def test_optimizer(verbose=False):
coef = Parameter(0)
in_ = Input()
model = Circuit([in_], [in_ * coef])
y_pred, y_true = Input(), Input()
loss_function = Circuit([y_pred, y_true],
[(y_pred - y_true) * (y_pred - y_true)])
optimizer = Optimizer(model, loss_function, verbose)
np.random.seed(0)
sample_size = 100
y = np.random.random(sample_size)
X = np.ones(sample_size).reshape((-1, 1))
validation_y = np.random.random(sample_size)
validation_X = np.ones(sample_size).reshape((-1, 1))
model = optimizer.fit(X,
y,
eval_set=(validation_X, validation_y),
n_epochs=10,
learning_rate=0.1,
batch_size=20)
assert abs(model([1])[0] - np.mean(y)) < 0.01
optimizer = Optimizer(model=GridStabilityPINN,
x_train=x_train,
y_train=y_train,
maxiter=20)
parser = argparse.ArgumentParser(description="Retrain selection")
parser.add_argument(
'retrain',
type=str,
default='No',
help="Train from scratch (Yes) or use pretrained weights (No)")
args = parser.parse_args()
if __name__ == '__main__':
if args.retrain == 'Yes':
optimizer.fit(2500)
with open('NNWeights10x20-{}.pickle'.format(time.strftime("%H-%M-%S")),
'wb') as handle:
pickle.dump(base_model.weights, handle)
if args.retrain == 'No':
weights = pickle.load(open("NNWeights10x20.pickle", "rb"))
flattened_weights = np.concatenate(
[np.array(w).flatten() for w in weights])
optimizer.set_weights(flattened_weights)
K_test = 0.2
V_test = 0.4
xbounds = [0, 1]
tbounds = [0, 1]
plot_contour(base_model, N_P=100, save=False)
