Is there a way to pass different sizes matrices as a sequence in
theano.scan()? For instance
X3 = T.tensor3(name='X3', dtype=theano.config.floatX)
theta = theano.shared(name='theta_temp',
value=np.array([0.1, 0.2, 0.3],
dtype=theano.config.floatX))
x_temp = np.array([[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]],
[[2, 3, 6],
[4, 7, 0],
[0, 0, 1]]])
y_temp = np.array([[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]],
[[2, 3, 6],
[4, 7, 0]]])
def foo(inputs):
hyp = T.dot(inputs, theta.T)
return hyp
results, _ = theano.scan(fn=cost2,
sequences=[X3])
f = theano.function(inputs=[X3], outputs=results,
allow_input_downcast=True)
print(f(x_temp)) # This works
print('----------')
print(f(y_temp)) # This does not work
I noticed that `x_temp` works every entry is of the same size, whereas
`y_temp` will yield an error, namely
ValueError: ('Bad input argument to theano function with name
"C:\\...\\Linear_Regression_theano_test.py:144" at index 0(0-based)',
'setting an array element with a sequence.')
now the only difference between `x_temp` and `y_temp` is that the last
matrix in `y_temp` is of size `2x3` instead of `3x3` and this seems to be
causing a problem.
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