diff --git a/GPy/core/gp.py b/GPy/core/gp.py
index 453de407..ab7e8f9d 100644
--- a/GPy/core/gp.py
+++ b/GPy/core/gp.py
@@ -110,7 +110,14 @@ class GP(Model):
         self.posterior = None
 
     def to_dict(self, save_data=True):
-        input_dict = super(GP, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :param boolean save_data: if true, it adds the training data self.X and self.Y to the dictionary
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+        input_dict = super(GP, self)._save_to_input_dict()
         input_dict["class"] = "GPy.core.GP"
         if not save_data:
             input_dict["X"] = None
@@ -137,7 +144,7 @@ class GP(Model):
         return input_dict
 
     @staticmethod
-    def _from_dict(input_dict, data=None):
+    def _build_from_input_dict(input_dict, data=None):
         import GPy
         import numpy as np
         if (input_dict['X'] is None) or (input_dict['Y'] is None):
@@ -282,7 +289,7 @@ class GP(Model):
             mu += self.mean_function.f(Xnew)
         return mu, var
 
-    def predict(self, Xnew, full_cov=False, Y_metadata=None, kern=None, 
+    def predict(self, Xnew, full_cov=False, Y_metadata=None, kern=None,
                 likelihood=None, include_likelihood=True):
         """
         Predict the function(s) at the new point(s) Xnew. This includes the
diff --git a/GPy/core/mapping.py b/GPy/core/mapping.py
index d8032c54..31e4d8bb 100644
--- a/GPy/core/mapping.py
+++ b/GPy/core/mapping.py
@@ -28,7 +28,7 @@ class Mapping(Parameterized):
     def to_dict(self):
         raise NotImplementedError
 
-    def _to_dict(self):
+    def _save_to_input_dict(self):
         input_dict = {}
         input_dict["input_dim"] = self.input_dim
         input_dict["output_dim"] = self.output_dim
@@ -37,16 +37,27 @@ class Mapping(Parameterized):
 
     @staticmethod
     def from_dict(input_dict):
+        """
+        Instantiate an object of a derived class using the information
+        in input_dict (built by the to_dict method of the derived class).
+        More specifically, after reading the derived class from input_dict,
+        it calls the method _build_from_input_dict of the derived class.
+        Note: This method should not be overrided in the derived class. In case
+        it is needed, please override _build_from_input_dict instate.
+
+        :param dict input_dict: Dictionary with all the information needed to
+           instantiate the object.
+        """
         import copy
         input_dict = copy.deepcopy(input_dict)
         mapping_class = input_dict.pop('class')
         input_dict["name"] = str(input_dict["name"])
         import GPy
         mapping_class = eval(mapping_class)
-        return mapping_class._from_dict(mapping_class, input_dict)
+        return mapping_class._build_from_input_dict(mapping_class, input_dict)
 
     @staticmethod
-    def _from_dict(mapping_class, input_dict):
+    def _build_from_input_dict(mapping_class, input_dict):
         return mapping_class(**input_dict)
 
 
diff --git a/GPy/core/model.py b/GPy/core/model.py
index 799d42bd..b57e1a77 100644
--- a/GPy/core/model.py
+++ b/GPy/core/model.py
@@ -8,7 +8,10 @@ class Model(ParamzModel, Priorizable):
     def __init__(self, name):
         super(Model, self).__init__(name)  # Parameterized.__init__(self)
 
-    def _to_dict(self):
+    def _save_to_input_dict(self):
+        """
+        It is used by the public method to_dict to create json serializable dictionary.
+        """
         input_dict = {}
         input_dict["name"] = self.name
         return input_dict
@@ -18,16 +21,37 @@ class Model(ParamzModel, Priorizable):
 
     @staticmethod
     def from_dict(input_dict, data=None):
+        """
+        Instantiate an object of a derived class using the information
+        in input_dict (built by the to_dict method of the derived class).
+        More specifically, after reading the derived class from input_dict,
+        it calls the method _build_from_input_dict of the derived class.
+        Note: This method should not be overrided in the derived class. In case
+        it is needed, please override _build_from_input_dict instate.
+
+        :param dict input_dict: Dictionary with all the information needed to
+           instantiate the object.
+        """
         import copy
         input_dict = copy.deepcopy(input_dict)
         model_class = input_dict.pop('class')
         input_dict["name"] = str(input_dict["name"])
         import GPy
         model_class = eval(model_class)
-        return model_class._from_dict(input_dict, data)
+        return model_class._build_from_input_dict(input_dict, data)
 
     @staticmethod
-    def _from_dict(model_class, input_dict, data=None):
+    def _build_from_input_dict(model_class, input_dict, data=None):
+        """
+        This method is used by the public method from_dict to build an object
+        of class model_class using the information contained in input_dict.
+        Note: This method is often overrided in the derived class to deal with
+        any pre-processing of the parameters in input_dict before calling the
+        constructor of the object.
+
+        :param str model_class: Class of the object to build.
+        :param dict input_dict: Extra information needed by the constructor of model_class.
+        """
         return model_class(**input_dict)
 
     def save_model(self, output_filename, compress=True, save_data=True):
diff --git a/GPy/core/sparse_gp.py b/GPy/core/sparse_gp.py
index 4c7e98c4..eb3650af 100644
--- a/GPy/core/sparse_gp.py
+++ b/GPy/core/sparse_gp.py
@@ -117,3 +117,50 @@ class SparseGP(GP):
             self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
             self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)
         self._Zgrad = self.Z.gradient.copy()
+
+    def to_dict(self, save_data=True):
+        """
+        Convert the object into a json serializable dictionary.
+
+        :param boolean save_data: if true, it adds the training data self.X and self.Y to the dictionary
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+        input_dict = super(SparseGP, self).to_dict(save_data)
+        input_dict["class"] = "GPy.core.SparseGP"
+        input_dict["Z"] = self.Z.tolist()
+        return input_dict
+
+    @staticmethod
+    def _build_from_input_dict(input_dict, data=None):
+        # Called from the from_dict method.
+        import GPy
+        if (input_dict['X'] is None) or (input_dict['Y'] is None):
+            if data is None:
+                raise ValueError("The model was serialized whithout the training data. 'data' must be not None!")
+            input_dict["X"], input_dict["Y"] = np.array(data[0]), np.array(data[1])
+        elif data is not None:
+            print("WARNING: The model has been saved with X,Y! The original values are being overriden!")
+            input_dict["X"], input_dict["Y"] = np.array(data[0]), np.array(data[1])
+        else:
+            input_dict["X"], input_dict["Y"] = np.array(input_dict['X']), np.array(input_dict['Y'])
+
+        input_dict["Z"] = np.array(input_dict['Z'])
+        input_dict["kernel"] = GPy.kern.Kern.from_dict(input_dict["kernel"])
+        input_dict["likelihood"] = GPy.likelihoods.likelihood.Likelihood.from_dict(input_dict["likelihood"])
+        mean_function = input_dict.get("mean_function")
+        if mean_function is not None:
+            input_dict["mean_function"] = GPy.core.mapping.Mapping.from_dict(mean_function)
+        else:
+            input_dict["mean_function"] = mean_function
+        input_dict["inference_method"] = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(input_dict["inference_method"])
+
+        #FIXME: Assumes the Y_metadata is serializable. We should create a Metadata class
+        Y_metadata = input_dict.get("Y_metadata")
+        input_dict["Y_metadata"] = Y_metadata
+
+        normalizer = input_dict.get("normalizer")
+        if normalizer is not None:
+            input_dict["normalizer"] = GPy.util.normalizer._Norm.from_dict(normalizer)
+        else:
+            input_dict["normalizer"] = normalizer
+        return SparseGP(**input_dict)
diff --git a/GPy/inference/latent_function_inference/__init__.py b/GPy/inference/latent_function_inference/__init__.py
index 97815a41..23742e6a 100644
--- a/GPy/inference/latent_function_inference/__init__.py
+++ b/GPy/inference/latent_function_inference/__init__.py
@@ -41,7 +41,7 @@ class LatentFunctionInference(object):
         """
         pass
 
-    def _to_dict(self):
+    def _save_to_input_dict(self):
         input_dict = {}
         return input_dict
 
@@ -50,15 +50,27 @@ class LatentFunctionInference(object):
 
     @staticmethod
     def from_dict(input_dict):
+        """
+        Instantiate an object of a derived class using the information
+        in input_dict (built by the to_dict method of the derived class).
+        More specifically, after reading the derived class from input_dict,
+        it calls the method _build_from_input_dict of the derived class.
+        Note: This method should not be overrided in the derived class. In case
+        it is needed, please override _build_from_input_dict instate.
+
+        :param dict input_dict: Dictionary with all the information needed to
+           instantiate the object.
+        """
+
         import copy
         input_dict = copy.deepcopy(input_dict)
         inference_class = input_dict.pop('class')
         import GPy
         inference_class = eval(inference_class)
-        return inference_class._from_dict(inference_class, input_dict)
+        return inference_class._build_from_input_dict(inference_class, input_dict)
 
     @staticmethod
-    def _from_dict(inference_class, input_dict):
+    def _build_from_input_dict(inference_class, input_dict):
         return inference_class(**input_dict)
 
 class InferenceMethodList(LatentFunctionInference, list):
diff --git a/GPy/inference/latent_function_inference/exact_gaussian_inference.py b/GPy/inference/latent_function_inference/exact_gaussian_inference.py
index a0a35fa5..569dbf3b 100644
--- a/GPy/inference/latent_function_inference/exact_gaussian_inference.py
+++ b/GPy/inference/latent_function_inference/exact_gaussian_inference.py
@@ -22,7 +22,15 @@ class ExactGaussianInference(LatentFunctionInference):
         pass#self._YYTfactor_cache = caching.cache()
 
     def to_dict(self):
-        input_dict = super(ExactGaussianInference, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(ExactGaussianInference, self)._save_to_input_dict()
         input_dict["class"] = "GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference"
         return input_dict
 
diff --git a/GPy/inference/latent_function_inference/expectation_propagation.py b/GPy/inference/latent_function_inference/expectation_propagation.py
index 61d3feff..2a558b5b 100644
--- a/GPy/inference/latent_function_inference/expectation_propagation.py
+++ b/GPy/inference/latent_function_inference/expectation_propagation.py
@@ -28,6 +28,14 @@ class cavityParams(object):
         self.tau[i] = 1./post_params.Sigma_diag[i] - eta*ga_approx.tau[i]
         self.v[i] = post_params.mu[i]/post_params.Sigma_diag[i] - eta*ga_approx.v[i]
     def to_dict(self):
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
         return {"tau": self.tau.tolist(), "v": self.v.tolist()}
     @staticmethod
     def from_dict(input_dict):
@@ -59,6 +67,14 @@ class gaussianApproximation(object):
 
         return (delta_tau, delta_v)
     def to_dict(self):
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
         return {"tau": self.tau.tolist(), "v": self.v.tolist()}
     @staticmethod
     def from_dict(input_dict):
@@ -89,6 +105,14 @@ class posteriorParams(posteriorParamsBase):
         DSYR(self.Sigma, si, -ci)
 
     def to_dict(self):
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
         #TODO: Implement a more memory efficient variant
         if self.L is None:
             return { "mu": self.mu.tolist(), "Sigma": self.Sigma.tolist()}
@@ -133,6 +157,14 @@ class posteriorParamsDTC(posteriorParamsBase):
         #mu = np.dot(Sigma, v_tilde)
 
     def to_dict(self):
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
         return { "mu": self.mu.tolist(), "Sigma_diag": self.Sigma_diag.tolist()}
 
     @staticmethod
@@ -205,8 +237,8 @@ class EPBase(object):
     def __getstate__(self):
         return [super(EPBase, self).__getstate__() , [self.epsilon, self.eta, self.delta]]
 
-    def _to_dict(self):
-        input_dict = super(EPBase, self)._to_dict()
+    def _save_to_input_dict(self):
+        input_dict = super(EPBase, self)._save_to_input_dict()
         input_dict["epsilon"]=self.epsilon
         input_dict["eta"]=self.eta
         input_dict["delta"]=self.delta
@@ -370,7 +402,15 @@ class EP(EPBase, ExactGaussianInference):
         return Posterior(woodbury_inv=Wi, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK, 'dL_dthetaL':dL_dthetaL, 'dL_dm':alpha}
 
     def to_dict(self):
-        input_dict = super(EP, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(EP, self)._save_to_input_dict()
         input_dict["class"] = "GPy.inference.latent_function_inference.expectation_propagation.EP"
         if self.ga_approx_old is not  None:
             input_dict["ga_approx_old"] = self.ga_approx_old.to_dict()
@@ -384,7 +424,7 @@ class EP(EPBase, ExactGaussianInference):
         return input_dict
 
     @staticmethod
-    def _from_dict(inference_class, input_dict):
+    def _build_from_input_dict(inference_class, input_dict):
         ga_approx_old = input_dict.pop('ga_approx_old', None)
         if ga_approx_old is not None:
             ga_approx_old = gaussianApproximation.from_dict(ga_approx_old)
@@ -402,7 +442,7 @@ class EP(EPBase, ExactGaussianInference):
 
 class EPDTC(EPBase, VarDTC):
     def inference(self, kern, X, Z, likelihood, Y, mean_function=None, Y_metadata=None, Lm=None, dL_dKmm=None, psi0=None, psi1=None, psi2=None):
-        if self.always_reset:
+        if self.always_reset and not self.loading:
             self.reset()
 
         num_data, output_dim = Y.shape
@@ -420,11 +460,11 @@ class EPDTC(EPBase, VarDTC):
         else:
             Kmn = psi1.T
 
-        if self.ep_mode=="nested":
+        if self.ep_mode=="nested" and not self.loading:
             #Force EP at each step of the optimization
             self._ep_approximation = None
             post_params, ga_approx, log_Z_tilde = self._ep_approximation = self.expectation_propagation(Kmm, Kmn, Y, likelihood, Y_metadata)
-        elif self.ep_mode=="alternated":
+        elif self.ep_mode=="alternated" or self.loading:
             if getattr(self, '_ep_approximation', None) is None:
                 #if we don't yet have the results of runnign EP, run EP and store the computed factors in self._ep_approximation
                 post_params, ga_approx, log_Z_tilde = self._ep_approximation = self.expectation_propagation(Kmm, Kmn, Y, likelihood, Y_metadata)
@@ -434,6 +474,8 @@ class EPDTC(EPBase, VarDTC):
         else:
             raise ValueError("ep_mode value not valid")
 
+        self.loading = False
+
         mu_tilde = ga_approx.v / ga_approx.tau.astype(float)
 
         return super(EPDTC, self).inference(kern, X, Z, likelihood, ObsAr(mu_tilde[:,None]),
@@ -543,7 +585,15 @@ class EPDTC(EPBase, VarDTC):
 
 
     def to_dict(self):
-        input_dict = super(EPDTC, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(EPDTC, self)._save_to_input_dict()
         input_dict["class"] = "GPy.inference.latent_function_inference.expectation_propagation.EPDTC"
         if self.ga_approx_old is not  None:
             input_dict["ga_approx_old"] = self.ga_approx_old.to_dict()
@@ -551,13 +601,12 @@ class EPDTC(EPBase, VarDTC):
             input_dict["_ep_approximation"] = {}
             input_dict["_ep_approximation"]["post_params"] = self._ep_approximation[0].to_dict()
             input_dict["_ep_approximation"]["ga_approx"] = self._ep_approximation[1].to_dict()
-            input_dict["_ep_approximation"]["cav_params"] = self._ep_approximation[2].to_dict()
-            input_dict["_ep_approximation"]["log_Z_tilde"] = self._ep_approximation[3].tolist()
+            input_dict["_ep_approximation"]["log_Z_tilde"] = self._ep_approximation[2]
 
         return input_dict
 
     @staticmethod
-    def _from_dict(inference_class, input_dict):
+    def _build_from_input_dict(inference_class, input_dict):
         ga_approx_old = input_dict.pop('ga_approx_old', None)
         if ga_approx_old is not None:
             ga_approx_old = gaussianApproximation.from_dict(ga_approx_old)
@@ -566,8 +615,7 @@ class EPDTC(EPBase, VarDTC):
         if _ep_approximation is not None:
             _ep_approximation.append(posteriorParamsDTC.from_dict(_ep_approximation_dict["post_params"]))
             _ep_approximation.append(gaussianApproximation.from_dict(_ep_approximation_dict["ga_approx"]))
-            _ep_approximation.append(cavityParams.from_dict(_ep_approximation_dict["cav_params"]))
-            _ep_approximation.append(np.array(_ep_approximation_dict["log_Z_tilde"]))
+            _ep_approximation.append(_ep_approximation_dict["log_Z_tilde"])
         ee = EPDTC(**input_dict)
         ee.ga_approx_old = ga_approx_old
         ee._ep_approximation = _ep_approximation
diff --git a/GPy/kern/src/add.py b/GPy/kern/src/add.py
index c1834f76..3fc34883 100644
--- a/GPy/kern/src/add.py
+++ b/GPy/kern/src/add.py
@@ -44,7 +44,15 @@ class Add(CombinationKernel):
             return False
 
     def to_dict(self):
-        input_dict = super(Add, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(Add, self)._save_to_input_dict()
         input_dict["class"] = str("GPy.kern.Add")
         return input_dict
 
diff --git a/GPy/kern/src/kern.py b/GPy/kern/src/kern.py
index c08489e2..8da3fbfb 100644
--- a/GPy/kern/src/kern.py
+++ b/GPy/kern/src/kern.py
@@ -60,7 +60,7 @@ class Kern(Parameterized):
         from .psi_comp import PSICOMP_GH
         self.psicomp = PSICOMP_GH()
 
-    def _to_dict(self):
+    def _save_to_input_dict(self):
         input_dict = {}
         input_dict["input_dim"] = self.input_dim
         if isinstance(self.active_dims, np.ndarray):
@@ -76,16 +76,28 @@ class Kern(Parameterized):
 
     @staticmethod
     def from_dict(input_dict):
+        """
+        Instantiate an object of a derived class using the information
+        in input_dict (built by the to_dict method of the derived class).
+        More specifically, after reading the derived class from input_dict,
+        it calls the method _build_from_input_dict of the derived class.
+        Note: This method should not be overrided in the derived class. In case
+        it is needed, please override _build_from_input_dict instate.
+
+        :param dict input_dict: Dictionary with all the information needed to
+           instantiate the object.
+        """
+
         import copy
         input_dict = copy.deepcopy(input_dict)
         kernel_class = input_dict.pop('class')
         input_dict["name"] = str(input_dict["name"])
         import GPy
         kernel_class = eval(kernel_class)
-        return kernel_class._from_dict(kernel_class, input_dict)
+        return kernel_class._build_from_input_dict(kernel_class, input_dict)
 
     @staticmethod
-    def _from_dict(kernel_class, input_dict):
+    def _build_from_input_dict(kernel_class, input_dict):
         return kernel_class(**input_dict)
 
 
@@ -375,15 +387,15 @@ class CombinationKernel(Kern):
         if link_parameters:
             self.link_parameters(*kernels)
 
-    def _to_dict(self):
-        input_dict = super(CombinationKernel, self)._to_dict()
+    def _save_to_input_dict(self):
+        input_dict = super(CombinationKernel, self)._save_to_input_dict()
         input_dict["parts"]  = {}
         for ii in range(len(self.parts)):
             input_dict["parts"][ii] = self.parts[ii].to_dict()
         return input_dict
 
     @staticmethod
-    def _from_dict(kernel_class, input_dict):
+    def _build_from_input_dict(kernel_class, input_dict):
         parts = input_dict.pop('parts', None)
         subkerns = []
         for pp in parts:
diff --git a/GPy/kern/src/linear.py b/GPy/kern/src/linear.py
index 10edb4c2..bf75f201 100644
--- a/GPy/kern/src/linear.py
+++ b/GPy/kern/src/linear.py
@@ -52,14 +52,14 @@ class Linear(Kern):
         self.psicomp = PSICOMP_Linear()
 
     def to_dict(self):
-        input_dict = super(Linear, self)._to_dict()
+        input_dict = super(Linear, self)._save_to_input_dict()
         input_dict["class"] = "GPy.kern.Linear"
         input_dict["variances"] = self.variances.values.tolist()
         input_dict["ARD"] = self.ARD
         return input_dict
 
     @staticmethod
-    def _from_dict(kernel_class, input_dict):
+    def _build_from_input_dict(kernel_class, input_dict):
         useGPU = input_dict.pop('useGPU', None)
         return Linear(**input_dict)
 
diff --git a/GPy/kern/src/prod.py b/GPy/kern/src/prod.py
index 31e62392..6bf5f9f3 100644
--- a/GPy/kern/src/prod.py
+++ b/GPy/kern/src/prod.py
@@ -43,7 +43,15 @@ class Prod(CombinationKernel):
         super(Prod, self).__init__(_newkerns, name)
 
     def to_dict(self):
-        input_dict = super(Prod, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(Prod, self)._save_to_input_dict()
         input_dict["class"] = str("GPy.kern.Prod")
         return input_dict
 
diff --git a/GPy/kern/src/rbf.py b/GPy/kern/src/rbf.py
index 0b6730d8..c17345d8 100644
--- a/GPy/kern/src/rbf.py
+++ b/GPy/kern/src/rbf.py
@@ -32,7 +32,15 @@ class RBF(Stationary):
             self.link_parameter(self.inv_l)
 
     def to_dict(self):
-        input_dict = super(RBF, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(RBF, self)._save_to_input_dict()
         input_dict["class"] = "GPy.kern.RBF"
         input_dict["inv_l"] = self.use_invLengthscale
         if input_dict["inv_l"] == True:
diff --git a/GPy/kern/src/standard_periodic.py b/GPy/kern/src/standard_periodic.py
index 201f8d19..e7b67239 100644
--- a/GPy/kern/src/standard_periodic.py
+++ b/GPy/kern/src/standard_periodic.py
@@ -94,7 +94,15 @@ class StdPeriodic(Kern):
         self.link_parameters(self.variance,  self.period, self.lengthscale)
 
     def to_dict(self):
-        input_dict = super(StdPeriodic, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(StdPeriodic, self)._save_to_input_dict()
         input_dict["class"] = "GPy.kern.StdPeriodic"
         input_dict["variance"] = self.variance.values.tolist()
         input_dict["period"] = self.period.values.tolist()
diff --git a/GPy/kern/src/static.py b/GPy/kern/src/static.py
index f7042cc1..a4831107 100644
--- a/GPy/kern/src/static.py
+++ b/GPy/kern/src/static.py
@@ -14,8 +14,8 @@ class Static(Kern):
         self.variance = Param('variance', variance, Logexp())
         self.link_parameters(self.variance)
 
-    def _to_dict(self):
-        input_dict = super(Static, self)._to_dict()
+    def _save_to_input_dict(self):
+        input_dict = super(Static, self)._save_to_input_dict()
         input_dict["variance"] =  self.variance.values.tolist()
         return input_dict
 
@@ -139,12 +139,12 @@ class Bias(Static):
         super(Bias, self).__init__(input_dim, variance, active_dims, name)
 
     def to_dict(self):
-        input_dict = super(Bias, self)._to_dict()
+        input_dict = super(Bias, self)._save_to_input_dict()
         input_dict["class"] = "GPy.kern.Bias"
         return input_dict
 
     @staticmethod
-    def _from_dict(kernel_class, input_dict):
+    def _build_from_input_dict(kernel_class, input_dict):
         useGPU = input_dict.pop('useGPU', None)
         return Bias(**input_dict)
 
diff --git a/GPy/kern/src/stationary.py b/GPy/kern/src/stationary.py
index 81129a75..cf3f5dff 100644
--- a/GPy/kern/src/stationary.py
+++ b/GPy/kern/src/stationary.py
@@ -79,8 +79,8 @@ class Stationary(Kern):
         assert self.variance.size==1
         self.link_parameters(self.variance, self.lengthscale)
 
-    def _to_dict(self):
-        input_dict = super(Stationary, self)._to_dict()
+    def _save_to_input_dict(self):
+        input_dict = super(Stationary, self)._save_to_input_dict()
         input_dict["variance"] =  self.variance.values.tolist()
         input_dict["lengthscale"] = self.lengthscale.values.tolist()
         input_dict["ARD"] = self.ARD
@@ -366,12 +366,20 @@ class Exponential(Stationary):
         return -self.K_of_r(r)
 
     def to_dict(self):
-        input_dict = super(Exponential, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(Exponential, self)._save_to_input_dict()
         input_dict["class"] = "GPy.kern.Exponential"
         return input_dict
 
     @staticmethod
-    def _from_dict(kernel_class, input_dict):
+    def _build_from_input_dict(kernel_class, input_dict):
         useGPU = input_dict.pop('useGPU', None)
         return Exponential(**input_dict)
 
@@ -424,12 +432,20 @@ class Matern32(Stationary):
         super(Matern32, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
 
     def to_dict(self):
-        input_dict = super(Matern32, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(Matern32, self)._save_to_input_dict()
         input_dict["class"] = "GPy.kern.Matern32"
         return input_dict
 
     @staticmethod
-    def _from_dict(kernel_class, input_dict):
+    def _build_from_input_dict(kernel_class, input_dict):
         useGPU = input_dict.pop('useGPU', None)
         return Matern32(**input_dict)
 
@@ -513,12 +529,20 @@ class Matern52(Stationary):
         super(Matern52, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
 
     def to_dict(self):
-        input_dict = super(Matern52, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(Matern52, self)._save_to_input_dict()
         input_dict["class"] = "GPy.kern.Matern52"
         return input_dict
 
     @staticmethod
-    def _from_dict(kernel_class, input_dict):
+    def _build_from_input_dict(kernel_class, input_dict):
         useGPU = input_dict.pop('useGPU', None)
         return Matern52(**input_dict)
 
@@ -578,12 +602,20 @@ class ExpQuad(Stationary):
         super(ExpQuad, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
 
     def to_dict(self):
-        input_dict = super(ExpQuad, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(ExpQuad, self)._save_to_input_dict()
         input_dict["class"] = "GPy.kern.ExpQuad"
         return input_dict
 
     @staticmethod
-    def _from_dict(kernel_class, input_dict):
+    def _build_from_input_dict(kernel_class, input_dict):
         useGPU = input_dict.pop('useGPU', None)
         return ExpQuad(**input_dict)
 
@@ -621,13 +653,21 @@ class RatQuad(Stationary):
         self.link_parameters(self.power)
 
     def to_dict(self):
-        input_dict = super(RatQuad, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(RatQuad, self)._save_to_input_dict()
         input_dict["class"] = "GPy.kern.RatQuad"
         input_dict["power"] = self.power.values.tolist()
         return input_dict
 
     @staticmethod
-    def _from_dict(kernel_class, input_dict):
+    def _build_from_input_dict(kernel_class, input_dict):
         useGPU = input_dict.pop('useGPU', None)
         return RatQuad(**input_dict)
 
diff --git a/GPy/likelihoods/bernoulli.py b/GPy/likelihoods/bernoulli.py
index a00798f9..2e745b9b 100644
--- a/GPy/likelihoods/bernoulli.py
+++ b/GPy/likelihoods/bernoulli.py
@@ -30,7 +30,15 @@ class Bernoulli(Likelihood):
             self.log_concave = True
 
     def to_dict(self):
-        input_dict = super(Bernoulli, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(Bernoulli, self)._save_to_input_dict()
         input_dict["class"] = "GPy.likelihoods.Bernoulli"
         return input_dict
 
diff --git a/GPy/likelihoods/gaussian.py b/GPy/likelihoods/gaussian.py
index 412fe404..830deb4d 100644
--- a/GPy/likelihoods/gaussian.py
+++ b/GPy/likelihoods/gaussian.py
@@ -47,7 +47,15 @@ class Gaussian(Likelihood):
             self.log_concave = True
 
     def to_dict(self):
-        input_dict = super(Gaussian, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(Gaussian, self)._save_to_input_dict()
         input_dict["class"] = "GPy.likelihoods.Gaussian"
         input_dict["variance"] = self.variance.values.tolist()
         return input_dict
diff --git a/GPy/likelihoods/likelihood.py b/GPy/likelihoods/likelihood.py
index b28fdef5..b4f72719 100644
--- a/GPy/likelihoods/likelihood.py
+++ b/GPy/likelihoods/likelihood.py
@@ -49,7 +49,7 @@ class Likelihood(Parameterized):
     def to_dict(self):
         raise NotImplementedError
 
-    def _to_dict(self):
+    def _save_to_input_dict(self):
         input_dict = {}
         input_dict["name"] = self.name
         input_dict["gp_link_dict"] = self.gp_link.to_dict()
@@ -57,6 +57,18 @@ class Likelihood(Parameterized):
 
     @staticmethod
     def from_dict(input_dict):
+        """
+        Instantiate an object of a derived class using the information
+        in input_dict (built by the to_dict method of the derived class).
+        More specifically, after reading the derived class from input_dict,
+        it calls the method _build_from_input_dict of the derived class.
+        Note: This method should not be overrided in the derived class. In case
+        it is needed, please override _build_from_input_dict instate.
+
+        :param dict input_dict: Dictionary with all the information needed to
+           instantiate the object.
+        """
+
         import copy
         input_dict = copy.deepcopy(input_dict)
         likelihood_class = input_dict.pop('class')
@@ -64,10 +76,10 @@ class Likelihood(Parameterized):
         name = input_dict.pop('name')
         import GPy
         likelihood_class = eval(likelihood_class)
-        return likelihood_class._from_dict(likelihood_class, input_dict)
+        return likelihood_class._build_from_input_dict(likelihood_class, input_dict)
 
     @staticmethod
-    def _from_dict(likelihood_class, input_dict):
+    def _build_from_input_dict(likelihood_class, input_dict):
         import copy
         input_dict = copy.deepcopy(input_dict)
         gp_link_dict = input_dict.pop('gp_link_dict')
diff --git a/GPy/likelihoods/link_functions.py b/GPy/likelihoods/link_functions.py
index d5fc785f..0eb05e74 100644
--- a/GPy/likelihoods/link_functions.py
+++ b/GPy/likelihoods/link_functions.py
@@ -46,20 +46,32 @@ class GPTransformation(object):
     def to_dict(self):
         raise NotImplementedError
 
-    def _to_dict(self):
+    def _save_to_input_dict(self):
         return {}
 
     @staticmethod
     def from_dict(input_dict):
+        """
+        Instantiate an object of a derived class using the information
+        in input_dict (built by the to_dict method of the derived class).
+        More specifically, after reading the derived class from input_dict,
+        it calls the method _build_from_input_dict of the derived class.
+        Note: This method should not be overrided in the derived class. In case
+        it is needed, please override _build_from_input_dict instate.
+
+        :param dict input_dict: Dictionary with all the information needed to
+           instantiate the object.
+        """
+
         import copy
         input_dict = copy.deepcopy(input_dict)
         link_class = input_dict.pop('class')
         import GPy
         link_class = eval(link_class)
-        return link_class._from_dict(link_class, input_dict)
+        return link_class._build_from_input_dict(link_class, input_dict)
 
     @staticmethod
-    def _from_dict(link_class, input_dict):
+    def _build_from_input_dict(link_class, input_dict):
         return link_class(**input_dict)
 
 class Identity(GPTransformation):
@@ -82,7 +94,15 @@ class Identity(GPTransformation):
         return np.zeros_like(f)
 
     def to_dict(self):
-        input_dict = super(Identity, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(Identity, self)._save_to_input_dict()
         input_dict["class"] = "GPy.likelihoods.link_functions.Identity"
         return input_dict
 
@@ -106,7 +126,15 @@ class Probit(GPTransformation):
         return (safe_square(f)-1.)*std_norm_pdf(f)
 
     def to_dict(self):
-        input_dict = super(Probit, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(Probit, self)._save_to_input_dict()
         input_dict["class"] = "GPy.likelihoods.link_functions.Probit"
         return input_dict
 
diff --git a/GPy/mappings/constant.py b/GPy/mappings/constant.py
index 24c632c9..315b47b7 100644
--- a/GPy/mappings/constant.py
+++ b/GPy/mappings/constant.py
@@ -40,7 +40,14 @@ class Constant(Mapping):
         return np.zeros_like(X)
 
     def to_dict(self):
-        input_dict = super(Constant, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+        input_dict = super(Constant, self)._save_to_input_dict()
         input_dict["class"] = "GPy.mappings.Constant"
         input_dict["value"] = self.C.values[0]
         return input_dict
diff --git a/GPy/mappings/identity.py b/GPy/mappings/identity.py
index 261d918f..3567b694 100644
--- a/GPy/mappings/identity.py
+++ b/GPy/mappings/identity.py
@@ -20,6 +20,13 @@ class Identity(Mapping):
         return dL_dF
 
     def to_dict(self):
-        input_dict = super(Identity, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+        input_dict = super(Identity, self)._save_to_input_dict()
         input_dict["class"] = "GPy.mappings.Identity"
         return input_dict
diff --git a/GPy/mappings/linear.py b/GPy/mappings/linear.py
index e348d458..82b7a0e0 100644
--- a/GPy/mappings/linear.py
+++ b/GPy/mappings/linear.py
@@ -39,13 +39,21 @@ class Linear(Mapping):
         return np.dot(dL_dF, self.A.T)
 
     def to_dict(self):
-        input_dict = super(Linear, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(Linear, self)._save_to_input_dict()
         input_dict["class"] = "GPy.mappings.Linear"
         input_dict["A"] = self.A.values.tolist()
         return input_dict
 
     @staticmethod
-    def _from_dict(mapping_class, input_dict):
+    def _build_from_input_dict(mapping_class, input_dict):
         import copy
         input_dict = copy.deepcopy(input_dict)
         A = np.array(input_dict.pop('A'))
diff --git a/GPy/models/sparse_gp_classification.py b/GPy/models/sparse_gp_classification.py
index a996732a..0e44966a 100644
--- a/GPy/models/sparse_gp_classification.py
+++ b/GPy/models/sparse_gp_classification.py
@@ -7,6 +7,7 @@ from ..core import SparseGP
 from .. import likelihoods
 from .. import kern
 from ..inference.latent_function_inference import EPDTC
+from copy import deepcopy
 
 class SparseGPClassification(SparseGP):
     """
@@ -40,6 +41,51 @@ class SparseGPClassification(SparseGP):
 
         SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=EPDTC(), name='SparseGPClassification',Y_metadata=Y_metadata)
 
+    @staticmethod
+    def from_sparse_gp(sparse_gp):
+        from copy import deepcopy
+        sparse_gp = deepcopy(sparse_gp)
+        SparseGPClassification(sparse_gp.X, sparse_gp.Y, sparse_gp.Z, sparse_gp.kern, sparse_gp.likelihood, sparse_gp.inference_method, sparse_gp.mean_function, name='sparse_gp_classification')
+
+    def to_dict(self, save_data=True):
+        """
+        Store the object into a json serializable dictionary
+
+        :param boolean save_data: if true, it adds the data self.X and self.Y to the dictionary
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+        model_dict = super(SparseGPClassification,self).to_dict(save_data)
+        model_dict["class"] = "GPy.models.SparseGPClassification"
+        return model_dict
+
+    @staticmethod
+    def from_dict(input_dict, data=None):
+        """
+        Instantiate an SparseGPClassification object using the information
+        in input_dict (built by the to_dict method).
+
+        :param data: It is used to provide X and Y for the case when the model
+           was saved using save_data=False in to_dict method.
+        :type data: tuple(:class:`np.ndarray`, :class:`np.ndarray`)
+        """
+        import GPy
+        m = GPy.core.model.Model.from_dict(input_dict, data)
+        from copy import deepcopy
+        sparse_gp = deepcopy(m)
+        return SparseGPClassification(sparse_gp.X, sparse_gp.Y, sparse_gp.Z, sparse_gp.kern, sparse_gp.likelihood,  sparse_gp.inference_method, sparse_gp.mean_function, name='sparse_gp_classification')
+
+    def save_model(self, output_filename, compress=True, save_data=True):
+        """
+        Method to serialize the model.
+
+        :param string output_filename: Output file
+        :param boolean compress: If true compress the file using zip
+        :param boolean save_data: if true, it serializes the training data
+            (self.X and self.Y)
+        """
+        self._save_model(output_filename, compress=True, save_data=True)
+
+
 class SparseGPClassificationUncertainInput(SparseGP):
     """
     Sparse Gaussian Process model for classification with uncertain inputs.
@@ -87,8 +133,3 @@ class SparseGPClassificationUncertainInput(SparseGP):
         self.psi2 = self.kern.psi2n(self.Z, self.X)
         self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.Y_metadata, psi0=self.psi0, psi1=self.psi1, psi2=self.psi2)
         self._update_gradients()
-
-
-
-
-
diff --git a/GPy/testing/serialization_tests.py b/GPy/testing/serialization_tests.py
index 7eb3fe5c..57781bf3 100644
--- a/GPy/testing/serialization_tests.py
+++ b/GPy/testing/serialization_tests.py
@@ -11,6 +11,7 @@ import tempfile
 import GPy
 from nose import SkipTest
 import numpy as np
+import os
 fixed_seed = 11
 
 
@@ -116,46 +117,38 @@ class Test(unittest.TestCase):
         np.testing.assert_array_equal(e1._ep_approximation[2].v[:], e1_r._ep_approximation[2].v[:])
         np.testing.assert_array_equal(e1._ep_approximation[3][:], e1_r._ep_approximation[3][:])
 
-
-        e1 = GPy.inference.latent_function_inference.expectation_propagation.EPDTC(ep_mode="nested")
-        e1.ga_approx_old = GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10))
-        e1._ep_approximation = []
-        e1._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.posteriorParamsDTC(np.random.rand(10),np.random.rand(10)))
-        e1._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10)))
-        e1._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.cavityParams(10))
-        e1._ep_approximation[-1].v = np.random.rand(10)
-        e1._ep_approximation[-1].tau = np.random.rand(10)
-        e1._ep_approximation.append(np.random.rand(10))
-        e1_r = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(e1.to_dict())
-
-
-        assert type(e1) == type(e1_r)
-        assert e1.epsilon==e1_r.epsilon
-        assert e1.eta==e1_r.eta
-        assert e1.delta==e1_r.delta
-        assert e1.always_reset==e1_r.always_reset
-        assert e1.max_iters==e1_r.max_iters
-        assert e1.ep_mode==e1_r.ep_mode
-        assert e1.parallel_updates==e1_r.parallel_updates
-
-        np.testing.assert_array_equal(e1.ga_approx_old.tau[:], e1_r.ga_approx_old.tau[:])
-        np.testing.assert_array_equal(e1.ga_approx_old.v[:], e1_r.ga_approx_old.v[:])
-        np.testing.assert_array_equal(e1._ep_approximation[0].mu[:], e1_r._ep_approximation[0].mu[:])
-        np.testing.assert_array_equal(e1._ep_approximation[0].Sigma_diag[:], e1_r._ep_approximation[0].Sigma_diag[:])
-        np.testing.assert_array_equal(e1._ep_approximation[1].tau[:], e1_r._ep_approximation[1].tau[:])
-        np.testing.assert_array_equal(e1._ep_approximation[1].v[:], e1_r._ep_approximation[1].v[:])
-        np.testing.assert_array_equal(e1._ep_approximation[2].tau[:], e1_r._ep_approximation[2].tau[:])
-        np.testing.assert_array_equal(e1._ep_approximation[2].v[:], e1_r._ep_approximation[2].v[:])
-        np.testing.assert_array_equal(e1._ep_approximation[3][:], e1_r._ep_approximation[3][:])
-
-
-        e2 = GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference()
+        e2 = GPy.inference.latent_function_inference.expectation_propagation.EPDTC(ep_mode="nested")
+        e2.ga_approx_old = GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10))
+        e2._ep_approximation = []
+        e2._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.posteriorParamsDTC(np.random.rand(10),np.random.rand(10)))
+        e2._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10)))
+        e2._ep_approximation.append(100.0)
         e2_r = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(e2.to_dict())
 
         assert type(e2) == type(e2_r)
+        assert e2.epsilon==e2_r.epsilon
+        assert e2.eta==e2_r.eta
+        assert e2.delta==e2_r.delta
+        assert e2.always_reset==e2_r.always_reset
+        assert e2.max_iters==e2_r.max_iters
+        assert e2.ep_mode==e2_r.ep_mode
+        assert e2.parallel_updates==e2_r.parallel_updates
+
+        np.testing.assert_array_equal(e2.ga_approx_old.tau[:], e2_r.ga_approx_old.tau[:])
+        np.testing.assert_array_equal(e2.ga_approx_old.v[:], e2_r.ga_approx_old.v[:])
+        np.testing.assert_array_equal(e2._ep_approximation[0].mu[:], e2_r._ep_approximation[0].mu[:])
+        np.testing.assert_array_equal(e2._ep_approximation[0].Sigma_diag[:], e2_r._ep_approximation[0].Sigma_diag[:])
+        np.testing.assert_array_equal(e2._ep_approximation[1].tau[:], e2_r._ep_approximation[1].tau[:])
+        np.testing.assert_array_equal(e2._ep_approximation[1].v[:], e2_r._ep_approximation[1].v[:])
+        assert(e2._ep_approximation[2] == e2_r._ep_approximation[2])
+
+        e3 = GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference()
+        e3_r = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(e3.to_dict())
+
+        assert type(e3) == type(e3_r)
 
 
-    def test_serialize_deserialize_model(self):
+    def test_serialize_deserialize_GP(self):
         np.random.seed(fixed_seed)
         N = 20
         Nhalf = int(N/2)
@@ -165,13 +158,13 @@ class Test(unittest.TestCase):
         likelihood = GPy.likelihoods.Bernoulli()
         inference_method=GPy.inference.latent_function_inference.expectation_propagation.EP(ep_mode="nested")
         mean_function=None
+
         m = GPy.core.GP(X=X, Y=Y,  kernel=kernel, likelihood=likelihood, inference_method=inference_method, mean_function=mean_function, normalizer=True, name='gp_classification')
         m.optimize()
         m.save_model("temp_test_gp_with_data.json", compress=True, save_data=True)
         m.save_model("temp_test_gp_without_data.json", compress=True, save_data=False)
         m1_r = GPy.core.GP.load_model("temp_test_gp_with_data.json.zip")
         m2_r = GPy.core.GP.load_model("temp_test_gp_without_data.json.zip", (X,Y))
-        import os
         os.remove("temp_test_gp_with_data.json.zip")
         os.remove("temp_test_gp_without_data.json.zip")
         var = m.predict(X)[0]
@@ -180,7 +173,32 @@ class Test(unittest.TestCase):
         np.testing.assert_array_equal(np.array(var).flatten(), np.array(var1_r).flatten())
         np.testing.assert_array_equal(np.array(var).flatten(), np.array(var2_r).flatten())
 
-    def test_serialize_deserialize_inference_GPRegressor(self):
+    def test_serialize_deserialize_SparseGP(self):
+        np.random.seed(fixed_seed)
+        N = 20
+        Nhalf = int(N/2)
+        X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[:, None]
+        Y = np.hstack([np.ones(Nhalf), np.zeros(Nhalf)])[:, None]
+        kernel = GPy.kern.RBF(1)
+        likelihood = GPy.likelihoods.Bernoulli()
+        inference_method=GPy.inference.latent_function_inference.expectation_propagation.EPDTC(ep_mode="nested")
+        mean_function=None
+
+        sm = GPy.core.SparseGP(X=X, Y=Y, Z=X[0:20,:], kernel=kernel, likelihood=likelihood, inference_method=inference_method, mean_function=mean_function, normalizer=True, name='sparse_gp_classification')
+        sm.optimize()
+        sm.save_model("temp_test_gp_with_data.json", compress=True, save_data=True)
+        sm.save_model("temp_test_gp_without_data.json", compress=True, save_data=False)
+        sm1_r = GPy.core.GP.load_model("temp_test_gp_with_data.json.zip")
+        sm2_r = GPy.core.GP.load_model("temp_test_gp_without_data.json.zip", (X,Y))
+        os.remove("temp_test_gp_with_data.json.zip")
+        os.remove("temp_test_gp_without_data.json.zip")
+        var = sm.predict(X)[0]
+        var1_r = sm1_r.predict(X)[0]
+        var2_r = sm2_r.predict(X)[0]
+        np.testing.assert_array_equal(np.array(var).flatten(), np.array(var1_r).flatten())
+        np.testing.assert_array_equal(np.array(var).flatten(), np.array(var2_r).flatten())
+
+    def test_serialize_deserialize_GPRegressor(self):
         np.random.seed(fixed_seed)
         N = 50
         N_new = 50
@@ -195,7 +213,6 @@ class Test(unittest.TestCase):
         m.save_model("temp_test_gp_regressor_without_data.json", compress=True, save_data=False)
         m1_r = GPy.models.GPRegression.load_model("temp_test_gp_regressor_with_data.json.zip")
         m2_r = GPy.models.GPRegression.load_model("temp_test_gp_regressor_without_data.json.zip", (X,Y))
-        import os
         os.remove("temp_test_gp_regressor_with_data.json.zip")
         os.remove("temp_test_gp_regressor_without_data.json.zip")
 
@@ -208,7 +225,7 @@ class Test(unittest.TestCase):
         np.testing.assert_array_equal(var.flatten(), var1_r.flatten())
         np.testing.assert_array_equal(var.flatten(), var2_r.flatten())
 
-    def test_serialize_deserialize_inference_GPClassifier(self):
+    def test_serialize_deserialize_GPClassification(self):
         np.random.seed(fixed_seed)
         N = 50
         Nhalf = int(N/2)
@@ -221,7 +238,6 @@ class Test(unittest.TestCase):
         m.save_model("temp_test_gp_classifier_without_data.json", compress=True, save_data=False)
         m1_r = GPy.models.GPClassification.load_model("temp_test_gp_classifier_with_data.json.zip")
         m2_r = GPy.models.GPClassification.load_model("temp_test_gp_classifier_without_data.json.zip", (X,Y))
-        import os
         os.remove("temp_test_gp_classifier_with_data.json.zip")
         os.remove("temp_test_gp_classifier_without_data.json.zip")
 
@@ -231,6 +247,28 @@ class Test(unittest.TestCase):
         np.testing.assert_array_equal(np.array(var).flatten(), np.array(var1_r).flatten())
         np.testing.assert_array_equal(np.array(var).flatten(), np.array(var1_r).flatten())
 
+    def test_serialize_deserialize_SparseGPClassification(self):
+        np.random.seed(fixed_seed)
+        N = 50
+        Nhalf = int(N/2)
+        X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[:, None]
+        Y = np.hstack([np.ones(Nhalf), np.zeros(Nhalf)])[:, None]
+        kernel = GPy.kern.RBF(1)
+        m = GPy.models.SparseGPClassification(X, Y, num_inducing=3, kernel=kernel)
+        m.optimize()
+        m.save_model("temp_test_sparse_gp_classifier_with_data.json", compress=True, save_data=True)
+        m.save_model("temp_test_sparse_gp_classifier_without_data.json", compress=True, save_data=False)
+        m1_r = GPy.models.SparseGPClassification.load_model("temp_test_sparse_gp_classifier_with_data.json.zip")
+        m2_r = GPy.models.SparseGPClassification.load_model("temp_test_sparse_gp_classifier_without_data.json.zip", (X,Y))
+        os.remove("temp_test_sparse_gp_classifier_with_data.json.zip")
+        os.remove("temp_test_sparse_gp_classifier_without_data.json.zip")
+
+        var = m.predict(X)[0]
+        var1_r = m1_r.predict(X)[0]
+        var2_r = m2_r.predict(X)[0]
+        np.testing.assert_array_equal(np.array(var).flatten(), np.array(var1_r).flatten())
+        np.testing.assert_array_equal(np.array(var).flatten(), np.array(var1_r).flatten())
+
 if __name__ == "__main__":
     #import sys;sys.argv = ['', 'Test.test_parameter_index_operations']
     unittest.main()
diff --git a/GPy/util/normalizer.py b/GPy/util/normalizer.py
index 7a3ee020..2e6d991a 100644
--- a/GPy/util/normalizer.py
+++ b/GPy/util/normalizer.py
@@ -52,21 +52,33 @@ class _Norm(object):
     def to_dict(self):
         raise NotImplementedError
 
-    def _to_dict(self):
+    def _save_to_input_dict(self):
         input_dict = {}
         return input_dict
 
     @staticmethod
     def from_dict(input_dict):
+        """
+        Instantiate an object of a derived class using the information
+        in input_dict (built by the to_dict method of the derived class).
+        More specifically, after reading the derived class from input_dict,
+        it calls the method _build_from_input_dict of the derived class.
+        Note: This method should not be overrided in the derived class. In case
+        it is needed, please override _build_from_input_dict instate.
+
+        :param dict input_dict: Dictionary with all the information needed to
+           instantiate the object.
+        """
+
         import copy
         input_dict = copy.deepcopy(input_dict)
         normalizer_class = input_dict.pop('class')
         import GPy
         normalizer_class = eval(normalizer_class)
-        return normalizer_class._from_dict(normalizer_class, input_dict)
+        return normalizer_class._build_from_input_dict(normalizer_class, input_dict)
 
     @staticmethod
-    def _from_dict(normalizer_class, input_dict):
+    def _build_from_input_dict(normalizer_class, input_dict):
         return normalizer_class(**input_dict)
 
 
@@ -96,7 +108,15 @@ class Standardize(_Norm):
         return self.mean is not None
 
     def to_dict(self):
-        input_dict = super(Standardize, self)._to_dict()
+        """
+        Convert the object into a json serializable dictionary.
+
+        Note: It uses the private method _save_to_input_dict of the parent.
+
+        :return dict: json serializable dictionary containing the needed information to instantiate the object
+        """
+
+        input_dict = super(Standardize, self)._save_to_input_dict()
         input_dict["class"] = "GPy.util.normalizer.Standardize"
         if self.mean is not None:
             input_dict["mean"] = self.mean.tolist()
@@ -104,7 +124,7 @@ class Standardize(_Norm):
         return input_dict
 
     @staticmethod
-    def _from_dict(kernel_class, input_dict):
+    def _build_from_input_dict(kernel_class, input_dict):
         s = Standardize()
         if "mean" in input_dict:
             s.mean = np.array(input_dict["mean"])