More Than Two Dimensions #4
Description
@caio-davi Thank you for the beneficial discussions so far!
I'm expanding the burgers example to 3D (2D space and 1D time), but things got a bit confusing for me for the initial and boundary conditions.
I try to define them as follows:
# initial condition points
x0 = tf.expand_dims(tf.convert_to_tensor(ux, dtype=tf.float32), -1)
y0 = tf.expand_dims(tf.convert_to_tensor(uy, dtype=tf.float32), -1)
xx0, yy0 = np.meshgrid(x0,y0)
XY0 = np.vstack((np.ravel(xx0), np.ravel(yy0))).T
# X0 = XY0.reshape(100,100)
X0 = xx0
t0 = tf.zeros(tf.shape(X0), dtype=tf.float32)
print ("t0",t0.shape)
u0 = tf.zeros(tf.shape(t0), dtype=tf.float32)
print ("u0",u0.shape)
# Dirichlet boundary condition points
xlb = tf.expand_dims(xlo * tf.ones(tf.shape(ut), dtype=tf.float32), -1)
xub = tf.expand_dims(xhi * tf.ones(tf.shape(ut), dtype=tf.float32), -1)
ylb = tf.expand_dims(ylo * tf.ones(tf.shape(ut), dtype=tf.float32), -1)
yub = tf.expand_dims(yhi * tf.ones(tf.shape(ut), dtype=tf.float32), -1)
tlb = tf.expand_dims(tf.convert_to_tensor(ut, dtype=tf.float32), -1)
tub = tf.expand_dims(tf.convert_to_tensor(ut, dtype=tf.float32), -1)
uxlb = tf.expand_dims(tf.zeros(tf.shape(ut), dtype=tf.float32), -1) #tf.expand_dims(tf.math.sin(freq * np.pi * tf.convert_to_tensor( ut, dtype=tf.float32)), -1)
uxub = tf.expand_dims(tf.zeros(tf.shape(ut), dtype=tf.float32), -1)
uylb = tf.expand_dims(tf.zeros(tf.shape(ut), dtype=tf.float32), -1) #tf.expand_dims(tf.math.sin(freq * np.pi * tf.convert_to_tensor( ut, dtype=tf.float32)), -1)
uyub = tf.expand_dims(tf.zeros(tf.shape(ut), dtype=tf.float32), -1)and modified the training functions as follows:
# initial condition points
x0 = tf.expand_dims(tf.convert_to_tensor(ux, dtype=tf.float32), -1)
y0 = tf.expand_dims(tf.convert_to_tensor(uy, dtype=tf.float32), -1)
xx0, yy0 = np.meshgrid(x0,y0)
XY0 = np.vstack((np.ravel(xx0), np.ravel(yy0))).T
# X0 = XY0.reshape(100,100)
X0 = xx0
t0 = tf.zeros(tf.shape(X0), dtype=tf.float32)
print ("t0",t0.shape)
u0 = tf.zeros(tf.shape(t0), dtype=tf.float32)
print ("u0",u0.shape)
# Dirichlet boundary condition points
xlb = tf.expand_dims(xlo * tf.ones(tf.shape(ut), dtype=tf.float32), -1)
xub = tf.expand_dims(xhi * tf.ones(tf.shape(ut), dtype=tf.float32), -1)
ylb = tf.expand_dims(ylo * tf.ones(tf.shape(ut), dtype=tf.float32), -1)
yub = tf.expand_dims(yhi * tf.ones(tf.shape(ut), dtype=tf.float32), -1)
tlb = tf.expand_dims(tf.convert_to_tensor(ut, dtype=tf.float32), -1)
tub = tf.expand_dims(tf.convert_to_tensor(ut, dtype=tf.float32), -1)
uxlb = tf.expand_dims(tf.zeros(tf.shape(ut), dtype=tf.float32), -1) #tf.expand_dims(tf.math.sin(freq * np.pi * tf.convert_to_tensor( ut, dtype=tf.float32)), -1)
uxub = tf.expand_dims(tf.zeros(tf.shape(ut), dtype=tf.float32), -1)
uylb = tf.expand_dims(tf.zeros(tf.shape(ut), dtype=tf.float32), -1) #tf.expand_dims(tf.math.sin(freq * np.pi * tf.convert_to_tensor( ut, dtype=tf.float32)), -1)
uyub = tf.expand_dims(tf.zeros(tf.shape(ut), dtype=tf.float32), -1)
def loss_grad():
def _loss(w, b):
with tf.GradientTape(persistent=True) as tape:
tape.watch(w)
tape.watch(b)
loss_value = loss(w, b)
trainable_variables = w + b
grads = tape.gradient(loss_value, trainable_variables)
return loss_value, grads
return _loss
def run_swarm(swarm, X):
new_swarm = []
for particle in swarm:
w, b = decode(particle, layer_sizes)
new_swarm.append(
multilayer_perceptron(w, b, X_flat.astype(np.float32))
)
return tf.convert_to_tensor(new_swarm, dtype=tf.float32)
but when I tried to run:
opt = pso(
loss_grad(),
layer_sizes,
n_iter,
pop_size,
B,
c1,
c2,
initialization_method="lecun",
verbose=True,
gd_alpha=1e-4,
)I got this error:
\pso-pinn-main\src\swarm\utils.py", line 124, in multilayer_perceptron *
H = tf.nn.tanh(tf.add(tf.matmul(H, W), b))
ValueError: Dimensions must be equal, but are 102 and 3 for '{{node MatMul}} = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false](sub_1, w)' with input shapes: [100,102], [3,50].
Any ideas on how to work around this?
Thanks again!