V-0.6.0: Fixes for Google Colab + Adaptive Weights

Author: ehsanhaghighat

examples/example-fitting-fourier.py

@@ -0,0 +1,61 @@
+'''
+# Curve fitting in 1D with Fourier features
+
+Here, a 1D curve fitting example is explored. Imagine, a synthetic data
+generated from \\\( \sin(x) \\\) over the range of \\\( [0, 2\pi] \\\).
+
+To train a neural network model on this curve, you should first define a `Variable`.
+
+A neural network with three layers, each containing 10 neurons, and with `tanh` activation function is then generated
+using the `Functional` class.
+
+The target is imposed on the output using the `Data` class from `Constraint`, and passed to the `SciModel` to form a
+SciANN model.
+'''
+
+import numpy as np
+from sciann import Variable, Functional, SciModel, Parameter
+from sciann.constraints import Data, MinMax
+from sciann.utils.math import diff
+import sciann as sn
+
+sn.set_random_seed(1234)
+# Synthetic data generated from sin function over [0, 2pi]
+x_true = np.linspace(0, np.pi*2, 10000)
+y_true = np.sin(x_true)
+
+# The network inputs should be defined with Variable.
+x = Variable('x', dtype='float64')
+xf = sn.fourier(x, 10)
+
+# Each network is defined by Functional.
+y1 = sn.Field('y1', 10)
+y2 = sn.Field('y2', 10)
+y1, y2 = sn.Functional([y1,y2], xf, [10, 10, 10], 'tanh', output_activation='tanh')
+
+y = sn.Functional('y', [xf*y1, xf*y2])
+
+d = Parameter(10.0, inputs=x, name='d')
+
+# Define the target (output) of your model.
+c1 = Data(y)
+
+L = d*diff(y, x, order=2) + y
+
+# The model is formed with input `x` and condition `c1`.
+model = SciModel(x, [c1, sn.PDE(L)])
+
+# Tra: .train runs the optimization and finds the parameters.
+history = model.train(
+    x_true,
+    [y_true, 'zeros'],
+    batch_size=32,
+    epochs=100,
+    adaptive_weights={"method": "NTK", "freq": 10},
+    log_parameters=[d]
+)
+
+# used to evaluate the model after the training.
+y_pred = y.eval(model, x_true)
+d_pred = d.eval(model, x_true)
+

sciann/functionals/__init__.py

@@ -18,6 +18,7 @@
 from .field import Field
 from .parameter import Parameter
 from .variable import Variable
+# from .fourier import FourierLayer
 from .radialbasis import RadialBasis
 
 from .rnn_functional import RNNFunctional