Merge branch '414-include-crps-analysis-and-other-ens-verif-methods-or-plots'...

Merge branch '414-include-crps-analysis-and-other-ens-verif-methods-or-plots' of ssh://gitlab.version.fz-juelich.de:10022/esde/machine-learning/mlair into 414-include-crps-analysis-and-other-ens-verif-methods-or-plots

mlair/model_modules/probability_models.py

@@ -1369,28 +1369,37 @@ class MyUnetProbMulti(AbstractModelClass):
         # )
 
         # params_size = tfpl.IndependentNormal.params_size(self._output_shape)
+
+
+        if self.k_mixed_components is None:
+            # Mulit B
             params_size = tfpl.MultivariateNormalTriL.params_size(self._output_shape)
 
             pars = tf.keras.layers.Dense(params_size)(dl)
-        # pars = DenseVariationalCustom(
-        #     units=params_size, make_prior_fn=prior, make_posterior_fn=posterior,
-        #     kl_use_exact=True, kl_weight=1./self.x_train_shape)(dl)
-
-        # outputs = tfpl.MixtureSameFamily(self.k_mixed_components,
-        #                                 tfpl.MultivariateNormalTriL(
-        #                                     self._output_shape,
-        #                                     convert_to_tensor_fn=tfp.distributions.Distribution.mode
-        #                                 )
-        #                                 )(pars)
 
             outputs = tfpl.MultivariateNormalTriL(
                 self._output_shape,
                 convert_to_tensor_fn=tfp.distributions.Distribution.mode
             )(pars)
+            # Multi E
+        else:
 
-        # outputs = tfpl.IndependentNormal(
-        #         self._output_shape
-        #         )(pars)
+            # Mix B
+            params_size = tfpl.MixtureSameFamily.params_size(
+                self.k_mixed_components,
+                component_params_size=tfpl.MultivariateNormalTriL.params_size(self._output_shape)
+            )
+
+            pars = tf.keras.layers.Dense(params_size)(dl)
+            #    tfpl.MultivariateNormalTriL(self._output_shape,
+            #                           convert_to_tensor_fn=tfp.distributions.Distribution.mode
+            #                           )
+            outputs = tfpl.MixtureSameFamily(self.k_mixed_components,
+                                             tfpl.MultivariateNormalTriL(
+                                                 self._output_shape,
+                                                 convert_to_tensor_fn=tfp.distributions.Distribution.mode
+                                             ))(pars)
+            # Mix E
 
         self.model = keras.Model(inputs=input_train, outputs=outputs)