In [1]:

    

import pylearn2.utils
import pylearn2.config
import theano
import neukrill_net.dense_dataset
import neukrill_net.utils
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import holoviews as hl
%load_ext holoviews.ipython
import sklearn.metrics


    

    




Using gpu device 0: Tesla K40c
:0: FutureWarning: IPython widgets are experimental and may change in the future.






    




Welcome to the HoloViews IPython extension! (http://ioam.github.io/holoviews/)
Available magics: %compositor, %opts, %params, %view, %%labels, %%opts, %%view






    






<matplotlib.figure.Figure at 0x7f40407a77d0>






    






<matplotlib.figure.Figure at 0x7f40407a7e90>






    






<matplotlib.figure.Figure at 0x7f40407a7c90>

In [20]:

    

def make_curves(model, *args):
    curves = None
    for c in args:
        channel = model.monitor.channels[c]
        c = c[0].upper() + c[1:]
        if not curves:
            curves = hl.Curve(zip(np.array(channel.time_record)/60,channel.val_record),group=c)
        else:
            curves += hl.Curve(zip(np.array(channel.time_record)/60,channel.val_record),group=c)
    return curves


    

In [8]:

    

channel = m.monitor.channels['valid_y_nll']


    

In [4]:

    

m = pylearn2.utils.serial.load(
    "/disk/scratch/neuroglycerin/models/online_manyaug_faster.pkl")


    

In [21]:

    

nll_channels = [c for c in m.monitor.channels.keys() if 'nll' in c]


    

In [22]:

    

make_curves(m,*nll_channels)


    

    
Out[22]:

In [24]:

    

m2 = pylearn2.utils.serial.load(
    "/disk/scratch/neuroglycerin/models/online_manyaug_faster_8aug_recent.pkl")


    

In [25]:

    

make_curves(m2,*nll_channels)


    

    
Out[25]:

In [26]:

    

m = pylearn2.utils.serial.load(
    "/disk/scratch/neuroglycerin/models/online_manyaug_faster_frscale_recent.pkl")


    

In [27]:

    

make_curves(m,*nll_channels)


    

    
Out[27]:

In [28]:

    

m = pylearn2.utils.serial.load(
    "/disk/scratch/neuroglycerin/models/online_manyaug_faster_frshear_recent.pkl")


    

In [29]:

    

make_curves(m,*nll_channels)


    

    
Out[29]:

In [30]:

    

m = pylearn2.utils.serial.load(
    "/disk/scratch/neuroglycerin/models/online_manyaug_faster_frtranslate_recent.pkl")


    

In [31]:

    

make_curves(m,*nll_channels)


    

    
Out[31]:

In [34]:

    

m = pylearn2.utils.serial.load(
    "/disk/scratch/neuroglycerin/models/online_manyaug_3conv1mlp_recent.pkl")


    

In [35]:

    

make_curves(m,*nll_channels)


    

    
Out[35]:

In [36]:

    

m = pylearn2.utils.serial.load(
    "/disk/scratch/neuroglycerin/models/online_manyaug_3conv2mlp_recent.pkl")


    

In [37]:

    

make_curves(m,*nll_channels)


    

    
Out[37]:

In [38]:

    

m = pylearn2.utils.serial.load(
    "/disk/scratch/neuroglycerin/models/online_manyaug_4conv1mlp_recent.pkl")


    

In [39]:

    

make_curves(m,*nll_channels)


    

    
Out[39]:

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