Refactoring model creation code

doc/sphinx/source/n3fit/methodology.rst

@@ -126,6 +126,56 @@ provide a faster convergence to the solution.
 .. important::
 	Parameters like the number of layers, nodes, activation functions are hyper-parameters that require tuning.
 
+
+To see the structure of the model, one can use Keras's ``plot_model`` function as illustrated in the script below.
+See the `Keras documentation <https://www.tensorflow.org/api_docs/python/tf/keras/utils/plot_model>`_ for more details.
+
+.. code-block:: python
+
+   from tensorflow.keras.utils import plot_model
+   from n3fit.model_gen import pdfNN_layer_generator
+   from validphys.api import API
+
+   fit_info = API.fit(fit="NNPDF40_nnlo_as_01180_1000").as_input()
+   basis_info = fit_info["fitting"]["basis"]
+
+   pdf_models = pdfNN_layer_generator(
+       nodes=[25, 20, 8],
+       activations=["tanh", "tanh", "linear"],
+       initializer_name="glorot_normal",
+       layer_type="dense",
+       flav_info=basis_info,
+       fitbasis="EVOL",
+       out=14,
+       seed=42,
+       dropout=0.0,
+       regularizer=None,
+       regularizer_args=None,
+       impose_sumrule="All",
+       scaler=None,
+       parallel_models=1,
+   )
+
+   pdf_model = pdf_models[0]
+   nn_model = pdf_model.get_layer("NN_0")
+   msr_model = pdf_model.get_layer("impose_msr")
+   models_to_plot = {
+           'plot_pdf': pdf_model,
+           'plot_nn': nn_model,
+           'plot_msr': msr_model
+           }
+
+   for name, model in models_to_plot.items():
+       plot_model(model, to_file=f"./{name}.png", show_shapes=True)
+
+
+This will produce for instance the plot of the PDF model below, and can also be used to plot the
+neural network model, and the momentum sum rule model.
+
+.. image::
+    figures/plot_pdf.png
+
+
 .. _preprocessing:
 
 Preprocessing

n3fit/src/n3fit/backends/keras_backend/operations.py

@@ -25,18 +25,17 @@
 
 import numpy as np
 import tensorflow as tf
+from tensorflow.keras import backend as K
+from tensorflow.keras.layers import Input
 from tensorflow.keras.layers import Lambda as keras_Lambda
 from tensorflow.keras.layers import multiply as keras_multiply
 from tensorflow.keras.layers import subtract as keras_subtract
 
-from tensorflow.keras.layers import Input
-from tensorflow.keras import backend as K
-
 from validphys.convolution import OP
 
 
 def evaluate(tensor):
-    """ Evaluate input tensor using the backend """
+    """Evaluate input tensor using the backend"""
     return K.eval(tensor)
 
 
@@ -107,36 +106,29 @@ def numpy_to_tensor(ival, **kwargs):
 
 # f(x: tensor) -> y: tensor
 def batchit(x, batch_dimension=0, **kwarg):
-    """ Add a batch dimension to tensor x """
+    """Add a batch dimension to tensor x"""
     return tf.expand_dims(x, batch_dimension, **kwarg)
 
 
 # layer generation
-def numpy_to_input(numpy_array, no_reshape=False, name=None):
+def numpy_to_input(numpy_array: np.ndarray, name: str = None):
     """
-    Takes a numpy array and generates a Input layer.
-    By default it adds a batch dimension (of size 1) so that the shape of the layer
-    is that of the array
+    Takes a numpy array and generates an Input layer with the same shape,
+    but with a batch dimension (of size 1) added.
 
     Parameters
     ----------
         numpy_array: np.ndarray
-        no_reshape: bool
-            if true, don't add batch dimension, take the first dimension of the array as the batch
-        name: bool
+        name: str
             name to give to the layer
     """
-    if no_reshape:
-        batched_array = numpy_array
-        batch_size = numpy_array.shape[0]
-        shape = numpy_array.shape[1:]
-    else:
-        batched_array = np.expand_dims(numpy_array, 0)
-        batch_size = 1
-        shape = numpy_array.shape
-    input_layer = Input(batch_size=batch_size, shape=shape, name=name)
+    batched_array = np.expand_dims(numpy_array, 0)
+    shape = list(numpy_array.shape)
+    # set the number of gridpoints to None, otherwise shapes don't show in model.summary
+    shape[0] = None
+
+    input_layer = Input(batch_size=1, shape=shape, name=name)
     input_layer.tensor_content = batched_array
-    input_layer.original_shape = no_reshape
     return input_layer
 
 
@@ -174,7 +166,7 @@ def op_gather_keep_dims(tensor, indices, axis=0, **kwargs):
     both eager and non-eager tensors
     """
     if indices == -1:
-        indices = tensor.shape[axis]-1
+        indices = tensor.shape[axis] - 1
 
     def tmp(x):
         y = tf.gather(x, indices, axis=axis, **kwargs)
@@ -189,6 +181,7 @@ def tmp(x):
 # f(x: tensor[s]) -> y: tensor
 #
 
+
 # Generation operations
 # generate tensors of given shape/content
 @tf.function
@@ -216,7 +209,7 @@ def many_replication(grid, replications, axis=0, **kwargs):
 # modify properties of the tensor like the shape or elements it has
 @tf.function
 def flatten(x):
-    """ Flatten tensor x """
+    """Flatten tensor x"""
     return tf.reshape(x, (-1,))
 
 
@@ -240,7 +233,7 @@ def transpose(tensor, **kwargs):
 
 
 def stack(tensor_list, axis=0, **kwargs):
-    """ Stack a list of tensors
+    """Stack a list of tensors
     see full `docs <https://www.tensorflow.org/api_docs/python/tf/stack>`_
     """
     return tf.stack(tensor_list, axis=axis, **kwargs)
@@ -280,8 +273,8 @@ def pdf_masked_convolution(raw_pdf, basis_mask):
         pdf_x_pdf: tf.tensor
             rank3 (len(mask_true), xgrid, xgrid, replicas)
     """
-    if raw_pdf.shape[-1] == 1: # only one replica!
-        pdf = tf.squeeze(raw_pdf, axis=(0,-1))
+    if raw_pdf.shape[-1] == 1:  # only one replica!
+        pdf = tf.squeeze(raw_pdf, axis=(0, -1))
         luminosity = tensor_product(pdf, pdf, axes=0)
         lumi_tmp = K.permute_dimensions(luminosity, (3, 1, 2, 0))
         pdf_x_pdf = batchit(boolean_mask(lumi_tmp, basis_mask), -1)
@@ -309,6 +302,14 @@ def tensor_product(*args, **kwargs):
     return tf.tensordot(*args, **kwargs)
 
 
+@tf.function
+def pow(tensor, power):
+    """
+    Computes the power of the tensor
+    """
+    return tf.pow(tensor, power)
+
+
 @tf.function(experimental_relax_shapes=True)
 def op_log(o_tensor, **kwargs):
     """

n3fit/src/n3fit/layers/msr_normalization.py

@@ -16,8 +16,7 @@ class MSR_Normalization(MetaLayer):
     _msr_enabled = False
     _vsr_enabled = False
 
-    def __init__(self, output_dim=14, mode="ALL", photons_contribution=None, **kwargs):
-        self._photons = photons_contribution
+    def __init__(self, output_dim=14, mode="ALL", **kwargs):
         if mode == True or mode.upper() == "ALL":
             self._msr_enabled = True
             self._vsr_enabled = True
@@ -38,9 +37,9 @@ def __init__(self, output_dim=14, mode="ALL", photons_contribution=None, **kwarg
             op.scatter_to_one, op_kwargs={"indices": idx, "output_dim": output_dim}
         )
 
-        super().__init__(**kwargs, name="normalizer")
+        super().__init__(**kwargs)
 
-    def call(self, pdf_integrated, ph_replica):
+    def call(self, pdf_integrated, photon_integral):
         """Imposes the valence and momentum sum rules:
         A_g = (1-sigma-photon)/g
         A_v = A_v24 = A_v35 = 3/V
@@ -49,17 +48,24 @@ def call(self, pdf_integrated, ph_replica):
         A_v15 = 3/V_15
 
         Note that both the input and the output are in the 14-flavours fk-basis
+
+        Parameters
+        ----------
+        pdf_integrated: (Tensor(1,None,14))
+            the integrated PDF
+        photon_integral: (Tensor(1)):
+            the integrated photon, not included in PDF
+
+        Returns
+        -------
+        normalization_factor: Tensor(14)
+            The normalization factors per flavour.
         """
         y = op.flatten(pdf_integrated)
         norm_constants = []
 
-        if self._photons:
-            photon_integral = self._photons[ph_replica]
-        else:
-            photon_integral = 0.0
-
         if self._msr_enabled:
-            n_ag = [(1.0 - y[GLUON_IDX[0][0] - 1] - photon_integral) / y[GLUON_IDX[0][0]]] * len(
+            n_ag = [(1.0 - y[GLUON_IDX[0][0] - 1] - photon_integral[0]) / y[GLUON_IDX[0][0]]] * len(
                 GLUON_IDX
             )
             norm_constants += n_ag

n3fit/src/n3fit/layers/preprocessing.py

@@ -1,11 +1,10 @@
-from n3fit.backends import MetaLayer
-from n3fit.backends import constraints
+from n3fit.backends import MetaLayer, constraints
 from n3fit.backends import operations as op
 
 
 class Preprocessing(MetaLayer):
     """
-    Applies preprocessing to the PDF.
+    Computes preprocessing factor for the PDF.
 
     This layer generates a factor (1-x)^beta*x^(1-alpha) where both beta and alpha
     are model paramters that can be trained. If feature scaling is used, the preprocessing
@@ -21,29 +20,26 @@ class Preprocessing(MetaLayer):
     Parameters
     ----------
         flav_info: list
-            list of dicts containing the information about the fitting of the preprocessing
+            list of dicts containing the information about the fitting of the preprocessing factor
             This corresponds to the `fitting::basis` parameter in the nnpdf runcard.
             The dicts can contain the following fields:
                 `smallx`: range of alpha
                 `largex`: range of beta
                 `trainable`: whether these alpha-beta should be trained during the fit
                             (defaults to true)
         large_x: bool
-            Whether large x preprocessing should be active
+            Whether large x preprocessing factor should be active
         seed: int
             seed for the initializer of the random alpha and beta values
     """
 
     def __init__(
-        self,
-        flav_info=None,
-        seed=0,
-        initializer="random_uniform",
-        large_x=True,
-        **kwargs,
+        self, flav_info=None, seed=0, initializer="random_uniform", large_x=True, **kwargs,
     ):
         if flav_info is None:
-            raise ValueError("Trying to instantiate a preprocessing with no basis information")
+            raise ValueError(
+                "Trying to instantiate a preprocessing factor with no basis information"
+            )
         self.flav_info = flav_info
         self.seed = seed
         self.output_dim = len(flav_info)

n3fit/src/n3fit/layers/x_operations.py

@@ -1,54 +1,55 @@
 """
     This module contains layers acting on the x-grid input of the NN
 
-    The three operations included are:
+    The two operations included are:
         - ``xDivide``
         - ``xIntegrator``
 
     The names are self-describing. The only subtlety is that they do not act equally
     for all flavours. The choice of flavours on which to act in a different way is given
     as an input argument.
 """
+from typing import List
 
 from n3fit.backends import MetaLayer
 from n3fit.backends import operations as op
 
-BASIS_SIZE=14
+BASIS_SIZE = 14
+
 
 class xDivide(MetaLayer):
     """
-    Divide some PDFs by x
+    Create tensor of either 1/x or ones depending on the flavour,
+    to be used to divide some PDFs by x by multiplying with the result.
 
-    By default it utilizes the 14-flavour FK basis and divides [v, v3, v8]
-    which corresponds to indices (3,4,5) from
+    By default it utilizes the 14-flavour FK basis and divides [v, v3, v8, v15]
+    which corresponds to indices (3, 4, 5, 6) from
     (photon, sigma, g, v, v3, v8, v15, v24, v35, t3, t8, t15, t24, t35)
 
     Parameters:
     -----------
         output_dim: int
             dimension of the pdf
         div_list: list
-            list of indices to be divided by X (by default [3,4,5]; [v, v3, v8]
+            list of indices to be divided by X (by default [3, 4, 5, 6]; [v, v3, v8, v15]
     """
 
-    def __init__(self, output_dim=BASIS_SIZE, div_list=None, **kwargs):
+    def __init__(self, output_dim: int = BASIS_SIZE, div_list: List = None, **kwargs):
         if div_list is None:
             div_list = [3, 4, 5, 6]
         self.output_dim = output_dim
         self.div_list = div_list
         super().__init__(**kwargs)
 
+        self.powers = [-1 if i in div_list else 0 for i in range(output_dim)]
+
     def call(self, x):
-        out_array = []
-        one = op.tensor_ones_like(x)
-        for i in range(self.output_dim):
-            if i in self.div_list:
-                res = one / x
-            else:
-                res = one
-            out_array.append(res)
-        out_tensor = op.concatenate(out_array)
-        return out_tensor
+        return op.pow(x, self.powers)
+
+    def get_config(self):
+        config = super().get_config()
+        config.update({"output_dim": self.output_dim, "div_list": self.div_list})
+        return config
 
 
 class xIntegrator(MetaLayer):