Hi everyone I've been some trouble lately trying to understand why the hell
theano keeps changing my variable types without being explicitly instructed
to do so.
I've enforced in my .theanorc to use float32 and also to raise any warning
in case of use float64 via the flag warn_float64=raise.
Here is a minimal example which I cannot understand where the change of
variable type is coming from.
import numpy as np
import theano
from theano import tensor as T
floatX = theano.config.floatX
N = 400
feats = 784
n_classes = 2
D = (np.random.randn(N, feats), np.random.randint(size=N, low=0, high=2))
training_steps = 10000
X = T.fmatrix("X")
y = T.ivector("y")
W = theano.shared(np.random.randn(feats, n_classes).astype(dtype=floatX),
name="W")
b = theano.shared(np.zeros(W.get_value().shape[1]).astype(dtype=floatX),
name="b")
p_1 = 1./(1 + T.exp(-T.dot(X, W) - b))
prediction = p_1 > 0.5
cost = T.mean(-y * T.log(p_1) - (1-y) * T.log(1-p_1),
dtype=floatX, acc_dtype=floatX)
cost += 0.01 * T.sum(W ** 2, dtype=floatX)
gW, gb = T.grad(cost, [W, b])
train = theano.function(inputs=[X, y], outputs=[prediction, cost],
updates=((W, W - 0.1 * gW), (b, b - 0.1 * gb)))
predict = theano.function(inputs=[X], outputs=prediction)
for i in range(training_steps):
pred, err = train(D[0], D[1])
print("Final model:")
print(W.get_value())
print(b.get_value())
print("target values for D:")
print(D[1])
print("prediction on D:")
print(predict(D[0]))
I always get an exception:
Exception: You are creating a TensorVariable with float64 dtype. You
requested an action via the Theano flag
warn_float64={ignore,warn,raise,pdf}.
Which points to the cost regarding the cross entropy error.
But I've explicitly told to use float32 both for the inner summation
calculation in mean operator and and also to cast the output as float32.
I don't understand where this exception is coming from?
Pretty much everything as been set up to float32.
Any help much appreciated?
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