I am trying to fit a predefined 2d gaussian function to some observed data with pymc. I keep running into errors and the last one I got was ValueError: setting an array element with a sequence. I understand what the error means, but I am not sure where the error is occurring in the code. My naive guess would be the random variables are being set to some array elements. Any suggestions would be much appreciated. Here is my code so far:
import pymc as mc
import numpy as np
import pyfits as pf
arr = pf.getdata('img.fits')
x=y=np.arange(0,71)
xx,yy=np.meshgrid(x,y)
err_map = pf.getdata('imgwht.fits')
def model((x,y),arr):
amp = mc.Uniform('amp',lower=-1,upper=1,doc='Amplitude')
x0 = mc.Uniform('x0',lower=21,upper=51,doc='xo')
y0 = mc.Uniform('y0',lower=21,upper=51,doc='yo')
sigx = mc.Uniform('sigx',lower=0.1,upper=10,doc='Sigma in X')
sigy = mc.Uniform('sigy',lower=0.1,upper=10,doc='Sigma in Y')
thta = mc.Uniform('theta',lower=0,upper=2*np.pi,doc='Rotation')
os = mc.Uniform('c',lower=-1,upper=1,doc='Vertical offset')
#mc.deterministic(plot=False,trace=False)
def gaussian((x, y)=(xx,yy), amplitude=amp, xo=x0, yo=y0, sigma_x=sigx, sigma_y=sigy, theta=thta, offset=os):
xo = float(xo)
yo = float(yo)
a = (mc.cos(theta)**2)/(2*sigma_x**2) + (mc.sin(theta)**2)/(2*sigma_y**2)
b = -(mc.sin(2*theta))/(4*sigma_x**2) + (mc.sin(2*theta))/(4*sigma_y**2)
c = (mc.sin(theta)**2)/(2*sigma_x**2) + (mc.cos(theta)**2)/(2*sigma_y**2)
gauss = offset+amplitude*mc.exp(-1*(a*((x-xo)**2)+2*b*(x-xo)*(y-yo)+c*((y-yo)**2)))
return gauss
flux = mc.Normal('flux',mu=gaussian,tau=err_map,value=arr,observed=True,doc='Observed Flux')
return locals()
mdl = mc.MCMC(model((xx,yy),arr))
mdl.sample(iter=1e5,burn=9e4)
Full traceback:
File "model.py", line 31, in <module>
mdl = mc.MCMC(model((xx,yy),arr))
File "model.py", line 29, in model
flux = mc.Normal('flux',mu=gaussian,tau=err_map,value=arr,observed=True,doc='Observed Flux')
File "/usr/lib64/python2.7/site-packages/pymc/distributions.py", line 318, in __init__
**arg_dict_out)
File "/usr/lib64/python2.7/site-packages/pymc/PyMCObjects.py", line 761, in __init__
verbose=verbose)
File "/usr/lib64/python2.7/site-packages/pymc/Node.py", line 219, in __init__
Node.__init__(self, doc, name, parents, cache_depth, verbose=verbose)
File "/usr/lib64/python2.7/site-packages/pymc/Node.py", line 129, in __init__
self.parents = parents
File "/usr/lib64/python2.7/site-packages/pymc/Node.py", line 152, in _set_parents
self.gen_lazy_function()
File "/usr/lib64/python2.7/site-packages/pymc/PyMCObjects.py", line 810, in gen_lazy_function
self._logp.force_compute()
File "LazyFunction.pyx", line 257, in pymc.LazyFunction.LazyFunction.force_compute (pymc/LazyFunction.c:2409)
File "/usr/lib64/python2.7/site-packages/pymc/distributions.py", line 2977, in wrapper
return f(value, **kwds)
File "/usr/lib64/python2.7/site-packages/pymc/distributions.py", line 2168, in normal_like
return flib.normal(x, mu, tau)
ValueError: setting an array element with a sequence.
I've run into an issue like this before, but never had a chance to track it down to its source. The problem line in your code is the one for the observed Stochastic:
flux = mc.Normal('flux',mu=gaussian,tau=err_map,value=arr,observed=True,doc='Observed Flux')
I know a work-around that you can use, which is to check if the mu variable is a pymc.Node, and only find the likelihood if it is not:
#mc.observed
def flux(mu=gaussian,tau=err_map,value=arr):
if isinstance(mu, mc.Node):
return 0
else:
return mc.normal_like(value, mu, tau)
I think it would be worth filing a bug report in the PyMC github issue tracker if you have time.
The #mc.deterministic decorator returns a deterministic variable. To get the value of the variable, use the attribute value.
flux = mc.Normal('flux',mu=gaussian.value,tau=err_map,value=arr,observed=True,doc='Observed Flux')
Related
So I was working on a project and I came across this error related to sympy:
Traceback (most recent call last):
File "c:\Users\Andres\OneDrive - Centre d'Estudis Monlau\z.ottro\final program try 1\testing area 2.py", line 4, in <module>
Ymin = minimum(root(x,3), x, domain=Interval(-7,7))
File "C:\Users\Andres\AppData\Local\Programs\Python\Python39\lib\site-packages\sympy\calculus\util.py", line 837, in minimum
return function_range(f, symbol, domain).inf
File "C:\Users\Andres\AppData\Local\Programs\Python\Python39\lib\site-packages\sympy\calculus\util.py", line 220, in function_range range_int += Interval(vals.inf, vals.sup, left_open, right_open)
File "C:\Users\Andres\AppData\Local\Programs\Python\Python39\lib\site-packages\sympy\sets\sets.py", line 274, in inf
return self._inf
File "C:\Users\Andres\AppData\Local\Programs\Python\Python39\lib\site-packages\sympy\sets\sets.py", line 1910, in _inf
return Min(*self)
File "C:\Users\Andres\AppData\Local\Programs\Python\Python39\lib\site-packages\sympy\functions\elementary\miscellaneous.py", line 391, in __new__
args = frozenset(cls._new_args_filter(args))
File "C:\Users\Andres\AppData\Local\Programs\Python\Python39\lib\site-packages\sympy\functions\elementary\miscellaneous.py", line 564, in _new_args_filter
raise ValueError("The argument '%s' is not comparable." % arg)
ValueError: The argument '(-7)**(1/3)' is not comparable.
I was able to isolate where happened and recreate the error with these lines:
from sympy import *
x = Symbol("x")
Ymin = minimum(root(x,3), x, domain=Interval(-7,7))
I'm not undrestanding why it's happening, as when is root(x,2) it doasn't have this problem. Does anyone have a clue?
Okey i found an answer online posted by user6655984(stackoverflow) and i wanted to share it online:
expression = root(Abs(x), 3)*sign(x)
I know there is a very similar question and answer on stackoverflow (here), but this seems to be distinctly different. I am using statsmodels v 0.13.2, and I am using an ARIMA model as opposed to a SARIMAX model.
I am trying to fit a list of time series data sets with an ARIMA model. The offending piece of my code is here:
import numpy as np
from statsmodels.tsa.arima.model import ARIMA
items = np.log(og_items)
items['count'] = items['count'].apply(lambda x: 0 if math.isnan(x) or math.isinf(x) else x)
model = ARIMA(items, order=(14, 0, 7))
trained = model.fit()
items is a dataframe containing a date index and a single column, count.
I apply the lambda on the second line because some counts can be 0, resulting in a negative infinity after log is applied. The final product going into the ARIMA does not contain any NaNs or Infinite numbers. However, when I try this without using the log function, I do not get the error. This only occurs on certain series, but there does not seem to be rhyme or reason to which are affected. One series had about half of its values as zero after applying the lambda, while another did not have a single zero. Here is the error:
Traceback (most recent call last):
File "item_pipeline.py", line 267, in <module>
main()
File "item_pipeline.py", line 234, in main
restaurant_predictions = make_predictions(restaurant_data=restaurant_data, models=models,
File "item_pipeline.py", line 138, in make_predictions
predictions = model(*data_tuple[:2], min_date=min_date, max_date=max_date,
File "/Users/rob/Projects/5out-ml/models/item_level/items/predict_arima.py", line 127, in predict_daily_arima
predict_date_arima(prediction_dict, item_dict, prediction_date, x_days_out=x_days_out, log_vals=log_vals,
File "/Users/rob/Projects/5out-ml/models/item_level/items/predict_arima.py", line 51, in predict_date_arima
raise e
File "/Users/rob/Projects/5out-ml/models/item_level/items/predict_arima.py", line 47, in predict_date_arima
fitted = model.fit()
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/arima/model.py", line 390, in fit
res = super().fit(
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/statespace/mlemodel.py", line 704, in fit
mlefit = super(MLEModel, self).fit(start_params, method=method,
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/base/model.py", line 563, in fit
xopt, retvals, optim_settings = optimizer._fit(f, score, start_params,
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/base/optimizer.py", line 241, in _fit
xopt, retvals = func(objective, gradient, start_params, fargs, kwargs,
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/base/optimizer.py", line 651, in _fit_lbfgs
retvals = optimize.fmin_l_bfgs_b(func, start_params, maxiter=maxiter,
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_lbfgsb_py.py", line 199, in fmin_l_bfgs_b
res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_lbfgsb_py.py", line 362, in _minimize_lbfgsb
f, g = func_and_grad(x)
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py", line 286, in fun_and_grad
self._update_grad()
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py", line 256, in _update_grad
self._update_grad_impl()
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py", line 173, in update_grad
self.g = approx_derivative(fun_wrapped, self.x, f0=self.f,
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_numdiff.py", line 505, in approx_derivative
return _dense_difference(fun_wrapped, x0, f0, h,
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_numdiff.py", line 576, in _dense_difference
df = fun(x) - f0
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_numdiff.py", line 456, in fun_wrapped
f = np.atleast_1d(fun(x, *args, **kwargs))
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py", line 137, in fun_wrapped
fx = fun(np.copy(x), *args)
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/base/model.py", line 531, in f
return -self.loglike(params, *args) / nobs
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/statespace/mlemodel.py", line 939, in loglike
loglike = self.ssm.loglike(complex_step=complex_step, **kwargs)
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/statespace/kalman_filter.py", line 983, in loglike
kfilter = self._filter(**kwargs)
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/statespace/kalman_filter.py", line 903, in _filter
self._initialize_state(prefix=prefix, complex_step=complex_step)
File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/statespace/representation.py", line 983, in _initialize_state
self._statespaces[prefix].initialize(self.initialization,
File "statsmodels/tsa/statespace/_representation.pyx", line 1362, in statsmodels.tsa.statespace._representation.dStatespace.initialize
File "statsmodels/tsa/statespace/_initialization.pyx", line 288, in statsmodels.tsa.statespace._initialization.dInitialization.initialize
File "statsmodels/tsa/statespace/_initialization.pyx", line 406, in statsmodels.tsa.statespace._initialization.dInitialization.initialize_stationary_stationary_cov
File "statsmodels/tsa/statespace/_tools.pyx", line 1206, in statsmodels.tsa.statespace._tools._dsolve_discrete_lyapunov
numpy.linalg.LinAlgError: LU decomposition error.
The solution in the other stackoverflow post was to initialize the statespace differently. It looks like the statespace is involved, if you look at the last few lines of the error. However, it does not seem that that workflow is exposed in the newer version of statsmodels. Is it? If not, what else can I try to circumvent this error?
So far, I have tried manually initializing the model to approximate diffuse, and manually setting the initialize property to approximate diffuse. Neither seem to be valid in the new statsmodels code.
Turns out there's a new way to initialize. The second line below is the operative line.
model = ARIMA(items, order=(14, 0, 7))
model.initialize_approximate_diffuse() # this line
trained = model.fit()
I have a problem with time series analysis. I have a dataset with 5 features. Following is the subset of my input dataset:
date,price,year,day,totaltx
1/1/2016 0:00,434.46,2016,1,126762
1/2/2016 0:00,433.59,2016,2,147449
1/3/2016 0:00,430.36,2016,3,148661
1/4/2016 0:00,433.49,2016,4,185279
1/5/2016 0:00,432.25,2016,5,178723
1/6/2016 0:00,429.46,2016,6,184207
My endogenous data is price column and exogenous data is totaltx price.
This is the code I am running and getting an error:
import statsmodels.api as sm
import pandas as pd
import numpy as np
from numpy.linalg import LinAlgError
def arima(filteredData, coinOutput, window, horizon, trainLength):
start_index = 0
end_index = 0
inputNumber = filteredData.shape[0]
predictions = np.array([], dtype=np.float32)
prices = np.array([], dtype=np.float32)
# sliding on time series data with 1 day step
while ((end_index) < inputNumber - 1):
end_index = start_index + trainLength
trainFeatures = filteredData[start_index:end_index]["totaltx"]
trainOutput = coinOutput[start_index:end_index]["price"]
arima = sm.tsa.statespace.SARIMAX(endog=trainOutput.values, exog=trainFeatures.values, order=(window, 0, 0))
arima_fit = arima.fit(disp=0)
testdata=filteredData[end_index:end_index+1]["totaltx"]
total_sample = end_index-start_index
predicted = arima_fit.predict(start=total_sample, end=total_sample, exog=np.array(testdata.values).reshape(-1,1))
price = coinOutput[end_index:end_index + 1]["price"].values
predictions = np.append(predictions, predicted)
prices = np.append(prices, price)
start_index = start_index + 1
return predictions, prices
def processCoins(bitcoinPrice, window, horizon):
output = bitcoinPrice[horizon:][["date", "day", "year", "price"]]
return output
trainLength=100;
for window in [3,5]:
for horizon in [1,2,5,7,10]:
bitcoinPrice = pd.read_csv("..\\prices.csv", sep=",")
coinOutput = processCoins(bitcoinPrice, window, horizon)
predictions, prices = arima(bitcoinPrice, coinOutput, window, horizon, trainLength)
In this code, I am using rolling window regression technique. I am training arima for start_index:end_index and predicting the test data with end_index:end_index+1
This the error that is thrown from my code:
Traceback (most recent call last):
File "C:/PycharmProjects/coinLogPrediction/src/arima.py", line 115, in <module>
predictions, prices = arima(filteredBitcoinPrice, coinOutput, window, horizon, trainLength, outputFile)
File "C:/PycharmProjects/coinLogPrediction/src/arima.py", line 64, in arima
arima_fit = arima.fit(disp=0)
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\statsmodels\tsa\statespace\mlemodel.py", line 469, in fit
skip_hessian=True, **kwargs)
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\statsmodels\base\model.py", line 466, in fit
full_output=full_output)
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\statsmodels\base\optimizer.py", line 191, in _fit
hess=hessian)
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\statsmodels\base\optimizer.py", line 410, in _fit_lbfgs
**extra_kwargs)
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\scipy\optimize\lbfgsb.py", line 193, in fmin_l_bfgs_b
**opts)
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\scipy\optimize\lbfgsb.py", line 328, in _minimize_lbfgsb
f, g = func_and_grad(x)
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\scipy\optimize\lbfgsb.py", line 273, in func_and_grad
f = fun(x, *args)
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\scipy\optimize\optimize.py", line 292, in function_wrapper
return function(*(wrapper_args + args))
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\statsmodels\base\model.py", line 440, in f
return -self.loglike(params, *args) / nobs
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\statsmodels\tsa\statespace\mlemodel.py", line 646, in loglike
loglike = self.ssm.loglike(complex_step=complex_step, **kwargs)
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\statsmodels\tsa\statespace\kalman_filter.py", line 825, in loglike
kfilter = self._filter(**kwargs)
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\statsmodels\tsa\statespace\kalman_filter.py", line 747, in _filter
self._initialize_state(prefix=prefix, complex_step=complex_step)
File "C:\AppData\Local\Continuum\Anaconda3\lib\site-packages\statsmodels\tsa\statespace\representation.py", line 723, in _initialize_state
self._statespaces[prefix].initialize_stationary(complex_step)
File "_representation.pyx", line 1351, in statsmodels.tsa.statespace._representation.dStatespace.initialize_stationary
File "_tools.pyx", line 1151, in statsmodels.tsa.statespace._tools._dsolve_discrete_lyapunov
numpy.linalg.linalg.LinAlgError: LU decomposition error.
This looks like it might be a bug. In the meantime, you may be able to fix this by using a different initialization, like so:
arima = sm.tsa.statespace.SARIMAX(
endog=trainOutput.values, exog=trainFeatures.values, order=(window, 0, 0),
initialization='approximate_diffuse')
If you get a chance, please file a bug report at https://github.com/statsmodels/statsmodels/issues/new!
I had the same error.
Erroneous code:
mod = sm.tsa.SARIMAX(y, order=(0 1,0), seasonal_order=(1,0,0,12))
res = mod.fit()
This gave me error :
LinAlgError: Schur decomposition solver error
I was able to solve this error by passing argument enforce_stationarity=False:
mod = sm.tsa.SARIMAX(y, order=(0 1,0), seasonal_order=(1,0,0,12),enforce_stationarity=False)
res = mod.fit()
I am learning OpenMDAO with the first Paraboloid Tutorial. However, where I run the code with the constrained case (add_constraint(...)) I get the error: AttributeError: 'float' object has no attribute 'size'.
I just copied-pasted the code from the tutorial but I can not fine the error. Here is the code:
from __future__ import print_function
from openmdao.api import IndepVarComp, Component, Problem, Group
from openmdao.api import ScipyOptimizer
from openmdao.api import ExecComp
class Paraboloid(Component):
def __init__(self):
super(Paraboloid, self).__init__()
self.add_param('x', val=0.0)
self.add_param('y', val=0.0)
self.add_output('f_xy', shape=1)
def solve_nonlinear(self, params, unknowns, resids):
"""f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
"""
x = params['x']
y = params['y']
unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0
def linearize(self, params, unknowns, resids):
""" Jacobian for our paraboloid."""
x = params['x']
y = params['y']
J = {}
J['f_xy', 'x'] = 2.0*x - 6.0 + y
J['f_xy', 'y'] = 2.0*y + 8.0 + x
return J
if __name__ == "__main__":
top = Problem()
root = top.root = Group()
root.add('p1', IndepVarComp('x', 3.0))
root.add('p2', IndepVarComp('y', -4.0))
root.add('p', Paraboloid())
# Constraint Equation
root.add('con', ExecComp('c = x-y'))
root.connect('p1.x', 'p.x')
root.connect('p2.y', 'p.y')
root.connect('p.x', 'con.x')
root.connect('p.y', 'con.y')
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.add_desvar('p1.x', lower=-50, upper=50)
top.driver.add_desvar('p2.y', lower=-50, upper=50)
top.driver.add_objective('p.f_xy')
top.driver.add_constraint('con.c', lower=15.0)
top.setup()
top.run()
print('\n')
print('Minimum of %f found at (%f, %f)' % (top['p.f_xy'], top['p.x'], top['p.y']))
I run the script and I get the following:
##############################################
Setup: Checking root problem for potential issues...
No recorders have been specified, so no data will be saved.
Setup: Check of root problem complete.
##############################################
Traceback (most recent call last):
File "paraboloid.py", line 65, in <module>
top.run()
File "/home/sim/.local/lib/python2.7/site-packages/openmdao/core/problem.py", line 1151, in run
self.driver.run(self)
File "/home/sim/.local/lib/python2.7/site-packages/openmdao/drivers/scipy_optimizer.py", line 211, in run
options=self.opt_settings)
File "/home/sim/.local/lib/python2.7/site-packages/scipy/optimize/_minimize.py", line 458, in minimize
constraints, callback=callback, **options)
File "/home/sim/.local/lib/python2.7/site-packages/scipy/optimize/slsqp.py", line 390, in _minimize_slsqp
g = append(fprime(x),0.0)
File "/home/sim/.local/lib/python2.7/site-packages/scipy/optimize/optimize.py", line 292, in function_wrapper
return function(*(wrapper_args + args))
File "/home/sim/.local/lib/python2.7/site-packages/openmdao/drivers/scipy_optimizer.py", line 334, in _gradfunc
return_format='array')
File "/home/sim/.local/lib/python2.7/site-packages/openmdao/core/driver.py", line 834, in calc_gradient
sparsity=sparsity, inactives=inactives)
File "/home/sim/.local/lib/python2.7/site-packages/openmdao/core/problem.py", line 1310, in calc_gradient
inactives=inactives)
File "/home/sim/.local/lib/python2.7/site-packages/openmdao/core/problem.py", line 1561, in _calc_gradient_ln_solver
root._sys_linearize(root.params, unknowns, root.resids)
File "/home/sim/.local/lib/python2.7/site-packages/openmdao/core/system.py", line 947, in _sys_linearize
self._jacobian_cache = linearize(params, unknowns, resids)
File "/home/sim/.local/lib/python2.7/site-packages/openmdao/core/group.py", line 836, in linearize
sub._sys_linearize(sub.params, sub.unknowns, sub.resids)
File "/home/sim/.local/lib/python2.7/site-packages/openmdao/core/system.py", line 947, in _sys_linearize
self._jacobian_cache = linearize(params, unknowns, resids)
File "/home/sim/.local/lib/python2.7/site-packages/openmdao/components/exec_comp.py", line 273, in linearize
J[(u, param)] = numpy.zeros((jval.size, psize))
AttributeError: 'float' object has no attribute 'size'
The error message appear only when I put the constraint equation.
I believe it is a silly error but, can anyone point me the right direction?
Thank you in advance of your help !
This was actually a bug caused by a small change in numpy between 1.12 and 1.13. We have a fix up on the Master branch of the github repository now.
I think you may be using an older version of numpy, under which the imag method doesn't return an array. Can you try upgrading to a later version of numpy? I am using 1.12.0 and the code works for me.
I want to use then scan for a tensor3 calculation in my DNN, we can say this tensor3 is a 3D matrix, I want each layer of this matrix go through the T.nnet.softmax(T.dot(v, W)+b) calculation, the W, and b should be the fixed, so I could do the update for later. The following is my coding:
XS = T.tensor3("XS")
W = T.matrix(name="W")
b = T.vector(name="b")
results, updates = theano.scan(lambda XS: T.nnet.softmax(T.dot(XS,W) + b),
sequences=[XS],
outputs_info=None)
result = results
Mutiply = theano.function(inputs=[XS, W, b], outputs=[result])
#initialization of output of layer2
w_o = init_weights((1089, 10))
b_o = init_weights((10,))
myFile_data = h5py.File('/Users/XIN/Masterthesis/Torch_Script/mnist-cluttered/train_data.h5', 'r')
myFile_label= h5py.File('/Users/XIN/Masterthesis/Torch_Script/mnist-cluttered/train_target.h5', 'r')
data = myFile_data['data'][...]
label = myFile_label['target'][...]
data = data.reshape(100000, 10000)
trX = data
trY = label
X_s = downsampling(trX)
X_nine = load_data_train(trX, trX.shape[0], 9)
X_nine = X_nine.transpose((((2,0,1))))
p_x_nine = Mutiply(X_nine, w_o, b_o)[0]
but the results show this error:
runfile('/Users/XIN/Masterthesis/keras/thean_scan_test.py', wdir='/Users/XIN/Masterthesis/keras')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/XIN/anaconda/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 699, in runfile
execfile(filename, namespace)
File "/Users/XIN/anaconda/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py", line 81, in execfile
builtins.execfile(filename, *where)
File "/Users/XIN/Masterthesis/keras/thean_scan_test.py", line 115, in <module>
p_x_nine = Mutiply(X_nine, w_o, b_o)[0]
File "/Users/XIN/anaconda/lib/python2.7/site-packages/theano/compile/function_module.py", line 786, in __call__
allow_downcast=s.allow_downcast)
File "/Users/XIN/anaconda/lib/python2.7/site-packages/theano/tensor/type.py", line 86, in filter
'Expected an array-like object, but found a Variable: '
TypeError: ('Bad input argument to theano function with name "/Users/XIN/Masterthesis/keras/thean_scan_test.py:92" at index 1(0-based)', 'Expected an array-like object, but found a Variable: maybe you are trying to call a function on a (possibly shared) variable instead of a numeric array?')
I check the X_nine type is: np array but the w_o, and b_o is theano type.
So what should I do to modify this code.
get the solution by my mentor, coding it like following:
XS = T.tensor3("XS")
w_o = init_weights((1089, 10))
b_o = init_weights((10,))
results, updates = theano.scan(lambda XS: T.nnet.softmax(T.dot(XS,w_o) + b_o),
sequences=[XS],
outputs_info=None)
result = results
Mutiply = theano.function(inputs=[XS], outputs=[result])