@@ -126,7 +126,7 @@ def sample_h_given_v(self, v0_sample):
126126 # Note that theano_rng.binomial returns a symbolic sample of dtype
127127 # int64 by default. If we want to keep our computations in floatX
128128 # for the GPU we need to specify to return the dtype floatX
129- h1_sample = self .theano_rng .binomial (size = h1_mean .shape , n = 1 , prob = h1_mean ,
129+ h1_sample = self .theano_rng .binomial (size = h1_mean .shape , n = 1 , p = h1_mean ,
130130 dtype = theano .config .floatX )
131131 return [pre_sigmoid_h1 , h1_mean , h1_sample ]
132132
@@ -150,7 +150,7 @@ def sample_v_given_h(self, h0_sample):
150150 # Note that theano_rng.binomial returns a symbolic sample of dtype
151151 # int64 by default. If we want to keep our computations in floatX
152152 # for the GPU we need to specify to return the dtype floatX
153- v1_sample = self .theano_rng .binomial (size = v1_mean .shape ,n = 1 ,prob = v1_mean ,
153+ v1_sample = self .theano_rng .binomial (size = v1_mean .shape ,n = 1 ,p = v1_mean ,
154154 dtype = theano .config .floatX )
155155 return [pre_sigmoid_v1 , v1_mean , v1_sample ]
156156
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