In [1]:

    

from __future__ import print_function, division
import numpy as np
import matplotlib.pyplot as plt
import theano
import theano.tensor as T


    

    




Couldn't import dot_parser, loading of dot files will not be possible.

In [2]:

    

SEQ_LENGTH = 1000
def generate_single_example(power, duration, start):
    activation = np.ones(duration, dtype=np.float32) * power
    sequence = np.zeros(SEQ_LENGTH, dtype=np.float32)
    sequence[start:start+duration] += activation
    return sequence


    

In [3]:

    

N_SEQ_PER_BATCH = 5
def generate_example(*args, **kwargs):
    data = np.empty((N_SEQ_PER_BATCH, SEQ_LENGTH, 1))
    for i in range(N_SEQ_PER_BATCH):
        data[i,:,0] = generate_single_example(*args, **kwargs)
    return data


    

In [4]:

    

error = 50
power = 100

short_seq_t = generate_single_example(power=power, duration=100, start=100)
short_seq_x = generate_single_example(power=power+error, duration=100, start=100)

long_seq_t = generate_single_example(power=power, duration=500, start=100)
long_seq_x = generate_single_example(power=power+error, duration=500, start=100)


    

In [5]:

    

def cost(x, t):
    return np.mean((x - t) ** 2)


    

In [6]:

    

cost(short_seq_t, short_seq_x)


    

    
Out[6]:





250.0

In [7]:

    

cost(long_seq_t, long_seq_x)


    

    
Out[7]:





1250.0

In [8]:

    

short_seq_t[short_seq_t > 0]


    

    
Out[8]:





array([ 100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,
        100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,
        100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,
        100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,
        100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,
        100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,
        100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,
        100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,
        100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,
        100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,
        100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.,  100.], dtype=float32)

In [48]:

    

from theano.ifelse import ifelse
THRESHOLD = 0
def cost2(x, t):
    sq_error = (x - t) ** 2
    def mask_and_mean_sq_error(mask):
        masked_sq_error = sq_error[mask.nonzero()]
        mean = masked_sq_error.mean()
        mean = ifelse(T.isnan(mean), 0.0, mean)
        return mean
    above_thresh_mean = mask_and_mean_sq_error(t > THRESHOLD)
    below_thresh_mean = mask_and_mean_sq_error(t <= THRESHOLD)
    return (above_thresh_mean + below_thresh_mean) / 2.0


    

In [49]:

    

cost2(theano.shared(short_seq_x), theano.shared(short_seq_t)).eval()


    

    
Out[49]:





array(1250.0, dtype=float32)

In [50]:

    

cost2(short_seq_x, short_seq_t)


    

    
Out[50]:





Elemwise{true_div,no_inplace}.0

In [51]:

    

cost2(theano.shared(np.zeros(TOTAL_LENGTH, dtype=np.float32)), theano.shared(np.zeros(TOTAL_LENGTH, dtype=np.float32))).eval()


    

    
Out[51]:





array(0.0, dtype=float32)

In [6]:

    

(t > THRESHOLD).shape[0].eval()


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-6-b8cc6d150b47> in <module>()
----> 1 (t > THRESHOLD).shape[0].eval()

NameError: name 't' is not defined

In [35]:

    

(0-0) ** 2


    

    
Out[35]:





0

In [41]:

    

t = generate_example(power=power, duration=100, start=100)
# t = theano.shared(t)


    

In [42]:

    

n_above_thresh_per_seq = (t > 0).sum(axis=1)


    

In [43]:

    

n_below_thresh_per_seq = SEQ_LENGTH - n_above_thresh_per_seq


    

In [49]:

    

n_above_thresh_per_seq


    

    
Out[49]:





array([[100],
       [100],
       [100],
       [100],
       [100]])

In [45]:

    

mask = np.ones(t.shape)


    

In [52]:

    

mask[(t > 0)] *= np.ones(500)


    

In [29]:

    

mask.shape


    

    
Out[29]:





(5, 1000, 1)

In [25]:

    

(t > 0).nonzero().eval()


    

    




---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-25-929b7e9ee44d> in <module>()
----> 1 (t > 0).nonzero().eval()

AttributeError: 'tuple' object has no attribute 'eval'

In [13]:

    

mask.shape


    

    
Out[13]:





(5, 1000, 1)

In [59]:

    

#for seq_i in range(t.shape[0]):
seq_i = 0
THRESHOLD = 5
error = t.copy()
elements_above_thresh = t[seq_i, :, 0] > THRESHOLD
n_above_thresh = elements_above_thresh.sum()
error[seq_i, elements_above_thresh, 0] *= 1 / n_above_thresh
n_below_thresh = SEQ_LENGTH - n_above_thresh
error[seq_i, -elements_above_thresh, 0] *= 1 / n_below_thresh
error[seq_i,:,0].sum() / 2


    

    
Out[59]:





50.0

In [55]:

    

n_above_thresh


    

    
Out[55]:





100

In [56]:

    

SEQ_LENGTH


    

    
Out[56]:





1000

In [61]:

    

theano.shared(np.zeros(10, dtype=np.float32)).eval()


    

    
Out[61]:





array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.], dtype=float32)

In [ ]: