From fe093237ede54aa1a271baeeeb7399b40c133a49 Mon Sep 17 00:00:00 2001
From: Job333 <joboosterbaan@hotmail.com>
Date: Sun, 22 Jul 2018 12:27:52 +0200
Subject: [PATCH] Declared X_in as dtype='float32' in autoencoder.py
train_op ran into trouble as default theano.tensor.matrix is of type float64 while all other variables are of type float32.
Declaring X_in as dtype='float32' fixes the problem.
---
unsupervised_class2/autoencoder.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/unsupervised_class2/autoencoder.py b/unsupervised_class2/autoencoder.py
index 87633fa1..4e9b85eb 100644
--- a/unsupervised_class2/autoencoder.py
+++ b/unsupervised_class2/autoencoder.py
@@ -60,7 +60,7 @@ def fit(self, X, learning_rate=0.5, mu=0.99, epochs=1, batch_sz=100, show_fig=Fa
self.dparams = [self.dW, self.dbh, self.dbo]
self.forward_dparams = [self.dW, self.dbh]
- X_in = T.matrix('X_%s' % self.id)
+ X_in = T.matrix('X_%s' % self.id, dtype='float32')
X_hat = self.forward_output(X_in)
# attach it to the object so it can be used later
@@ -177,7 +177,7 @@ def fit(self, X, Y, Xtest, Ytest,
for ae in self.hidden_layers:
self.params += ae.forward_params
- X_in = T.matrix('X_in')
+ X_in = T.matrix('X_in', dtype='float32')
targets = T.ivector('Targets')
pY = self.forward(X_in)