Gaussian Process as non-final layer

Hi,

This is a fantastic job gluing Keras to GPML. I got your examples to work and a toy problem of my own working already, so I'm quite happy. The next thing I wanted to try is to connect several GP layers to Dense keras layer before the output. I'm getting some errors when attempting this, despite it compiling.

Here's my code:
```def make_GP_layer(batch_size,nb_train_samples):
return GP(inf='infGrid',
lik='likGauss',
dlik='dlikGrid',
cov='covSEiso',
opt={'cg_maxit': 2000,'cg_tol': 1e-6},
mean='meanConst',
grid_kwargs={'eq': 1,'k':150.0}, #equispaced grid w/ 100 pts over data range
update_grid=1,
batch_size=batch_size,
nb_train_samples=nb_train_samples,
hyp={'lik': float(np.log(2.0)), #hyper parameter is the std dev
'cov': [[1.0],[0.5]], #hyper initial params are tiled for all dims
'mean': float(0.1)}
)

#note: passing information to matlab engine requires that it be python types, not numpy types.
def assemble_hierarchal_model(input_shape,chunk_D,batch_size,nb_train_samples):
inp = Input(shape=input_shape)
slice_1 = Lambda(lambda x: x[...,0:chunk_D])(inp)
slice_2 = Lambda(lambda x: x[...,chunk_D:(chunk_D*2)])(inp)
gp1 = make_GP_layer(batch_size,nb_train_samples)
gp2 = make_GP_layer(batch_size,nb_train_samples)
g_1 = gp1(slice_1)
g_2 = gp2(slice_2)
slurp = Concatenate()([g_1,g_2])
sslurp = Reshape((2,))(slurp)
out = Dense(1,use_bias=False)(sslurp)
model = Model(inputs=inp, outputs=out)
#loss = [gen_gp_loss(x) for x in [g_1,g_2]]
model.compile(optimizer=Adam(1e-4), loss='mse')
return model
```

As mentioned, the model compiles. The reshape layer is necessary to keep the TensorFlow back-end happy. Somehow it can detect the size of the GP output layers and correctly concatenate them, but when I add the Dense layer after the Concatenate, it acts like it doesn't know the size. Reshape fixes this.

Anyhow, if you run this model, though, it gives an odd error:

```
/Users/fdfuller/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in control_dependencies(self, control_inputs)
3312 current = self._current_control_dependencies()
3313 for c in control_inputs:
-> 3314 c = self.as_graph_element(c)
3315 if isinstance(c, Tensor):
3316 c = c.op

/Users/fdfuller/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in as_graph_element(self, obj, allow_tensor, allow_operation)
2403
2404 with self._lock:
-> 2405 return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
2406
2407 def _as_graph_element_locked(self, obj, allow_tensor, allow_operation):

/Users/fdfuller/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in _as_graph_element_locked(self, obj, allow_tensor, allow_operation)
2492 # We give up!
2493 raise TypeError("Can not convert a %s into a %s."
-> 2494 % (type(obj).__name__, types_str))
2495
2496 def get_operations(self):

TypeError: Can not convert a int into a Tensor or Operation.
```

I'm digging into your backend to try and understand this. If I use the gp loss function, it complains that the Dense layer doesn't know about dh/dx and won't compile. With the `mse` loss, it compiles but gives this error. Possibly because the kgp Model doesn't have this loss registered?

Anyways, any tips would be helpful.