73a5954fcf6bd3c48391306f93c98bb7dd48287f,GPflow/model.py,GPModel,predict_f_samples,#GPModel#Any#Any#,346

Before Change

        samples = []
        for i in range(self.num_latent):
            L = tf.cholesky(var[:,:,i])
            samples.append(mu[:,i:i+1] + tf.matmul(L, tf.random_normal(tf.pack([tf.shape(L)[0], num_samples]), dtype=tf.float64)))
        return tf.transpose(tf.pack(samples))

    @AutoFlow(tf.placeholder(tf.float64, [None, None]))

After Change

        for i in range(self.num_latent):
            L = tf.cholesky(var[:, :, i])
            shape = tf.pack([tf.shape(L)[0], num_samples])
            V = tf.random_normal(shape, dtype=tf.float64)
            samples.append(mu[:, i:i + 1] + tf.matmul(L, V))
        return tf.transpose(tf.pack(samples))

    @AutoFlow(tf.placeholder(tf.float64, [None, None]))

Instances