move to floatX

Author: ferrine

pymc3/tests/test_variational_inference.py

@@ -230,26 +230,26 @@ def test_replacements_in_sample_node_aevb(three_var_aevb_approx, aevb_initial):
 
 def test_vae():
     minibatch_size = 10
-    data = np.random.rand(100).astype('float32')
+    data = pm.floatX(np.random.rand(100))
     x_mini = pm.Minibatch(data, minibatch_size)
     x_inp = tt.vector()
     x_inp.tag.test_value = data[:minibatch_size]
 
-    ae = theano.shared(np.asarray([.1, .1], 'float32'))
-    be = theano.shared(np.asarray(1., dtype='float32'))
+    ae = theano.shared(pm.floatX([.1, .1]))
+    be = theano.shared(pm.floatX(1.))
 
-    ad = theano.shared(np.asarray(1., dtype='float32'))
-    bd = theano.shared(np.asarray(1., dtype='float32'))
+    ad = theano.shared(pm.floatX(1.))
+    bd = theano.shared(pm.floatX(1.))
 
     enc = x_inp.dimshuffle(0, 'x') * ae.dimshuffle('x', 0) + be
     mu,  rho = enc[:, 0], enc[:, 1]
 
     with pm.Model():
         # Hidden variables
-        zs = pm.Normal('zs', mu=0, sd=1, shape=minibatch_size, dtype='float32')
+        zs = pm.Normal('zs', mu=0, sd=1, shape=minibatch_size)
         dec = zs * ad + bd
         # Observation model
-        pm.Normal('xs_', mu=dec, sd=0.1, observed=x_inp, dtype='float32')
+        pm.Normal('xs_', mu=dec, sd=0.1, observed=x_inp)
 
         pm.fit(1, local_rv={zs: dict(mu=mu, rho=rho)},
                more_replacements={x_inp: x_mini}, more_obj_params=[ae, be, ad, bd])