In [29]:

    

import cPickle as pickle
import numpy as np


    

In [38]:

    

inFile = 'models/test.bin'
phoneFile = 'data/timit_sets.txt'


    

In [31]:

    

# read model training history
with open(inFile,'r') as f:
    nn_opts = pickle.load(f)
    costt = pickle.load(f)
    nn_hist = pickle.load(f)


    

In [47]:

    

# read phone strings
phone_list = []
with open(phoneFile,'r') as f:
    phone_list = map(lambda x: x.rstrip(), f.readlines())
# prepend symbol for blank
phone_list.insert(0,'')


    

In [32]:

    

pyplot.plot(costt,'kx')


    

    
Out[32]:





[<matplotlib.lines.Line2D at 0xd660950>]

In [33]:

    

k =nn_hist.keys()[0]


    

In [34]:

    

# analyze probs after first iteration
cur_prob, cur_delta = nn_hist[k][0]


    

In [35]:

    

cur_prob.shape


    

    
Out[35]:





(40, 259)

In [36]:

    

key_list = nn_hist.keys()[:3]


    

In [54]:

    

# plot a bunch of the training set 
for k in key_list:
    cur_prob, cur_delta = nn_hist[k][0]
    plt.figure()
    # Have a look at the colormaps here and decide which one you'd like:
    # http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html
    colormap = plt.cm.gist_ncar
    plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, 40)])
    for p in range(cur_prob.shape[0]):
        plt.subplot(2,1,1)
        plt.plot(cur_prob[p,:], )
        plt.subplot(2,1,2)
        plt.plot(cur_delta[p,:], )
    # put text for each new argmax phone
    plt.subplot(2,1,1)
    pmInd = -1
    hyp = []
    maxInd = np.argmax(cur_prob, axis=0)
    for t in range(cur_prob.shape[1]):
            if maxInd[t] != pmInd:
                pmInd = maxInd[t]
                plt.text(t,0.5,phone_list[pmInd])
                hyp.append(phone_list[pmInd])
    plt.title('-'.join(hyp))
    plt.show()
    #input('press to continue')


    

In [55]:

    

plt.close('all')


    

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