def get_batch(n, bs, indices):
batch_indices = [k for k in range(n*bs, (n+1)*bs)]
batch_IDs = indices[batch_indices]
data_list = []
mesh_list = []
soln_list = []
domain_list = []
for ID in batch_IDs:
# Read mesh and data files
source = read_data(ID, os.path.join(DATA_dir,data_dir), USE_HIRES=USE_HIRES)
data_array = np.expand_dims(np.transpose(source, (1, 2, 0)),0)
data_list.append(data_array)
mesh_data = read_mesh(ID, os.path.join(DATA_dir,mesh_dir), USE_HIRES=USE_HIRES)
mesh_array = np.expand_dims(np.transpose(mesh_data, (1, 2, 0)),0)
mesh_list.append(mesh_array)
y_data = read_soln(ID, os.path.join(DATA_dir,soln_dir), USE_HIRES=USE_HIRES)
soln_array = np.expand_dims(np.transpose(y_data, (1, 2, 0)),0)
soln_list.append(soln_array)
domain_count = mesh_array[mesh_array > 0.0].size
domain_list.append(domain_count)
data_batch = np.concatenate(data_list, axis=0)
mesh_batch = np.concatenate(mesh_list, axis=0)
soln_batch = np.concatenate(soln_list, axis=0)
domain_batch = np.array(domain_list)
return data_batch, mesh_batch, soln_batch, domain_batch
#print(op.name)
# Define input and output nodes
data = graph.get_tensor_by_name('prefix/data_test:0')
mesh = graph.get_tensor_by_name('prefix/mesh_test:0')
soln = graph.get_tensor_by_name('prefix/soln_test:0')
y_pred = graph.get_tensor_by_name('prefix/masked_pred_test:0')
y_scale = graph.get_tensor_by_name('prefix/masked_scale_test:0')
with tf.Session(graph=graph) as sess:
# Run initial session to remove graph loading time
# Read mesh and data files
source = read_data(0,
os.path.join(DATA_dir, data_dir),
USE_HIRES=USE_HIRES)
data_batch = np.expand_dims(np.transpose(source, (1, 2, 0)), 0)
mesh_data = read_mesh(0,
os.path.join(DATA_dir, mesh_dir),
USE_HIRES=USE_HIRES)
mesh_batch = np.expand_dims(np.transpose(mesh_data, (1, 2, 0)), 0)
y_data = read_soln(0,
os.path.join(DATA_dir, soln_dir),
USE_HIRES=USE_HIRES)
soln_batch = np.expand_dims(np.transpose(y_data, (1, 2, 0)), 0)
# Compute network prediction
y_out = sess.run(y_pred,
training = np.isin(ID, t_indices, assume_unique=True)
validation = np.isin(ID, v_indices, assume_unique=True)
if training:
print("\n[ TRAINING SET ]")
elif validation:
print("\n[ VALIDATION SET ]")
else:
print("\n[*] Warning: ID not found in indices.")
with tf.Session(graph=graph) as sess:
# Run initial session to remove graph loading time
# Read mesh and data files
source = read_data(0,
os.path.join(DATA_dir, data_dir),
USE_HIRES=USE_HIRES)
data_batch = np.expand_dims(np.transpose(source, (1, 2, 0)), 0)
stiff = read_data(0,
os.path.join(DATA_dir, data_dir),
USE_HIRES=USE_HIRES,
STIFF=True)
coeff_batch = np.expand_dims(np.transpose(stiff, (1, 2, 0)), 0)
mesh_data = read_mesh(0,
os.path.join(DATA_dir, mesh_dir),
USE_HIRES=USE_HIRES)
mesh_batch = np.expand_dims(np.transpose(mesh_data, (1, 2, 0)), 0)
y_data = read_soln(0,
# Display operators defined in graph
#for op in graph.get_operations():
#print(op.name)
# Define input and output nodes
data = graph.get_tensor_by_name('prefix/data_test:0')
mesh = graph.get_tensor_by_name('prefix/mesh_test:0')
soln = graph.get_tensor_by_name('prefix/soln_test:0')
y_pred = graph.get_tensor_by_name('prefix/masked_pred_test:0')
with tf.Session(graph=graph) as sess:
# Run initial session to remove graph loading time
# Read mesh and data files
source = read_data(0, data_dir, USE_HIRES=USE_HIRES)
data_batch = np.expand_dims(np.transpose(source, (1, 2, 0)), 0)
mesh_data = read_mesh(0, mesh_dir, USE_HIRES=USE_HIRES)
mesh_batch = np.expand_dims(np.transpose(mesh_data, (1, 2, 0)), 0)
y_data = read_soln(0, soln_dir, USE_HIRES=USE_HIRES)
soln_batch = np.expand_dims(np.transpose(y_data, (1, 2, 0)), 0)
# Compute network prediction
y_out = sess.run(y_pred,
feed_dict={
data: data_batch,
mesh: mesh_batch,
soln: soln_batch
})