Hi everybody,
I am trying to approximate a function that consist of multiple overlapping
Gaussian kernels. The calculation should be done in the scan function that
iterates over the Gaussian kernels. At this point I got stuck with two
problems.
1. I have tried to calculate the sum of all kernels for each input by using
the previous result. However, the return value is not the sum of all
kernels for a given input but something else. I was not able to figure it
out yet. See attached file scan_sum_kernel.py
2. I would like to define the input vector and the kernel parameters
(height, mean, variance) as NumPy arrays. Therefore, I added the ‘givens’
parameter to the function. However, this leads to the Value Error: "length
not known: <TensorType(int64, vector)> [id A]" See attached file
scan_shared_variable.py
Any hint and advice how to go on and what to try next is much appreciated!
Below the code that is working so far:
x = np.arange(start=1,stop=100,step=1)
xs = T.dscalar('xs')
height = T.dvector('height')
mean = T.dvector('mean')
variance = T.dvector('variance')
bias = T.dvector('bias')
def gaussian(height, mean, variance, bias, x):
return (height * \
T.exp(-(T.sqr( x-mean) / \
(2*variance)))) + bias
gaus_dist, updates = theano.scan(
fn=gaussian,
sequences=[height, mean, variance, bias],
non_sequences=[xs]
)
get_gaus = theano.function(inputs=[xs, height, mean, variance, bias],
outputs=gaus_dist)
gaus3 = np.array([])
for xs in x:
gaus1 = get_gaus(xs, [2,10,11], [10,35,64], [2,12,22], [2, 3, 4])
gaus2=0
for gaus_tmp in gaus1:
gaus2=gaus2+gaus_tmp
gaus3 = np.append(gaus3,gaus2)
%matplotlib inline
plt.plot(gaus3)
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# coding: utf-8
# In[4]:
import theano.tensor as T
import theano
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(start=1,stop=100,step=1)
xs = T.dscalar('xs')
height = T.dvector('height')
mean = T.dvector('mean')
variance = T.dvector('variance')
bias = T.dvector('bias')
def gaussian(height, mean, variance, bias, last_ret, x):
tmp = (height * T.exp(-(T.sqr( x-mean) / (2*variance)))) + bias
return tmp + last_ret
gaus_dist, updates = theano.scan(
fn=gaussian,
sequences=[height, mean, variance, bias],
outputs_info=[np.float64(0)],
non_sequences=[xs]
)
get_gaus = theano.function(inputs=[xs, height, mean, variance, bias],
outputs=gaus_dist)
gaus3 = np.array([])
for xs in x:
gaus1 = get_gaus(xs, [2,10,11], [10,35,64], [2,12,22], [0, 0, 0])
gaus2=0
for gaus_tmp in gaus1:
gaus2=gaus2+gaus_tmp
gaus3 = np.append(gaus3,gaus2)
get_ipython().magic('matplotlib inline')
plt.plot(gaus3)
# In[ ]:
# coding: utf-8
# In[1]:
import theano.tensor as T
import theano
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(start=1,stop=100,step=1)
xs = theano.shared(x)
height_np = np.array([2,10,11])
height = theano.shared(height_np)
mean_np = np.array([10,35,64])
mean = theano.shared(mean_np)
variance_np= np.array([2,12,22])
variance = theano.shared(variance_np)
bias_np = np.array([2,2,4])
bias = theano.shared(bias_np)
def gaussian(height, mean, variance, bias, x):
return (height * T.exp(-(T.sqr( x-mean) / (2*variance)))) + bias
gaus_dist, updates = theano.scan(
fn=gaussian,
sequences=[height, mean, variance, bias],
non_sequences=[xs],
)
get_gaus = theano.function(inputs=[xs],
outputs=gaus_dist,
givens=[height, mean, variance, bias])
gaus3 = np.array([])
for xs in x:
gaus1 = get_gaus(xs, height, mean, variance, bias)
gaus2=0
for gaus_tmp in gaus1:
gaus2=gaus2+gaus_tmp
gaus3 = np.append(gaus3,gaus2)
get_ipython().magic('matplotlib inline')
plt.plot(gaus3)
# In[ ]:
