rmsGrad = tf.sqrt(gradSq)
if (numSteps % printIncrement == 0):
print("Energy after " + str(numSteps) + " iterations: " +\
str(energy.numpy()))
print("RSS gradient after " + str(numSteps) + " iterations: " +\
str(rmsGrad.numpy()))
# Perform the gradient descent step
opt.apply_gradients(zip(grads, vars))
numSteps += 1
# Postprocess the fields to avoid drift away from SU(2)/its Lie algebra
scalarFieldVar.assign(0.5 *
(scalarFieldVar + tf.math.conj(scalarFieldVar)))
gaugeFieldVar.assign(FieldTools.projectToSu2(gaugeFieldVar))
print("Gradient descent finished in " + str(numSteps) + " iterations")
print("Final energy: " + str(energy.numpy()))
# Save fields as .npy files for plotting and further analysis
outputPath = args.outputPath
if outputPath != "":
np.save(outputPath + "/X", X.numpy())
np.save(outputPath + "/Y", Y.numpy())
np.save(outputPath + "/Z", Z.numpy())
np.save(outputPath + "/scalarField", scalarFieldVar.numpy())
np.save(outputPath + "/gaugeField", gaugeFieldVar.numpy())
np.save(outputPath + "/params", params)
rssGrad = tf.sqrt(gradSq)
if (numSteps % printIncrement == 0):
print("Energy after " + str(numSteps) + " iterations: " +\
str(energy.numpy()))
print("RSS gradient after " + str(numSteps) + " iterations: " +\
str(rssGrad.numpy()))
# Perform the gradient descent step
opt.apply_gradients(zip(grads, vars))
numSteps += 1
# Postprocess the fields to avoid drift away from SU(2)/its Lie algebra
scalarField.assign(0.5*(scalarField + tf.math.conj(scalarField)))
gaugeField.assign(FieldTools.projectToSu2(gaugeField))
while rssGrad > tol and numSteps < maxNumSteps:
# Compute the field energy, with tf watching the variables
with tf.GradientTape() as tape:
energy = lossFn()
vars = [scalarField, gaugeField]
# Compute the gradients using automatic differentiation
grads = tape.gradient(energy, vars)
# Postprocess the gauge field gradients so they point in the tangent space
# to SU(2)
grads[1] = FieldTools.projectSu2Gradients(grads[1], gaugeField)