose_func leaky ReLU
fc_affinity_hidden 32 0
conv5_init radial.5 xavier
conv1_init radial.5 xavier
conv4_init radial.5 xavier
pool2_type AVE MAX
pool2_size 0 2
pool1_size 4 2
pool3_size 0 2
pool2_type AVE MAX
pool2_size 8 2
pool1_size 4 2
pool3_size 8 2
pool3_type AVE MAX
fc_affinity_hidden 2048 0
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 4 2
pool3_size 8 2
pool3_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 4 2
pool3_size 8 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
pool2_size 0 2
pool1_size 4 2
pool3_size 8 2
pool1_size 4 2
pool2_size 8 2
pool1_size 4 2
pool3_size 0 2
fc_affinity_hidden 4096 0
pool2_size 8 2
pool1_size 4 2
fc_affinity_hidden 64 0
pool2_size 8 2
pool1_size 4 2
pool3_size 8 2
pool2_type AVE MAX
pool2_size 0 2
fc_affinity_func Sigmoid ReLU
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool2_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool2_type AVE MAX
pool2_size 0 2
pool1_size 8 2
pool2_type AVE MAX
pool2_size 0 2
pool1_size 8 2
pool3_size 8 2
pool2_type AVE MAX
pool2_size 4 2
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool2_type AVE MAX
pool2_size 8 2
pool1_size 8 2
pool3_size 0 2
pool3_type AVE MAX
fc_affinity_hidden 4096 0
pool2_type AVE MAX
pool2_size 8 2
pool1_size 8 2
pool3_type AVE MAX
pool2_type AVE MAX
pool2_size 8 2
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool2_type AVE MAX
pool2_size 8 2
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
fc_affinity_hidden 32 0
pool2_type AVE MAX
pool2_size 8 2
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
fc_affinity_hidden 4096 0
pool2_size 0 2
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool2_size 0 2
fc_affinity_func Sigmoid ReLU
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 8 2
pool3_size 8 2
fc_pose_func Sigmoid ReLU
pool3_type AVE MAX
pool2_size 0 2
fc_affinity_func TanH ReLU
pool1_size 8 2
pool3_size 8 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
pool2_size 0 2
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
fc_affinity_hidden 4096 0
pool2_size 0 2
pool1_size 8 2
pool3_size 0 2
pool2_size 0 2
pool1_size 8 2
pool3_size 4 2
pool2_size 0 2
pool1_size 8 2
pool3_size 8 2
pool2_size 0 2
pool1_size 8 2
pool3_size 8 2
fc_affinity_hidden 32 0
pool2_size 0 2
pool1_size 8 2
pool3_size 8 2
fc_affinity_hidden 64 0
pool1_size 8 2
pool3_size 4 2
pool3_type AVE MAX
fc_affinity_hidden 32 0
pool1_size 8 2
pool2_size 4 2
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool2_size 4 2
pool1_size 8 2
pool3_size 0 2
pool2_size 4 2
pool1_size 8 2
pool3_size 8 2
pool2_size 8 2
pool1_size 8 2
pool3_size 0 2
pool3_type AVE MAX
pool2_size 8 2
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
pool2_size 8 2
pool1_size 8 2
pool3_size 8 2
pool3_type AVE MAX
fc_affinity_hidden 4096 0
pool2_size 8 2
pool1_size 8 2
pool3_size 0 2
pool2_size 8 2
pool1_size 8 2
pool2_size 8 2
pool1_size 8 2
fc_affinity_hidden 2048 0
pool2_size 8 2
pool1_size 8 2
fc_affinity_hidden 4096 0
pool2_size 8 2
pool1_size 8 2
pool3_size 4 2
fc_affinity_hidden 4096 0
pool2_size 8 2
pool1_size 8 2
pool3_size 8 2
pool2_size 8 2
pool1_size 8 2
pool3_size 8 2
fc_affinity_hidden 32 0
pool2_size 8 2
pool1_size 8 2
pool3_size 8 2
fc_affinity_hidden 2048 0
loss_delta 5.0 4
loss_delta 6.0 4
loss_delta 7.0 4
loss_delta 8.0 4
loss_penalty 0.625 0
loss_penalty 1.25 0
pool2_size 0 2
conv3_width 512 128
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
pool5_size 2 0
conv1_init uniform xavier
conv3_size 7 3
loss_penalty 1.25 0
fc_pose_hidden 64 0
pool2_size 0 2
conv3_width 2 128
pool1_size 0 2
pool3_size 8 2
conv3_func leaky ReLU
pool3_type AVE MAX
pool5_size 4 0
conv1_init uniform xavier
loss_penalty 1.25 0
loss_penalty 1.875 0
loss_penalty 2.5 0
loss_penalty 3.125 0
loss_penalty 3.75 0
jitter 1.0 0.0
loss_penalty 4.375 0
pool2_size 0 2
stratify_affinity 1 0
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
loss_penalty 4.375 0
loss_penalty 5.0 0
loss_gap 0.625 0
loss_gap 1.25 0
loss_gap 1.875 0
loss_gap 2.5 0
conv2_size 5 3
loss_gap 3.125 0
conv3_init positive_unitball xavier
pool1_size 0 2
pool3_type AVE MAX
loss_gap 3.125 0
loss_gap 3.75 0
loss_gap 4.375 0
loss_gap 5.0 0
momentum 1.0 0.9
weight_decay_exp -2.5 -3
weight_decay_exp -1.25 -3
weight_decay_exp 0.0 -3
weight_decay_exp -6.97964775908 -3
base_lr_exp -1.95646540462 -2
loss_gap 0.72009146284 0
weight_decay_exp 0.0 -3
solver Adam SGD
base_lr_exp -1.94683239407 -2
momentum 0.0 0.9
pool2_size 0 2
pool1_size 0 2
pool3_size 8 2
weight_decay_exp -10.0 -3
conv1_func TanH ReLU
pool3_type AVE MAX
pool5_size 4 0
base_lr_exp -1.875 -2
conv1_init uniform xavier
jitter 0.625 0.0
pool4_size 8 0
conv2_size 1 3
fc_pose_hidden 64 0
conv3_init positive_unitball xavier
pool1_size 0 2
fc_pose_func TanH ReLU
pool3_type AVE MAX
base_lr_exp -1.875 -2
momentum 0.875 0.9
conv3_size 5 3
pool2_size 0 2
pool1_size 0 2
pool3_size 8 2
conv3_func ELU ReLU
pool3_type AVE MAX
pool5_size 4 0
base_lr_exp -1.875 -2
conv1_init uniform xavier
conv3_size 5 3
pool2_size 0 2
pool1_size 0 2
pool3_size 8 2
conv3_func ELU ReLU
pool3_type AVE MAX
pool5_size 4 0
base_lr_exp -1.875 -2
conv3_size 5 3
pool2_size 0 2
stratify_affinity 1 0
pool1_size 0 2
pool3_size 8 2
conv3_func ELU ReLU
pool3_type AVE MAX
pool5_size 4 0
base_lr_exp -1.875 -2
conv1_init uniform xavier
base_lr_exp -1.875 -2
loss_penalty 1.25 0
fc_pose_hidden 64 0
pool2_size 0 2
fc_affinity_init gaussian xavier
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
pool5_size 2 0
base_lr_exp -1.875 -2
conv1_init uniform xavier
conv3_size 5 3
loss_penalty 1.875 0
pool2_size 0 2
pool1_size 0 2
pool3_size 8 2
conv3_func ELU ReLU
pool3_type AVE MAX
base_lr_exp -1.875 -2
loss_penalty 2.5 0
fc_pose_hidden 64 0
conv4_width 1 0
pool2_size 0 2
pool1_size 0 2
pool3_size 8 2
pool3_type AVE MAX
pool5_size 2 0
base_lr_exp -1.875 -2
loss_pseudohuber 0 1
loss_penalty 5.0 0
weight_decay_exp -7.58709075011 -3
base_lr_exp -1.84011526765 -2
momentum 0.155964279603 0.9
weight_decay_exp -4.38813331861 -3
base_lr_exp -1.7776577702 -2
weight_decay_exp -10.0 -3
base_lr_exp -1.7307027469 -2
weight_decay_exp -8.39070918157 -3
base_lr_exp -1.67827173213 -2
weight_decay_exp 0.0 -3
base_lr_exp -1.59086587788 -2
momentum 0.427867837955 0.9
loss_gap 5.0 0
weight_decay_exp -7.0859246647 -3
base_lr_exp -1.46484641083 -2
momentum 0.171878822149 0.9
weight_decay_exp -10.0 -3
base_lr_exp -1.42115692385 -2
momentum 0.032926426816 0.9
loss_penalty 5.0 0
loss_gap 5.0 0
weight_decay_exp -10.0 -3
solver Adam SGD
base_lr_exp -1.41656835909 -2
momentum 1.0 0.9
weight_decay_exp -6.09532770235 -3
base_lr_exp -1.41499099062 -2
weight_decay_exp -6.51638521857 -3
base_lr_exp -1.34645744759 -2
momentum 0.0 0.9
conv4_norm BatchNorm none
jitter 0.0765686 0.0
conv5_width 256 0
conv2_width 1024 64
pool4_size 2 0
conv1_stride 4 1
conv3_size 1 3
balanced 0 1
stratify_affinity_step 4 1
fc_pose_hidden2 512 0
conv1_norm BatchNorm none
conv2_size 7 3
loss_penalty 4.51217651367 0
fc_pose_init uniform xavier
conv1_size 7 3
loss_gap 2.72903442383 0
conv2_init msra xavier
conv4_width 2 0
pool2_size 4 2
conv3_init positive_unitball xavier
fc_affinity_func ELU ReLU
stratify_affinity 1 0
conv3_width 0 128
conv4_stride 4 1
pool1_size 0 2
loss_delta 2.86450195312 4
pool3_size 8 2
weight_decay_exp -4.48333740234 -3
fc_pose_func2 leaky ReLU
conv1_func leaky ReLU
conv2_norm BatchNorm none
ranklossmult 0.0800476074219 0
conv5_func leaky ReLU
conv3_func TanH ReLU
fc_affinity_hidden 256 0
conv5_init radial.5 xavier
conv3_stride 2 1
fc_affinity_func2 leaky ReLU
fc_affinity_hidden2 512 0
conv5_stride 4 1
base_lr_exp -1.27944946289 -2
conv4_size 7 3
momentum 0.918121337891 0.9
conv1_init gaussian xavier
conv4_func leaky ReLU
conv2_func leaky ReLU
resolution 1.0 0.5
conv4_init msra xavier
base_lr_exp -1.25 -2
weight_decay_exp -1.77678156758 -3
base_lr_exp -1.15983714183 -2
base_lr_exp -0.625 -2
jitter 0.294617 0.0
conv5_width 2 0
conv2_width 1024 64
pool4_size 8 0
conv1_stride 2 1
conv5_norm LRN none
pool4_type AVE MAX
ranklossneg 1 0
stratify_affinity_step 2 1
fc_pose_hidden2 32 0
pool2_type AVE MAX
loss_penalty 3.56109619141 0
fc_pose_init msra xavier
conv1_size 1 3
conv2_stride 3 1
fc_pose_hidden 128 0
loss_gap 2.35931396484 0
conv1_width 512 32
conv2_init radial.5 xavier
pool1_type AVE MAX
conv4_width 1024 0
conv3_init positive_unitball xavier
fc_affinity_func TanH ReLU
stratify_affinity 1 0
conv3_width 0 128
fc_affinity_init gaussian xavier
conv4_stride 3 1
pool1_size 4 2
loss_delta 5.90869140625 4
pool3_size 0 2
weight_decay_exp -6.23596191406 -3
fc_pose_func2 TanH ReLU
fc_pose_func leaky ReLU
ranklossmult 0.109680175781 0
conv5_func leaky ReLU
conv3_func Sigmoid ReLU
pool5_size 2 0
fc_affinity_hidden 32 0
conv5_init gaussian xavier
conv3_stride 2 1
fc_affinity_func2 ELU ReLU
fc_affinity_hidden2 32 0
conv5_stride 2 1
base_lr_exp -0.304870605469 -2
conv4_size 7 3
momentum 0.640808105469 0.9
conv1_init radial.5 xavier
conv2_func ELU ReLU
resolution 1.0 0.5
conv4_init gaussian xavier
loss_gap 5.0 0
weight_decay_exp -10.0 -3
base_lr_exp 0.0 -2
momentum 0.0 0.9
weight_decay_exp -10.0 -3
solver Adam SGD
base_lr_exp 0.0 -2
momentum 0.348806818803 0.9
weight_decay_exp -8.16877227051 -3
base_lr_exp 0.0 -2
weight_decay_exp -3.67372761121 -3
base_lr_exp 0.0 -2
base_lr_exp 0.0 -2
weight_decay_exp -0.0601919697183 -3
base_lr_exp 0.0 -2
weight_decay_exp 0.0 -3
base_lr_exp 0.0 -2
momentum 0.0 0.9
weight_decay_exp 0.0 -3
solver Adam SGD
base_lr_exp 0.0 -2
momentum 1.11022302463e-16 0.9
loss_gap 4.94228499605 0
weight_decay_exp 0.0 -3
base_lr_exp 0.0 -2
momentum 0.525314150012 0.9
In [410]:
len(goodrows)
Out[410]:
294
In [411]:
singleres = res.iloc[goodrows].copy()
In [412]:
singleres['catname'] = catnames
In [413]:
singleres['catval'] = catvals
In [514]:
c = sns.color_palette()
sns.set_context("notebook", font_scale=1.0)
for (i,n) in sorted(enumerate(params), key=lambda (i,n): n):
fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(12,4))
for (ax,name) in [(ax1,'Rtop'),(ax2,'R'),(ax3,'top')]:
slice = singleres[(singleres.catname == n) | (singleres.catname == 'default')]
mean = '%s_mean'%name
std = '%s_std'%name
ax.errorbar(slice[n],slice[mean],yerr=slice[std],fmt='o',color=c[i%len(c)])
dave = float(singleres[singleres.catname=='default'][mean])
dstd = float(singleres[singleres.catname=='default'][std])
ax.axhline(dave,color='grey')
ax.axhline(dave+dstd,color='grey',linewidth=.5)
ax.axhline(dave-dstd,color='grey',linewidth=.5)
ax.set_title(name)
plt.suptitle(n)
In [44]:
singleres[(singleres.catname == 'conv1_width') | (singleres.catname == 'default')].loc[:,['R_mean','top_mean','conv1_width']]
Out[44]:
R_mean
top_mean
conv1_width
172
0.000000
0.000000
0
173
0.403063
0.422847
1
174
0.478140
0.502640
2
175
0.499709
0.582038
4
176
0.537189
0.675401
8
177
0.546951
0.712213
16
246
0.537388
0.729756
32
334
0.520902
0.739650
64
335
0.512824
0.746163
128
336
0.526540
0.756723
256
337
0.517271
0.758451
512
338
0.525962
0.758352
1024
In [46]:
singleres[(singleres.catname == 'conv2_width') | (singleres.catname == 'default')].loc[:,['R_mean','top_mean','conv2_width']]
Out[46]:
R_mean
top_mean
conv2_width
178
0.000000
0.000000
0
179
0.214316
0.548483
1
180
0.503941
0.699433
2
181
0.517907
0.703479
4
182
0.526116
0.699136
8
183
0.523457
0.707427
16
184
0.529946
0.723415
32
246
0.537388
0.729756
64
330
0.539784
0.736541
128
331
0.535517
0.738860
256
332
0.524145
0.749568
512
333
0.521092
0.754108
1024
In [47]:
singleres[(singleres.catname == 'conv3_width') | (singleres.catname == 'default')].loc[:,['R_mean','top_mean','conv3_width']]
Out[47]:
R_mean
top_mean
conv3_width
185
0.536181
0.727659
0
186
0.361051
0.647520
1
187
0.537415
0.731211
2
188
0.541678
0.735208
4
189
0.535782
0.737528
8
190
0.548916
0.734616
16
191
0.535456
0.734271
32
192
0.525103
0.727757
64
246
0.537388
0.729756
128
327
0.528732
0.725981
256
328
0.544989
0.735505
512
329
0.543082
0.731261
1024
In [509]:
c = sns.color_palette()
sns.set_context("notebook", font_scale=1.5)
s = singleres.sort_values(by='Rtop_mean')[:-40:-1]
plt.figure(figsize=(16,4))
plt.errorbar(xrange(len(s)),s.Rtop_mean,yerr=s.Rtop_std,fmt='o',color=c[i%len(c)])
plt.xticks(xrange(len(s)),map(str,zip(s.catname,s.catval)),rotation='vertical');
ax = plt.gca()
dave = float(singleres[singleres.catname=='default'].Rtop_mean)
dstd = float(singleres[singleres.catname=='default'].Rtop_std)
ax.axhline(dave,color='grey')
ax.axhline(dave-dstd,color='grey',linewidth=0.5)
ax.axhline(dave+dstd,color='grey',linewidth=0.5);
defaultave = dave
defaultstd = dstd
plt.ylabel('Rtop');
In [416]:
singleres[singleres.catname == 'default']
Out[416]:
rmse_mean
top_mean
R_mean
auc_mean
Rtop_mean
rmse_std
top_std
R_std
auc_std
Rtop_std
serial_min
serial_max
conv4_norm
loss_pseudohuber
jitter
conv5_width
conv2_width
pool4_size
conv1_stride
conv5_norm
conv3_size
pool4_type
balanced
ranklossneg
stratify_affinity_step
fc_pose_hidden2
conv1_norm
conv3_norm
conv2_size
pool2_type
loss_penalty
fc_pose_init
conv1_size
conv2_stride
fc_pose_hidden
loss_gap
conv1_width
conv2_init
pool1_type
conv4_width
pool2_size
conv3_init
fc_affinity_func
stratify_affinity
conv3_width
fc_affinity_init
conv5_size
conv4_stride
pool1_size
loss_delta
pool3_size
weight_decay_exp
fc_pose_func2
fc_pose_func
conv1_func
conv2_norm
ranklossmult
conv5_func
conv3_func
pool3_type
pool5_size
fc_affinity_hidden
conv5_init
conv3_stride
fc_affinity_func2
solver
fc_affinity_hidden2
conv5_stride
base_lr_exp
stratify_receptor
conv4_size
momentum
conv1_init
conv4_func
pool5_type
conv2_func
resolution
conv4_init
catname
catval
246
1.733727
0.729756
0.537388
0.896221
0.392266
0.031688
0.012827
0.018652
0.002652
0.017951
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
0
none
none
3
MAX
0.0
xavier
3
1
0
0.0
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
2
4.0
2
-3.0
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
SGD
0
1
-2.0
1
3
0.9
xavier
ReLU
MAX
ReLU
0.5
xavier
default
default
In [507]:
for metric in ['R','top','Rtop','auc']:
mean = '%s_mean'%metric
std = '%s_std'%metric
s = singleres.sort_values(by=mean)[-40:]
plt.figure(figsize=(8,12))
plt.errorbar(s[mean],xrange(len(s)),xerr=s[std],fmt='o')
plt.yticks(xrange(len(s)),map(str,zip(s.catname,s.catval)));
ax = plt.gca()
dave = float(singleres[singleres.catname=='default'][mean])
dstd = float(singleres[singleres.catname=='default'][std])
ax.axvline(dave,color='grey')
ax.axvline(dave-dstd,color='grey',linewidth=0.5)
ax.axvline(dave+dstd,color='grey',linewidth=0.5);
plt.xlim(dave-dstd,max(s[mean])+dstd)
plt.title(metric)
In [418]:
s = singleres.sort_values(by='top_mean')[-60:]
plt.figure(figsize=(8,16))
plt.errorbar(s.top_mean,xrange(len(s)),xerr=s.top_std,fmt='o')
plt.yticks(xrange(len(s)),map(str,zip(s.catname,s.catval)));
ax = plt.gca()
dave = float(singleres[singleres.catname=='default'].top_mean)
dstd = float(singleres[singleres.catname=='default'].top_std)
ax.axvline(dave,color='grey')
ax.axvline(dave-dstd,color='grey',linewidth=0.5)
ax.axvline(dave+dstd,color='grey',linewidth=0.5);
In [520]:
sns.set_context("notebook", font_scale=1.5)
plt.figure(figsize=(8,8))
plt.scatter(res.R_mean,res.top_mean,label="All")
plt.scatter(singleres.R_mean,singleres.top_mean,marker='+',linewidth=1,label='Single Param Scan')
plt.gca().set_aspect('equal', 'box')
plt.xlabel('R')
plt.ylabel('top')
plt.legend();
In [420]:
plt.plot(res.sort_values(by='Rtop_mean').Rtop_mean.values,'o')
plt.plot(singleres.sort_values(by='Rtop_mean').Rtop_mean.values,'o')
Out[420]:
[<matplotlib.lines.Line2D at 0x7f73b3b3d050>]
In [421]:
def diffonly(top):
'''return dataframe with only differing columns'''
nunique = top.apply(pd.Series.nunique)
cols_to_drop = nunique[nunique == 1].index
return top.drop(cols_to_drop, axis=1)
In [422]:
diffonly(res.sort_values(by='Rtop_mean',ascending=False)[:10])
Out[422]:
rmse_mean
top_mean
R_mean
auc_mean
Rtop_mean
rmse_std
top_std
R_std
auc_std
Rtop_std
serial_min
serial_max
conv3_size
pool2_type
loss_penalty
fc_pose_hidden
pool1_type
pool2_size
conv3_init
fc_affinity_func
stratify_affinity
fc_affinity_init
pool3_size
fc_pose_func
conv3_func
pool3_type
pool5_size
base_lr_exp
conv1_init
473
1.736949
0.735258
0.583137
0.896338
0.428845
0.050272
0.007204
0.023445
0.003224
0.020174
NaN
NaN
5
MAX
0.00
0
MAX
0
xavier
ReLU
1
xavier
8
ReLU
ELU
AVE
4
-1.875
uniform
471
1.671352
0.749914
0.568846
0.897157
0.426644
0.036630
0.007837
0.020083
0.006877
0.017596
NaN
NaN
5
MAX
0.00
0
MAX
0
xavier
ReLU
0
xavier
8
ReLU
ELU
AVE
4
-1.875
uniform
131
1.716524
0.778090
0.547251
0.907292
0.425772
0.023136
0.007475
0.013910
0.002910
0.009618
NaN
NaN
3
MAX
0.00
64
MAX
2
positive_unitball
ReLU
0
msra
2
TanH
ReLU
AVE
0
-2.000
xavier
97
1.678214
0.760523
0.557887
0.886491
0.424225
0.021201
0.010716
0.010884
0.007677
0.006910
NaN
NaN
3
MAX
0.00
0
AVE
0
xavier
TanH
0
xavier
8
TanH
ReLU
AVE
0
-2.000
xavier
117
1.674817
0.754330
0.560787
0.883020
0.423022
0.024953
0.010179
0.015895
0.006425
0.013321
NaN
NaN
3
AVE
0.00
0
MAX
0
xavier
TanH
0
xavier
8
ReLU
ReLU
AVE
0
-2.000
xavier
95
1.683226
0.759043
0.556986
0.886191
0.422857
0.015060
0.010665
0.012257
0.007978
0.014503
NaN
NaN
3
MAX
0.00
0
AVE
0
xavier
ReLU
0
xavier
8
ReLU
ReLU
AVE
0
-2.000
xavier
141
1.731896
0.787269
0.536598
0.911558
0.422455
0.010725
0.002810
0.008542
0.001973
0.007547
NaN
NaN
3
MAX
0.00
0
MAX
2
xavier
ReLU
0
xavier
2
ReLU
ReLU
MAX
0
-2.000
xavier
472
1.668170
0.744486
0.566883
0.896241
0.422126
0.037831
0.009601
0.024204
0.006467
0.021209
NaN
NaN
5
MAX
0.00
0
MAX
0
xavier
ReLU
0
xavier
8
ReLU
ELU
AVE
4
-1.875
xavier
475
1.701260
0.765852
0.551032
0.895282
0.422035
0.019890
0.007757
0.013622
0.003375
0.012515
NaN
NaN
3
MAX
1.25
64
MAX
0
xavier
ReLU
0
gaussian
8
ReLU
ReLU
AVE
2
-1.875
uniform
93
1.683542
0.757069
0.556831
0.885394
0.421616
0.026848
0.006643
0.014939
0.002247
0.014200
NaN
NaN
3
AVE
0.00
0
AVE
0
xavier
TanH
0
xavier
8
ReLU
ReLU
AVE
0
-2.000
xavier
In [423]:
diffonly(singleres.sort_values(by='Rtop_mean',ascending=False)[:10])
Out[423]:
rmse_mean
top_mean
R_mean
auc_mean
Rtop_mean
rmse_std
top_std
R_std
auc_std
Rtop_std
serial_min
serial_max
conv4_norm
conv3_size
conv2_size
conv1_width
conv4_width
pool2_size
conv3_width
pool1_size
conv2_func
catname
catval
141
1.731896
0.787269
0.536598
0.911558
0.422455
0.010725
0.002810
0.008542
0.001973
0.007547
NaN
NaN
none
3
3
32
0
2
128
0
ReLU
pool1_size
0
194
1.698088
0.735998
0.548055
0.896918
0.403420
0.025881
0.005011
0.015422
0.003201
0.013764
NaN
NaN
none
3
1
32
0
2
128
2
ReLU
conv2_size
1
190
1.713857
0.734616
0.548916
0.897160
0.403321
0.024592
0.010425
0.016101
0.005118
0.015909
NaN
NaN
none
3
3
32
0
2
16
2
ReLU
conv3_width
16
155
1.772086
0.770244
0.522311
0.905518
0.402338
0.030534
0.007994
0.016625
0.005634
0.014612
NaN
NaN
none
3
3
32
0
0
128
2
ReLU
pool2_size
0
199
1.723322
0.736837
0.544301
0.898652
0.401118
0.035350
0.007968
0.015593
0.003403
0.014438
NaN
NaN
BatchNorm
3
3
32
0
2
128
2
ReLU
conv4_norm
BatchNorm
328
1.716913
0.735505
0.544989
0.899003
0.400900
0.028426
0.007849
0.015009
0.003493
0.014085
NaN
NaN
none
3
3
32
0
2
512
2
ReLU
conv3_width
512
322
1.724854
0.731508
0.547485
0.897694
0.400527
0.018313
0.006596
0.008822
0.003598
0.009582
NaN
NaN
none
7
3
32
0
2
128
2
ReLU
conv3_size
7
294
1.721422
0.730175
0.547318
0.899351
0.399787
0.035849
0.011826
0.020282
0.004662
0.020502
NaN
NaN
none
3
3
32
2
2
128
2
ReLU
conv4_width
2
164
1.721007
0.737774
0.540970
0.895927
0.399112
0.018988
0.004534
0.008516
0.002870
0.006612
NaN
NaN
none
3
3
32
0
2
128
2
ELU
conv2_func
ELU
338
1.771888
0.758352
0.525962
0.909481
0.398938
0.031937
0.010744
0.018892
0.002219
0.017666
NaN
NaN
none
3
3
1024
0
2
128
2
ReLU
conv1_width
1024
In [ ]:
In [ ]:
In [424]:
singleres[singleres.catname == 'default']
Out[424]:
rmse_mean
top_mean
R_mean
auc_mean
Rtop_mean
rmse_std
top_std
R_std
auc_std
Rtop_std
serial_min
serial_max
conv4_norm
loss_pseudohuber
jitter
conv5_width
conv2_width
pool4_size
conv1_stride
conv5_norm
conv3_size
pool4_type
balanced
ranklossneg
stratify_affinity_step
fc_pose_hidden2
conv1_norm
conv3_norm
conv2_size
pool2_type
loss_penalty
fc_pose_init
conv1_size
conv2_stride
fc_pose_hidden
loss_gap
conv1_width
conv2_init
pool1_type
conv4_width
pool2_size
conv3_init
fc_affinity_func
stratify_affinity
conv3_width
fc_affinity_init
conv5_size
conv4_stride
pool1_size
loss_delta
pool3_size
weight_decay_exp
fc_pose_func2
fc_pose_func
conv1_func
conv2_norm
ranklossmult
conv5_func
conv3_func
pool3_type
pool5_size
fc_affinity_hidden
conv5_init
conv3_stride
fc_affinity_func2
solver
fc_affinity_hidden2
conv5_stride
base_lr_exp
stratify_receptor
conv4_size
momentum
conv1_init
conv4_func
pool5_type
conv2_func
resolution
conv4_init
catname
catval
246
1.733727
0.729756
0.537388
0.896221
0.392266
0.031688
0.012827
0.018652
0.002652
0.017951
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
0
none
none
3
MAX
0.0
xavier
3
1
0
0.0
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
2
4.0
2
-3.0
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
SGD
0
1
-2.0
1
3
0.9
xavier
ReLU
MAX
ReLU
0.5
xavier
default
default
In [425]:
import MySQLdb
from MySQLdb.cursors import DictCursor
def getcursor(host,passwd,db):
'''create a connection and return a cursor;
doing this guards against dropped connections'''
conn = MySQLdb.connect (host = host,user = "opter",passwd=passwd,db=db)
conn.autocommit(True)
cursor = conn.cursor(DictCursor)
return cursor
cursor = getcursor('35.196.158.205','optimize','opt2')
cursor.execute('SELECT * FROM params WHERE id != "REQUESTED"')
rows = cursor.fetchall()
data = pd.DataFrame(list(rows))
#make errors zero - appropriate if error is due to parameters
data.loc[data.id == 'ERROR','R'] = 0
data.loc[data.id == 'ERROR','rmse'] = 0
data.loc[data.id == 'ERROR','top'] = 0
data.loc[data.id == 'ERROR','auc'] = 0
data['Rtop'] = data.R*data.top
data = data.dropna('index').apply(pd.to_numeric, errors='ignore')
In [470]:
defaultave
Out[470]:
0.3922661567182789
In [487]:
plt.figure(figsize=(9,2))
plt.plot(data.Rtop,'o-',label=None)
plt.ylabel("Rtop")
plt.gca().axhline(np.max(data.Rtop),color='orange',label='best')
plt.gca().axhline(defaultave,color='black',linewidth=1,label='default')
plt.xlim(0,3700)
plt.legend(loc='lower right',ncol=3);
plt.savefig("rtopall.pdf",bbox_inches='tight')
In [502]:
import datetime
datetime.datetime.fromtimestamp(data.time[3500]/1e9)
Out[502]:
datetime.datetime(2018, 7, 5, 12, 27, 30)
In [493]:
data.time[0]/1e9
Out[493]:
1527776497.0
In [467]:
plt.figure(figsize=(18,4))
plt.plot(data.serial,data.time,'o-')
#plt.xlim(0,3700)
Out[467]:
[<matplotlib.lines.Line2D at 0x7f73aa2103d0>]
In [458]:
plt.figure(figsize=(18,4))
plt.plot(data.top,c='darkblue')
plt.gca().axhline(np.max(data.top),color='grey')
plt.xlim(0,3700)
plt.title("top");
In [459]:
plt.figure(figsize=(18,4))
plt.plot(data.R,c='maroon')
plt.gca().axhline(np.max(data.R),color='grey')
plt.xlim(0,3700)
plt.title("R")
Out[459]:
Text(0.5,1,u'R')
In [429]:
plt.figure(figsize=(18,4))
plt.plot(data.Rtop,label="Rtop")
plt.plot(data.R,label="R")
plt.plot(data.top,label="top")
plt.legend()
plt.xlim(3500,3700);
In [461]:
plt.figure(figsize=(18,4))
plt.plot(list(data.time))
plt.xlim(0,3700)
Out[461]:
(0, 3700)
In [431]:
import time, datetime
datetime.datetime.fromtimestamp(data.time[3400]/1000000000)
Out[431]:
datetime.datetime(2018, 6, 16, 19, 26, 1)
In [ ]:
In [432]:
pd.set_option('display.max_rows', None)
data.sort_values(by='Rtop',ascending=False).iloc[0]
Out[432]:
R 0.576376
auc 0.906201
balanced 1
base_lr_exp -2
conv1_func ReLU
conv1_init xavier
conv1_norm none
conv1_size 5
conv1_stride 1
conv1_width 64
conv2_func ReLU
conv2_init xavier
conv2_norm LRN
conv2_size 3
conv2_stride 1
conv2_width 64
conv3_func ReLU
conv3_init positive_unitball
conv3_norm none
conv3_size 3
conv3_stride 1
conv3_width 128
conv4_func ReLU
conv4_init xavier
conv4_norm none
conv4_size 3
conv4_stride 1
conv4_width 0
conv5_func ReLU
conv5_init xavier
conv5_norm none
conv5_size 3
conv5_stride 4
conv5_width 0
fc_affinity_func ELU
fc_affinity_func2 ReLU
fc_affinity_hidden 0
fc_affinity_hidden2 0
fc_affinity_init xavier
fc_pose_func ELU
fc_pose_func2 ReLU
fc_pose_hidden 0
fc_pose_hidden2 0
fc_pose_init xavier
id ./clients-1z30-Fh_OIC
jitter 0
loss_delta 4
loss_gap 0
loss_penalty 0
loss_pseudohuber 1
momentum 0.9
msg SUCCESS
pool1_size 0
pool1_type MAX
pool2_size 0
pool2_type MAX
pool3_size 8
pool3_type AVE
pool4_size 0
pool4_type MAX
pool5_size 0
pool5_type MAX
ranklossmult 0
ranklossneg 0
resolution 0.5
rmse 1.65472
seed 95698
serial 3553
solver SGD
split 2
stratify_affinity 0
stratify_affinity_step 1
stratify_receptor 1
time 1530932059000000000
top 0.790279
weight_decay_exp -3
Rtop 0.455498
Name: 3552, dtype: object
In [433]:
import deepdiff
modeldefaults = makemodel.getdefaults()
def cleanparams(p):
'''standardize params that do not matter'''
for i in xrange(1,6):
if p['conv%d_width'%i] == 0:
for suffix in ['func', 'init', 'norm', 'size', 'stride', 'width']:
name = 'conv%d_%s'%(i,suffix)
p[name] = modeldefaults[name]
if p['pool%d_size'%i] == 0:
name = 'pool%d_type'%i
p[name] = modeldefaults[name]
if p['fc_pose_hidden'] == 0:
p['fc_pose_func'] = modeldefaults['fc_pose_func']
p['fc_pose_hidden2'] = modeldefaults['fc_pose_hidden2']
p['fc_pose_func2'] = modeldefaults['fc_pose_func2']
p['fc_pose_init'] = modeldefaults['fc_pose_init']
elif p['fc_pose_hidden2'] == 0:
p['fc_pose_hidden2'] = modeldefaults['fc_pose_hidden2']
p['fc_pose_func2'] = modeldefaults['fc_pose_func2']
if p['fc_affinity_hidden'] == 0:
p['fc_affinity_func'] = modeldefaults['fc_affinity_func']
p['fc_affinity_hidden2'] = modeldefaults['fc_affinity_hidden2']
p['fc_affinity_func2'] = modeldefaults['fc_affinity_func2']
p['fc_affinity_init'] = modeldefaults['fc_affinity_init']
elif p['fc_affinity_hidden2'] == 0:
p['fc_affinity_hidden2'] = modeldefaults['fc_affinity_hidden2']
p['fc_affinity_func2'] = modeldefaults['fc_affinity_func2']
for (name,val) in p.iteritems():
if 'item' in dir(val):
p[name] = np.asscalar(val)
if type(p[name]) == int:
p[name] = float(p[name])
return p
In [434]:
notparams = ['R','auc','id','msg','rmse','seed','serial','time','top']
best = cleanparams(data.drop(notparams,axis=1).sort_values(by='Rtop',ascending=False).iloc[0].to_dict())
In [435]:
res.sort_values(by='Rtop_mean',ascending=False).iloc[:10]
Out[435]:
rmse_mean
top_mean
R_mean
auc_mean
Rtop_mean
rmse_std
top_std
R_std
auc_std
Rtop_std
serial_min
serial_max
conv4_norm
loss_pseudohuber
jitter
conv5_width
conv2_width
pool4_size
conv1_stride
conv5_norm
conv3_size
pool4_type
balanced
ranklossneg
stratify_affinity_step
fc_pose_hidden2
conv1_norm
conv3_norm
conv2_size
pool2_type
loss_penalty
fc_pose_init
conv1_size
conv2_stride
fc_pose_hidden
loss_gap
conv1_width
conv2_init
pool1_type
conv4_width
pool2_size
conv3_init
fc_affinity_func
stratify_affinity
conv3_width
fc_affinity_init
conv5_size
conv4_stride
pool1_size
loss_delta
pool3_size
weight_decay_exp
fc_pose_func2
fc_pose_func
conv1_func
conv2_norm
ranklossmult
conv5_func
conv3_func
pool3_type
pool5_size
fc_affinity_hidden
conv5_init
conv3_stride
fc_affinity_func2
solver
fc_affinity_hidden2
conv5_stride
base_lr_exp
stratify_receptor
conv4_size
momentum
conv1_init
conv4_func
pool5_type
conv2_func
resolution
conv4_init
473
1.736949
0.735258
0.583137
0.896338
0.428845
0.050272
0.007204
0.023445
0.003224
0.020174
NaN
NaN
none
1
0.0
0
64
0
1
none
5
MAX
1
0
1
0
none
none
3
MAX
0.00
xavier
3
1
0
0.0
32
xavier
MAX
0
0
xavier
ReLU
1
128
xavier
3
1
0
4.0
8
-3.0
ReLU
ReLU
ReLU
none
0.0
ReLU
ELU
AVE
4
0
xavier
1
ReLU
SGD
0
1
-1.875
1
3
0.9
uniform
ReLU
MAX
ReLU
0.5
xavier
471
1.671352
0.749914
0.568846
0.897157
0.426644
0.036630
0.007837
0.020083
0.006877
0.017596
NaN
NaN
none
1
0.0
0
64
0
1
none
5
MAX
1
0
1
0
none
none
3
MAX
0.00
xavier
3
1
0
0.0
32
xavier
MAX
0
0
xavier
ReLU
0
128
xavier
3
1
0
4.0
8
-3.0
ReLU
ReLU
ReLU
none
0.0
ReLU
ELU
AVE
4
0
xavier
1
ReLU
SGD
0
1
-1.875
1
3
0.9
uniform
ReLU
MAX
ReLU
0.5
xavier
131
1.716524
0.778090
0.547251
0.907292
0.425772
0.023136
0.007475
0.013910
0.002910
0.009618
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
0
none
none
3
MAX
0.00
xavier
3
1
64
0.0
32
xavier
MAX
0
2
positive_unitball
ReLU
0
128
msra
3
1
0
4.0
2
-3.0
ReLU
TanH
ReLU
none
0.0
ReLU
ReLU
AVE
0
0
xavier
1
ReLU
SGD
0
1
-2.000
1
3
0.9
xavier
ReLU
MAX
ReLU
0.5
xavier
97
1.678214
0.760523
0.557887
0.886491
0.424225
0.021201
0.010716
0.010884
0.007677
0.006910
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
0
none
none
3
MAX
0.00
xavier
3
1
0
0.0
32
xavier
AVE
0
0
xavier
TanH
0
128
xavier
3
1
0
4.0
8
-3.0
ReLU
TanH
ReLU
none
0.0
ReLU
ReLU
AVE
0
0
xavier
1
ReLU
SGD
0
1
-2.000
1
3
0.9
xavier
ReLU
MAX
ReLU
0.5
xavier
117
1.674817
0.754330
0.560787
0.883020
0.423022
0.024953
0.010179
0.015895
0.006425
0.013321
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
0
none
none
3
AVE
0.00
xavier
3
1
0
0.0
32
xavier
MAX
0
0
xavier
TanH
0
128
xavier
3
1
0
4.0
8
-3.0
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
AVE
0
0
xavier
1
ReLU
SGD
0
1
-2.000
1
3
0.9
xavier
ReLU
MAX
ReLU
0.5
xavier
95
1.683226
0.759043
0.556986
0.886191
0.422857
0.015060
0.010665
0.012257
0.007978
0.014503
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
0
none
none
3
MAX
0.00
xavier
3
1
0
0.0
32
xavier
AVE
0
0
xavier
ReLU
0
128
xavier
3
1
0
4.0
8
-3.0
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
AVE
0
0
xavier
1
ReLU
SGD
0
1
-2.000
1
3
0.9
xavier
ReLU
MAX
ReLU
0.5
xavier
141
1.731896
0.787269
0.536598
0.911558
0.422455
0.010725
0.002810
0.008542
0.001973
0.007547
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
0
none
none
3
MAX
0.00
xavier
3
1
0
0.0
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
0
4.0
2
-3.0
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
MAX
0
0
xavier
1
ReLU
SGD
0
1
-2.000
1
3
0.9
xavier
ReLU
MAX
ReLU
0.5
xavier
472
1.668170
0.744486
0.566883
0.896241
0.422126
0.037831
0.009601
0.024204
0.006467
0.021209
NaN
NaN
none
1
0.0
0
64
0
1
none
5
MAX
1
0
1
0
none
none
3
MAX
0.00
xavier
3
1
0
0.0
32
xavier
MAX
0
0
xavier
ReLU
0
128
xavier
3
1
0
4.0
8
-3.0
ReLU
ReLU
ReLU
none
0.0
ReLU
ELU
AVE
4
0
xavier
1
ReLU
SGD
0
1
-1.875
1
3
0.9
xavier
ReLU
MAX
ReLU
0.5
xavier
475
1.701260
0.765852
0.551032
0.895282
0.422035
0.019890
0.007757
0.013622
0.003375
0.012515
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
0
none
none
3
MAX
1.25
xavier
3
1
64
0.0
32
xavier
MAX
0
0
xavier
ReLU
0
128
gaussian
3
1
0
4.0
8
-3.0
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
AVE
2
0
xavier
1
ReLU
SGD
0
1
-1.875
1
3
0.9
uniform
ReLU
MAX
ReLU
0.5
xavier
93
1.683542
0.757069
0.556831
0.885394
0.421616
0.026848
0.006643
0.014939
0.002247
0.014200
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
0
none
none
3
AVE
0.00
xavier
3
1
0
0.0
32
xavier
AVE
0
0
xavier
TanH
0
128
xavier
3
1
0
4.0
8
-3.0
ReLU
ReLU
ReLU
none
0.0
ReLU
ReLU
AVE
0
0
xavier
1
ReLU
SGD
0
1
-2.000
1
3
0.9
xavier
ReLU
MAX
ReLU
0.5
xavier
In [436]:
bestave = cleanparams(res.sort_values(by='Rtop_mean',ascending=False).iloc[0].to_dict())
In [437]:
deepdiff.DeepDiff(cleanparams(modeldefaults),best)
Out[437]:
{'dictionary_item_added': {"root['Rtop']", "root['split']"},
'values_changed': {"root['conv1_size']": {'new_value': 5.0, 'old_value': 3.0},
"root['conv1_width']": {'new_value': 64.0, 'old_value': 32.0},
"root['conv2_norm']": {'new_value': 'LRN', 'old_value': 'none'},
"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['pool1_size']": {'new_value': 0.0, 'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0, 'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0, 'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE', 'old_value': 'MAX'}}}
In [438]:
deepdiff.DeepDiff(cleanparams(modeldefaults),bestave)
Out[438]:
{'dictionary_item_added': {"root['R_mean']",
"root['R_std']",
"root['Rtop_mean']",
"root['Rtop_std']",
"root['auc_mean']",
"root['auc_std']",
"root['rmse_mean']",
"root['rmse_std']",
"root['serial_max']",
"root['serial_min']",
"root['top_mean']",
"root['top_std']"},
'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_init']": {'new_value': 'uniform', 'old_value': 'xavier'},
"root['conv3_func']": {'new_value': 'ELU', 'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0, 'old_value': 3.0},
"root['pool1_size']": {'new_value': 0.0, 'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0, 'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0, 'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE', 'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0, 'old_value': 0.0},
"root['stratify_affinity']": {'new_value': 1.0, 'old_value': 0.0}}}
In [439]:
pd.set_option("display.max_columns",100)
res.iloc[130:132]
Out[439]:
rmse_mean
top_mean
R_mean
auc_mean
Rtop_mean
rmse_std
top_std
R_std
auc_std
Rtop_std
serial_min
serial_max
conv4_norm
loss_pseudohuber
jitter
conv5_width
conv2_width
pool4_size
conv1_stride
conv5_norm
conv3_size
pool4_type
balanced
ranklossneg
stratify_affinity_step
fc_pose_hidden2
conv1_norm
conv3_norm
conv2_size
pool2_type
loss_penalty
fc_pose_init
conv1_size
conv2_stride
fc_pose_hidden
loss_gap
conv1_width
conv2_init
pool1_type
conv4_width
pool2_size
conv3_init
fc_affinity_func
stratify_affinity
conv3_width
fc_affinity_init
conv5_size
conv4_stride
pool1_size
loss_delta
pool3_size
weight_decay_exp
fc_pose_func2
fc_pose_func
conv1_func
conv2_norm
ranklossmult
conv5_func
conv3_func
pool3_type
pool5_size
fc_affinity_hidden
conv5_init
conv3_stride
fc_affinity_func2
solver
fc_affinity_hidden2
conv5_stride
base_lr_exp
stratify_receptor
conv4_size
momentum
conv1_init
conv4_func
pool5_type
conv2_func
resolution
conv4_init
130
1.717572
0.744584
0.537387
0.887562
0.400171
0.027410
0.013028
0.015014
0.003251
0.014684
NaN
NaN
none
1
0.0
0
32
0
1
none
3
MAX
1
0
1
0
none
none
3
MAX
0.0
xavier
1
1
0
0.0
32
xavier
MAX
0
2
xavier
ReLU
0
128
xavier
3
1
0
4.0
2
-3.0
ReLU
TanH
ReLU
none
0.0
ReLU
ReLU
AVE
0
0
positive_unitball
1
ReLU
SGD
0
1
-2.0
1
3
0.9
xavier
ReLU
MAX
ReLU
0.5
xavier
131
1.716524
0.778090
0.547251
0.907292
0.425772
0.023136
0.007475
0.013910
0.002910
0.009618
NaN
NaN
none
1
0.0
0
64
0
1
none
3
MAX
1
0
1
0
none
none
3
MAX
0.0
xavier
3
1
64
0.0
32
xavier
MAX
0
2
positive_unitball
ReLU
0
128
msra
3
1
0
4.0
2
-3.0
ReLU
TanH
ReLU
none
0.0
ReLU
ReLU
AVE
0
0
xavier
1
ReLU
SGD
0
1
-2.0
1
3
0.9
xavier
ReLU
MAX
ReLU
0.5
xavier
In [440]:
import pprint
In [441]:
pp = pprint.PrettyPrinter(indent=1)
for (r,row) in res.sort_values(by='Rtop_mean',ascending=False).iloc[:25].iterrows():
ave = row.drop(['Rtop_mean','serial_min','auc_mean','top_std','Rtop_std','rmse_mean','serial_max','top_mean','auc_std','rmse_std','R_mean','R_std']).to_dict()
print r
pp.pprint(deepdiff.DeepDiff(cleanparams(modeldefaults),cleanparams(ave)))
473
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['conv3_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0},
"root['stratify_affinity']": {'new_value': 1.0,
'old_value': 0.0}}}
471
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['conv3_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0}}}
131
{'values_changed': {"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
97
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
117
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
95
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
141
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0}}}
472
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv3_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0}}}
475
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['loss_penalty']": {'new_value': 1.25,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 2.0,
'old_value': 0.0}}}
93
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
126
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
132
{'values_changed': {"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
125
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
140
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0}}}
96
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
124
{'values_changed': {"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['conv3_width']": {'new_value': 64.0,
'old_value': 128.0},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 1024.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['stratify_affinity']": {'new_value': 1.0,
'old_value': 0.0}}}
82
{'values_changed': {"root['conv1_width']": {'new_value': 128.0,
'old_value': 32.0},
"root['conv3_func']": {'new_value': 'leaky',
'old_value': 'ReLU'},
"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['conv3_stride']": {'new_value': 3.0,
'old_value': 1.0},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 2048.0,
'old_value': 0.0},
"root['momentum']": {'new_value': 0.75, 'old_value': 0.9},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
139
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0}}}
129
{'values_changed': {"root['conv1_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['conv1_init']": {'new_value': 'gaussian',
'old_value': 'xavier'},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
122
{'values_changed': {"root['conv1_norm']": {'new_value': 'LRN',
'old_value': 'none'},
"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
444
{'values_changed': {"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['conv3_width']": {'new_value': 512.0,
'old_value': 128.0},
"root['loss_penalty']": {'new_value': 1.25,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 2.0,
'old_value': 0.0}}}
458
{'values_changed': {"root['conv2_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['loss_gap']": {'new_value': 3.125,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
128
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0}}}
469
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0},
"root['weight_decay_exp']": {'new_value': -10.0,
'old_value': -3.0}}}
137
{'values_changed': {"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
In [442]:
pp = pprint.PrettyPrinter(indent=1)
for (r,row) in res.sort_values(by='top_mean',ascending=False).iloc[:10].iterrows():
ave = row.drop(['Rtop_mean','serial_min','serial_max','auc_mean','top_std','Rtop_std','rmse_mean','top_mean','auc_std','rmse_std','R_mean','R_std']).to_dict()
print r
pp.pprint(deepdiff.DeepDiff(cleanparams(modeldefaults),cleanparams(ave)))
141
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0}}}
140
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0}}}
127
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 4.0,
'old_value': 2.0}}}
139
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0}}}
118
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 4.0,
'old_value': 2.0}}}
131
{'values_changed': {"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
129
{'values_changed': {"root['conv1_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['conv1_init']": {'new_value': 'gaussian',
'old_value': 'xavier'},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
132
{'values_changed': {"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
82
{'values_changed': {"root['conv1_width']": {'new_value': 128.0,
'old_value': 32.0},
"root['conv3_func']": {'new_value': 'leaky',
'old_value': 'ReLU'},
"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['conv3_stride']": {'new_value': 3.0,
'old_value': 1.0},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 2048.0,
'old_value': 0.0},
"root['momentum']": {'new_value': 0.75, 'old_value': 0.9},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
150
{'values_changed': {"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 0.0,
'old_value': 2.0}}}
In [443]:
pp = pprint.PrettyPrinter(indent=1)
for (r,row) in res.sort_values(by='R_mean',ascending=False).iloc[:10].iterrows():
ave = row.drop(['Rtop_mean','serial_min','serial_max','auc_mean','top_std','Rtop_std','rmse_mean','top_mean','auc_std','rmse_std','R_mean','R_std']).to_dict()
print r
pp.pprint(deepdiff.DeepDiff(cleanparams(modeldefaults),cleanparams(ave)))
473
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['conv3_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0},
"root['stratify_affinity']": {'new_value': 1.0,
'old_value': 0.0}}}
471
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['conv3_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0}}}
472
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv3_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0}}}
124
{'values_changed': {"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['conv3_width']": {'new_value': 64.0,
'old_value': 128.0},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 1024.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['stratify_affinity']": {'new_value': 1.0,
'old_value': 0.0}}}
77
{'values_changed': {"root['loss_gap']": {'new_value': 0.625,
'old_value': 0.0},
"root['momentum']": {'new_value': 0.125,
'old_value': 0.9},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['ranklossneg']": {'new_value': 1.0,
'old_value': 0.0},
"root['stratify_affinity_step']": {'new_value': 2.0,
'old_value': 1.0}}}
324
{'values_changed': {"root['conv2_size']": {'new_value': 7.0,
'old_value': 3.0}}}
117
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
125
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
97
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
451
{'values_changed': {"root['jitter']": {'new_value': 1.0, 'old_value': 0.0},
"root['loss_penalty']": {'new_value': 4.375,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['stratify_affinity']": {'new_value': 1.0,
'old_value': 0.0}}}
In [444]:
defaults
Out[444]:
{'balanced': 1,
'base_lr_exp': -2,
'conv1_func': 'ReLU',
'conv1_init': 'xavier',
'conv1_norm': 'none',
'conv1_size': 3,
'conv1_stride': 1,
'conv1_width': 32,
'conv2_func': 'ReLU',
'conv2_init': 'xavier',
'conv2_norm': 'none',
'conv2_size': 3,
'conv2_stride': 1,
'conv2_width': 64,
'conv3_func': 'ReLU',
'conv3_init': 'xavier',
'conv3_norm': 'none',
'conv3_size': 3,
'conv3_stride': 1,
'conv3_width': 128,
'conv4_func': 'ReLU',
'conv4_init': 'xavier',
'conv4_norm': 'none',
'conv4_size': 3,
'conv4_stride': 1,
'conv4_width': 0,
'conv5_func': 'ReLU',
'conv5_init': 'xavier',
'conv5_norm': 'none',
'conv5_size': 3,
'conv5_stride': 1,
'conv5_width': 0,
'fc_affinity_func': 'ReLU',
'fc_affinity_func2': 'ReLU',
'fc_affinity_hidden': 0,
'fc_affinity_hidden2': 0,
'fc_affinity_init': 'xavier',
'fc_pose_func': 'ReLU',
'fc_pose_func2': 'ReLU',
'fc_pose_hidden': 0,
'fc_pose_hidden2': 0,
'fc_pose_init': 'xavier',
'jitter': 0.0,
'loss_delta': 4,
'loss_gap': 0,
'loss_penalty': 0,
'loss_pseudohuber': 1,
'momentum': 0.9,
'pool1_size': 2,
'pool1_type': 'MAX',
'pool2_size': 2,
'pool2_type': 'MAX',
'pool3_size': 2,
'pool3_type': 'MAX',
'pool4_size': 0,
'pool4_type': 'MAX',
'pool5_size': 0,
'pool5_type': 'MAX',
'ranklossmult': 0,
'ranklossneg': 0,
'resolution': 0.5,
'solver': 'SGD',
'stratify_affinity': 0,
'stratify_affinity_step': 1,
'stratify_receptor': 1,
'weight_decay_exp': -3}
In [445]:
rtop = res.sort_values(by='Rtop_mean',ascending=False)
plt.errorbar(xrange(len(rtop)),rtop.Rtop_mean,yerr=rtop.Rtop_std,label="Rtop")
plt.errorbar(xrange(len(rtop)),rtop.R_mean,yerr=rtop.R_std,label="R")
plt.errorbar(xrange(len(rtop)),rtop.top_mean,yerr=rtop.top_std,label="top")
plt.xlim(0,100)
plt.ylim(0.35,.85)
plt.legend()
Out[445]:
<matplotlib.legend.Legend at 0x7f73bcb89850>
In [521]:
rtop = res.sort_values(by='Rtop_mean',ascending=False)
plt.errorbar(xrange(len(rtop)),rtop.Rtop_mean,yerr=rtop.Rtop_std,label="Rtop")
plt.xlim(-0.5,25)
plt.ylim(0.35,.45)
ax = plt.gca()
ax.axhline(defaultave,color='grey')
ax.axhline(defaultave-defaultstd,color='grey',linewidth=0.5)
ax.axhline(defaultave+dstd,color='grey',linewidth=0.5);
plt.title('Rtop')
Out[521]:
Text(0.5,1,u'Rtop')
In [447]:
vals = res.sort_values(by='R_mean',ascending=False)
plt.errorbar(xrange(len(vals)),vals.R_mean,yerr=vals.R_std,label="R",color='orange')
plt.xlim(-0.5,25)
plt.ylim(0.5,.6)
ax = plt.gca()
dave = float(singleres[singleres.catname=='default'].R_mean)
dstd = float(singleres[singleres.catname=='default'].R_std)
ax.axhline(dave,color='grey')
ax.axhline(dave-dstd,color='grey',linewidth=0.5)
ax.axhline(dave+dstd,color='grey',linewidth=0.5);
plt.legend()
Out[447]:
<matplotlib.legend.Legend at 0x7f73aa651a10>
In [448]:
vals = res.sort_values(by='top_mean',ascending=False)
plt.errorbar(xrange(len(vals)),vals.top_mean,yerr=vals.top_std,label="top",color='green')
plt.xlim(-0.5,25)
plt.ylim(0.7,.8)
ax = plt.gca()
dave = float(singleres[singleres.catname=='default'].top_mean)
dstd = float(singleres[singleres.catname=='default'].top_std)
ax.axhline(dave,color='grey')
ax.axhline(dave-dstd,color='grey',linewidth=0.5)
ax.axhline(dave+dstd,color='grey',linewidth=0.5);
plt.legend()
Out[448]:
<matplotlib.legend.Legend at 0x7f73aa589bd0>
In [ ]:
In [ ]:
In [449]:
cleaneddata = pd.DataFrame([ cleanparams(d) for d in data.to_dict('records')])
In [450]:
grouped = cleaneddata.groupby(params)
metrics = grouped.agg([np.mean,np.std,np.min,np.max])
metrics = metrics[grouped.size() >= 5]
metrics = metrics.my_flatten_cols()
metrics = metrics.reset_index()
In [451]:
rtop = metrics.sort_values(by='Rtop_mean',ascending=False)
plt.errorbar(xrange(len(rtop)),rtop.Rtop_mean,yerr=rtop.Rtop_std,label="Rtop")
plt.errorbar(xrange(len(rtop)),rtop.R_mean,yerr=rtop.R_std,label="R")
plt.errorbar(xrange(len(rtop)),rtop.top_mean,yerr=rtop.top_std,label="top")
plt.xlim(-.5,25)
plt.ylim(0.35,.85)
plt.legend()
Out[451]:
<matplotlib.legend.Legend at 0x7f73b143acd0>
In [452]:
pp = pprint.PrettyPrinter(indent=1)
dcols = [c for c in rtop.columns if '_std' in c or '_mean' in c or '_amax' in c or '_amin' in c or c == 'index']
for (r,row) in rtop.iloc[:25].iterrows():
ave = row.drop(dcols).to_dict()
print r
pp.pprint(deepdiff.DeepDiff(cleanparams(modeldefaults),cleanparams(ave)))
354
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['conv3_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0},
"root['stratify_affinity']": {'new_value': 1.0,
'old_value': 0.0}}}
352
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['conv3_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0}}}
312
{'values_changed': {"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
353
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv3_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0}}}
334
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['loss_penalty']": {'new_value': 1.25,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 2.0,
'old_value': 0.0}}}
68
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
99
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0}}}
319
{'values_changed': {"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['conv3_width']": {'new_value': 64.0,
'old_value': 128.0},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 1024.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['stratify_affinity']": {'new_value': 1.0,
'old_value': 0.0}}}
321
{'values_changed': {"root['conv1_width']": {'new_value': 128.0,
'old_value': 32.0},
"root['conv3_func']": {'new_value': 'leaky',
'old_value': 'ReLU'},
"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['conv3_stride']": {'new_value': 3.0,
'old_value': 1.0},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 2048.0,
'old_value': 0.0},
"root['momentum']": {'new_value': 0.75, 'old_value': 0.9},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
100
{'values_changed': {"root['conv1_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['conv1_init']": {'new_value': 'gaussian',
'old_value': 'xavier'},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
19
{'values_changed': {"root['conv1_norm']": {'new_value': 'LRN',
'old_value': 'none'},
"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
332
{'values_changed': {"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['conv3_width']": {'new_value': 512.0,
'old_value': 128.0},
"root['loss_penalty']": {'new_value': 1.25,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 2.0,
'old_value': 0.0}}}
345
{'values_changed': {"root['conv2_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['loss_gap']": {'new_value': 3.125,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
70
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0}}}
67
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0},
"root['weight_decay_exp']": {'new_value': -10.0,
'old_value': -3.0}}}
314
{'values_changed': {"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
356
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv3_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['loss_penalty']": {'new_value': 1.875,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
98
{'values_changed': {"root['conv1_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0}}}
378
{'values_changed': {"root['conv2_size']": {'new_value': 1.0,
'old_value': 3.0},
"root['conv2_width']": {'new_value': 32.0,
'old_value': 64.0},
"root['conv3_width']": {'new_value': 64.0,
'old_value': 128.0},
"root['conv4_size']": {'new_value': 1.0,
'old_value': 3.0},
"root['conv4_width']": {'new_value': 64.0,
'old_value': 0.0},
"root['conv5_width']": {'new_value': 128.0,
'old_value': 0.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool5_size']": {'new_value': 2.0,
'old_value': 0.0}}}
66
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 4.0,
'old_value': 2.0}}}
6
{'values_changed': {"root['conv1_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['conv1_init']": {'new_value': 'radial',
'old_value': 'xavier'},
"root['conv3_init']": {'new_value': 'radial',
'old_value': 'xavier'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['loss_pseudohuber']": {'new_value': 0.0,
'old_value': 1.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
71
{'values_changed': {"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 0.0,
'old_value': 2.0}}}
23
{'values_changed': {"root['conv2_size']": {'new_value': 1.0,
'old_value': 3.0}}}
95
{'values_changed': {"root['conv3_width']": {'new_value': 16.0,
'old_value': 128.0}}}
72
{'values_changed': {"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0}}}
In [531]:
pp = pprint.PrettyPrinter(indent=1)
dcols = [c for c in metrics.columns if '_std' in c or '_mean' in c or '_amax' in c or '_amin' in c or c == 'index']
for (r,row) in metrics.sort_values(by='top_mean',ascending=False)[:10].iterrows():
ave = row.drop(dcols).to_dict()
print r,row.Rtop_mean, row.R_mean,row.top_mean
pp.pprint(deepdiff.DeepDiff(cleanparams(modeldefaults),cleanparams(ave)))
99 0.419261316683 0.534384485516 0.784587548318
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0}}}
66 0.404156806872 0.516345352673 0.782704169751
{'values_changed': {"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 4.0,
'old_value': 2.0}}}
312 0.423441274793 0.545284743224 0.776511226252
{'values_changed': {"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 64.0,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
100 0.415185866347 0.535493375084 0.775326918332
{'values_changed': {"root['conv1_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['conv1_init']": {'new_value': 'gaussian',
'old_value': 'xavier'},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
321 0.417439047728 0.539182259581 0.774142610412
{'values_changed': {"root['conv1_width']": {'new_value': 128.0,
'old_value': 32.0},
"root['conv3_func']": {'new_value': 'leaky',
'old_value': 'ReLU'},
"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['conv3_stride']": {'new_value': 3.0,
'old_value': 1.0},
"root['fc_pose_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['fc_pose_hidden']": {'new_value': 2048.0,
'old_value': 0.0},
"root['momentum']": {'new_value': 0.75, 'old_value': 0.9},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
71 0.40379964542 0.522534712613 0.772711571675
{'values_changed': {"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 0.0,
'old_value': 2.0}}}
67 0.410116007832 0.532235763661 0.770441648162
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv1_func']": {'new_value': 'TanH',
'old_value': 'ReLU'},
"root['conv1_init']": {'new_value': 'uniform',
'old_value': 'xavier'},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'},
"root['pool5_size']": {'new_value': 4.0,
'old_value': 0.0},
"root['weight_decay_exp']": {'new_value': -10.0,
'old_value': -3.0}}}
356 0.409505703616 0.531647838474 0.770244263509
{'values_changed': {"root['base_lr_exp']": {'new_value': -1.875,
'old_value': -2.0},
"root['conv3_func']": {'new_value': 'ELU',
'old_value': 'ReLU'},
"root['conv3_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['loss_penalty']": {'new_value': 1.875,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_size']": {'new_value': 8.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
72 0.40233765343 0.522311386189 0.770244263509
{'values_changed': {"root['pool2_size']": {'new_value': 0.0,
'old_value': 2.0}}}
345 0.413366604657 0.537705248678 0.768763878608
{'values_changed': {"root['conv2_size']": {'new_value': 5.0,
'old_value': 3.0},
"root['conv3_init']": {'new_value': 'positive_unitball',
'old_value': 'xavier'},
"root['loss_gap']": {'new_value': 3.125,
'old_value': 0.0},
"root['pool1_size']": {'new_value': 0.0,
'old_value': 2.0},
"root['pool3_type']": {'new_value': 'AVE',
'old_value': 'MAX'}}}
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Content source: gnina/scripts
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