remove most tf dependencies

extra_tests/regression_fits/central.yml

@@ -12,9 +12,9 @@ description: n3fit regression test
 # ewk: apply ewk k-factors
 # sys: systematics treatment (see systypes)
 dataset_inputs:
-  - {dataset: NMC_NC_NOTFIXED_P_EM-SIGMARED, frac: 0.5, variant: legacy}
-  - {dataset: SLAC_NC_NOTFIXED_P_EM-F2, frac: 0.5, variant: legacy}
-  - {dataset: CMS_Z0J_8TEV_PT-Y, frac: 0.5, cfac: [QCD], variant: legacy_10}
+  - {dataset: NMC_NC_NOTFIXED_P_EM-SIGMARED, frac: 0.55, variant: legacy}
+  - {dataset: SLAC_NC_NOTFIXED_P_EM-F2, frac: 0.75, variant: legacy}
+  - {dataset: CMS_Z0J_8TEV_PT-Y, frac: 0.75, cfac: [QCD], variant: legacy_10}
   - {dataset: ATLAS_TTBAR_8TEV_TOT_X-SEC, frac: 1.0, cfac: [QCD], variant: legacy}
 
 ############################################################
@@ -32,8 +32,7 @@ genrep: False    # on = generate MC replicas, False = use real data
 trvlseed: 3
 nnseed: 2
 mcseed: 1
-
-load: "weights.weights.h5"
+sum_rules: vsr
 
 parameters: # This defines the parameter dictionary that is passed to the Model Trainer
   nodes_per_layer: [15, 10, 8]
@@ -78,3 +77,4 @@ integrability:
 ############################################################
 debug: true
 double_precision: false
+parallel_models: true

n3fit/src/n3fit/backends/keras_backend/MetaModel.py

@@ -8,22 +8,16 @@
 from pathlib import Path
 import re
 
+from keras import optimizers as Kopt
+from keras.models import Model
 import numpy as np
 import tensorflow as tf
-from tensorflow.keras import optimizers as Kopt
-from tensorflow.keras.models import Model
-from tensorflow.python.keras.utils import tf_utils  # pylint: disable=no-name-in-module
 
 import n3fit.backends.keras_backend.operations as op
 
 # We need a function to transform tensors to numpy/python primitives
 # which is not part of the official TF interface and can change with the version
-if hasattr(tf_utils, "to_numpy_or_python_type"):
-    _to_numpy_or_python_type = tf_utils.to_numpy_or_python_type
-elif hasattr(tf_utils, "sync_to_numpy_or_python_type"):  # from TF 2.5
-    _to_numpy_or_python_type = tf_utils.sync_to_numpy_or_python_type
-else:  # in case of disaster
-    _to_numpy_or_python_type = lambda ret: {k: i.numpy() for k, i in ret.items()}
+_to_numpy_or_python_type = lambda ret: {k: i.numpy() for k, i in ret.items()}
 
 # Starting with TF 2.16, a memory leak in TF https://github.com/tensorflow/tensorflow/issues/64170
 # makes jit compilation unusable in GPU.

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

@@ -17,16 +17,14 @@
     The names of the layer and the activation function are the ones to be used in the n3fit runcard.
 """
 
-from tensorflow import expand_dims, math, nn
-from tensorflow.keras.layers import Dense as KerasDense
-from tensorflow.keras.layers import Dropout, Lambda
-from tensorflow.keras.layers import Input  # pylint: disable=unused-import
-from tensorflow.keras.layers import LSTM, Concatenate
-from tensorflow.keras.regularizers import l1_l2
+from keras.layers import Dense as KerasDense
+from keras.layers import Dropout, Lambda
+from keras.layers import Input  # pylint: disable=unused-import
+from keras.layers import LSTM, Concatenate
+from keras.regularizers import l1_l2
 
+from . import operations as ops
 from .MetaLayer import MetaLayer
-from .operations import concatenate_function
-
 
 # Custom activation functions
 def square_activation(x):
@@ -38,17 +36,17 @@ def square_singlet(x):
     """Square the singlet sector
     Defined as the two first values of the NN"""
     singlet_squared = x[..., :2] ** 2
-    return concatenate_function([singlet_squared, x[..., 2:]], axis=-1)
+    return ops.concatenate([singlet_squared, x[..., 2:]], axis=-1)
 
 
 def modified_tanh(x):
     """A non-saturating version of the tanh function"""
-    return math.abs(x) * nn.tanh(x)
+    return ops.absolute(x) * ops.tanh(x)
 
 
 def leaky_relu(x):
     """Computes the Leaky ReLU activation function"""
-    return nn.leaky_relu(x, alpha=0.2)
+    return ops.leaky_relu(x, alpha=0.2)
 
 
 custom_activations = {
@@ -64,7 +62,7 @@ def LSTM_modified(**kwargs):
     LSTM asks for a sample X timestep X features kind of thing so we need to reshape the input
     """
     the_lstm = LSTM(**kwargs)
-    ExpandDim = Lambda(lambda x: expand_dims(x, axis=-1))
+    ExpandDim = Lambda(lambda x: ops.expand_dims(x, axis=-1))
 
     def ReshapedLSTM(input_tensor):
         if len(input_tensor.shape) == 2:

n3fit/src/n3fit/backends/keras_backend/callbacks.py

@@ -15,9 +15,9 @@
 import logging
 from time import time
 
+from keras.callbacks import Callback, TensorBoard
 import numpy as np
 import tensorflow as tf
-from tensorflow.keras.callbacks import Callback, TensorBoard
 
 log = logging.getLogger(__name__)
 

n3fit/src/n3fit/backends/keras_backend/constraints.py

@@ -2,9 +2,8 @@
     Implementations of weight constraints for initializers
 """
 
-import tensorflow as tf
-from tensorflow.keras import backend as K
-from tensorflow.keras.constraints import MinMaxNorm
+from keras import backend as K
+from keras.constraints import MinMaxNorm
 
 
 class MinMaxWeight(MinMaxNorm):

n3fit/src/n3fit/backends/keras_backend/internal_state.py

@@ -1,6 +1,7 @@
 """
     Library of functions that modify the internal state of Keras/Tensorflow
 """
+
 import os
 
 import psutil
@@ -13,10 +14,10 @@
 import logging
 import random as rn
 
+import keras
+from keras import backend as K
 import numpy as np
 import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import backend as K
 
 log = logging.getLogger(__name__)