Replace old dense layer everywhere

n3fit/src/n3fit/backends/keras_backend/base_layers.py

@@ -92,7 +92,7 @@ def dense_per_flavour(basis_size=8, kernel_initializer="glorot_normal", **dense_
 
     # Need to generate a list of dense layers
     dense_basis = [
-        base_layer_selector("dense", kernel_initializer=initializer, **dense_kwargs)
+        base_layer_selector("single_dense", kernel_initializer=initializer, **dense_kwargs)
         for initializer in kernel_initializer
     ]
 
@@ -122,7 +122,7 @@ def apply_dense(xinput):
 
 
 layers = {
-    "multi_dense": (
+    "dense": (
         MultiDense,
         {
             "input_shape": (1,),
@@ -134,7 +134,8 @@ def apply_dense(xinput):
             "replica_input": True,
         },
     ),
-    "dense": (
+    # This one is only used inside dense_per_flavour
+    "single_dense": (
         Dense,
         {
             "input_shape": (1,),

n3fit/src/n3fit/checks.py

@@ -111,7 +111,7 @@ def check_initializer(initializer):
 def check_layer_type_implemented(parameters):
     """Checks whether the layer_type is implemented"""
     layer_type = parameters.get("layer_type")
-    implemented_types = ["dense", "multi_dense", "dense_per_flavour"]
+    implemented_types = ["dense", "dense_per_flavour"]
     if layer_type not in implemented_types:
         raise CheckError(
             f"Layer type {layer_type} not implemented, must be one of {implemented_types}"
@@ -427,10 +427,9 @@ def check_fiatlux_pdfs_id(replicas, fiatlux):
                 f"Cannot generate a photon replica with id larger than the number of replicas of the PDFs set {luxset.name}:\nreplica id={max_id}, replicas of {luxset.name} = {pdfs_ids}"
             )
 
+
 @make_argcheck
 def check_multireplica_qed(replicas, fiatlux):
     if fiatlux is not None:
         if len(replicas) > 1:
-            raise CheckError(
-                "At the moment, running a multireplica QED fits is not allowed."
-            )
+            raise CheckError("At the moment, running a multireplica QED fits is not allowed.")

n3fit/src/n3fit/model_gen.py

@@ -741,7 +741,7 @@ def initializer_generator(seed, i_layer):
     # list_of_pdf_layers[d][r] is the layer at depth d for replica r
     list_of_pdf_layers = []
     for i_layer, (nodes_out, activation) in enumerate(zip(nodes_list, activations)):
-        if layer_type == "multi_dense":
+        if layer_type == "dense":
             layers = base_layer_selector(
                 layer_type,
                 replica_seeds=replica_seeds,
@@ -777,7 +777,7 @@ def initializer_generator(seed, i_layer):
         list_of_pdf_layers[-1] = [lambda x: concat(layer(x)) for layer in list_of_pdf_layers[-1]]
 
     # Apply all layers to the input to create the models
-    if layer_type == "multi_dense":
+    if layer_type == "dense":
         pdfs = x_input
         for layer in list_of_pdf_layers:
             pdfs = layer(pdfs)

n3fit/src/n3fit/tests/test_modelgen.py

@@ -25,16 +25,17 @@
 
 
 def test_generate_dense_network():
-    nn = generate_nn("dense", **COMMON_ARGS).get_layer("NN_0")
+    nn = generate_nn("dense", **COMMON_ARGS)
 
     # The number of layers should be input layer + len(OUT_SIZES)
     assert len(nn.layers) == len(OUT_SIZES) + 1
     # Check that the number of parameters is as expected
-    # We expect 4 weights where the two first ones are
-    # (INSIZE, OUT_SIZE[0]) (OUT_SIZE[0],)
-    # and the second one
-    # (OUT_SIZE[0], OUT_SIZE[1]) (OUT_SIZE[1],)
-    expected_sizes = [(INSIZE, OUT_SIZES[0]), (OUT_SIZES[0],), OUT_SIZES, (OUT_SIZES[1],)]
+    expected_sizes = [
+        (1, INSIZE, OUT_SIZES[0]),
+        (1, 1, OUT_SIZES[0]),
+        (1, *OUT_SIZES),
+        (1, 1, OUT_SIZES[1]),
+    ]
     for weight, esize in zip(nn.weights, expected_sizes):
         assert weight.shape == esize