def solve_Multiscale_PDE(R):
log_out_path = R['FolderName'] # 将路径从字典 R 中提取出来
if not os.path.exists(log_out_path): # 判断路径是否已经存在
os.mkdir(log_out_path) # 无 log_out_path 路径，创建一个 log_out_path 路径
outfile_name1 = '%s%s.txt' % ('log2', 'train')
log_fileout_NN = open(os.path.join(log_out_path, outfile_name1),
'w') # 在这个路径下创建并打开一个可写的 log_train.txt文件
dictionary_out2file(R,
log_fileout_NN,
actName2normal=R['act_name2NN1'],
actName2scale=R['act_name2NN2'])
# laplace 问题需要的设置
batchsize_it = R['batch_size2interior']
batchsize_bd = R['batch_size2boundary']
bd_penalty_init = R[
'init_boundary_penalty'] # Regularization parameter for boundary conditions
lr_decay = R['learning_rate_decay']
learning_rate = R['learning_rate']
init_penalty2powU = R['balance2solus']
hidden2normal = R['hidden2normal']
hidden2scale = R['hidden2scale']
wb_regular = R[
'regular_weight_biases'] # Regularization parameter for weights and biases
# ------- set the problem ---------
input_dim = R['input_dim']
out_dim = R['output_dim']
act_func1 = R['act_name2NN1']
act_func2 = R['act_name2NN2']
region_l = 0.0
region_r = 1.0
if R['PDE_type'] == 'general_laplace':
# -laplace u = f
region_l = 0.0
region_r = 1.0
f, u_true, u_left, u_right = laplace_eqs1d.get_laplace_infos(
input_dim=input_dim,
out_dim=out_dim,
left_bottom=region_l,
right_top=region_r,
laplace_name=R['equa_name'])
elif R['PDE_type'] == 'p_laplace':
# 求解如下方程, A_eps(x) 震荡的比较厉害，具有多个尺度
# d **** d ****
# - ---- | A_eps(x)* ---- u_eps(x) | =f(x), x \in R^n
# dx **** dx ****
# 问题区域，每个方向设置为一样的长度。等网格划分，对于二维是方形区域
p_index = R['order2laplace']
epsilon = R['epsilon']
if 2 == p_index:
region_l = 0.0
region_r = 1.0
u_true, f, A_eps, u_left, u_right = pLaplace_eqs1d.get_infos_2laplace(
in_dim=input_dim,
out_dim=out_dim,
region_a=region_l,
region_b=region_r,
p=p_index,
eps=epsilon)
elif 3 == p_index:
region_l = 0.0
region_r = 1.0
u_true, f, A_eps, u_left, u_right = pLaplace_eqs1d.get_infos_3laplace(
in_dim=input_dim,
out_dim=out_dim,
region_a=region_l,
region_b=region_r,
p=p_index,
eps=epsilon)
elif 5 == p_index:
region_l = 0.0
region_r = 1.0
u_true, f, A_eps, u_left, u_right = pLaplace_eqs1d.get_infos_5laplace(
in_dim=input_dim,
out_dim=out_dim,
region_a=region_l,
region_b=region_r,
p=p_index,
eps=epsilon)
elif 8 == p_index:
region_l = 0.0
region_r = 1.0
u_true, f, A_eps, u_left, u_right = pLaplace_eqs1d.get_infos_8laplace(
in_dim=input_dim,
out_dim=out_dim,
region_a=region_l,
region_b=region_r,
p=p_index,
eps=epsilon)
else:
region_l = 0.0
region_r = 1.0
u_true, f, A_eps, u_left, u_right = pLaplace_eqs1d.get_infos_pLaplace(
in_dim=input_dim,
out_dim=out_dim,
region_a=region_l,
region_b=region_r,
p=p_index,
eps=epsilon,
eqs_name=R['equa_name'])
# 初始化权重和和偏置的模式
if R['weight_biases_model'] == 'general_model':
flag_normal = 'WB_NN2normal'
flag_scale = 'WB_NN2scale'
# Weights, Biases = PDE_DNN_base.Initial_DNN2different_hidden(input_dim, out_dim, hidden_layers, flag)
# Weights, Biases = laplace_DNN1d_base.initialize_NN_xavier(input_dim, out_dim, hidden_layers, flag1)
# Weights, Biases = laplace_DNN1d_base.initialize_NN_random_normal(input_dim, out_dim, hidden_layers, flag1)
if R['model2normal'] == 'PDE_DNN_Cos_C_Sin_Base' or R[
'model2normal'] == 'DNN_adaptCosSin_Base':
W2NN_Normal, B2NN_Normal = DNN_base.initialize_NN_random_normal2_CS(
input_dim, out_dim, hidden2normal, flag_normal)
else:
W2NN_Normal, B2NN_Normal = DNN_base.initialize_NN_random_normal2(
input_dim, out_dim, hidden2normal, flag_normal)
if R['model2scale'] == 'PDE_DNN_Cos_C_Sin_Base' or R[
'model2scale'] == 'DNN_adaptCosSin_Base':
W2NN_freqs, B2NN_freqs = DNN_base.initialize_NN_random_normal2_CS(
input_dim, out_dim, hidden2scale, flag_scale)
else:
W2NN_freqs, B2NN_freqs = DNN_base.initialize_NN_random_normal2(
input_dim, out_dim, hidden2scale, flag_scale)
global_steps = tf.Variable(0, trainable=False)
with tf.device('/gpu:%s' % (R['gpuNo'])):
with tf.variable_scope('vscope', reuse=tf.AUTO_REUSE):
X_it = tf.placeholder(tf.float32,
name='X_it',
shape=[None, input_dim]) # * 行 1 列
X_left_bd = tf.placeholder(tf.float32,
name='X_left_bd',
shape=[None, input_dim]) # * 行 1 列
X_right_bd = tf.placeholder(tf.float32,
name='X_right_bd',
shape=[None, input_dim]) # * 行 1 列
bd_penalty = tf.placeholder_with_default(input=1e3,
shape=[],
name='bd_p')
penalty2powU = tf.placeholder_with_default(input=1.0,
shape=[],
name='p_powU')
in_learning_rate = tf.placeholder_with_default(input=1e-5,
shape=[],
name='lr')
train_opt = tf.placeholder_with_default(input=True,
shape=[],
name='train_opt')
if R['model2normal'] == 'PDE_DNN':
U_NN_Normal = DNN_base.PDE_DNN(X_it,
W2NN_Normal,
B2NN_Normal,
hidden2normal,
activate_name=act_func1)
ULeft_NN_Normal = DNN_base.PDE_DNN(X_left_bd,
W2NN_Normal,
B2NN_Normal,
hidden2normal,
activate_name=act_func1)
URight_NN_Normal = DNN_base.PDE_DNN(X_right_bd,
W2NN_Normal,
B2NN_Normal,
hidden2normal,
activate_name=act_func1)
elif R['model2normal'] == 'PDE_DNN_Cos_C_Sin_Base':
freq = [1]
U_NN_Normal = DNN_base.PDE_DNN_Cos_C_Sin_Base(
X_it,
W2NN_Normal,
B2NN_Normal,
hidden2normal,
freq,
activate_name=act_func1)
ULeft_NN_Normal = DNN_base.PDE_DNN_Cos_C_Sin_Base(
X_left_bd,
W2NN_Normal,
B2NN_Normal,
hidden2normal,
freq,
activate_name=act_func1)
URight_NN_Normal = DNN_base.PDE_DNN_Cos_C_Sin_Base(
X_right_bd,
W2NN_Normal,
B2NN_Normal,
hidden2normal,
freq,
activate_name=act_func1)
elif R['model2normal'] == 'DNN_adaptCosSin_Base':
freq = [1]
U_NN_Normal = DNN_base.DNN_adaptCosSin_Base(
X_it,
W2NN_Normal,
B2NN_Normal,
hidden2normal,
freq,
activate_name=act_func1)
ULeft_NN_Normal = DNN_base.DNN_adaptCosSin_Base(
X_left_bd,
W2NN_Normal,
B2NN_Normal,
hidden2normal,
freq,
activate_name=act_func1)
URight_NN_Normal = DNN_base.DNN_adaptCosSin_Base(
X_right_bd,
W2NN_Normal,
B2NN_Normal,
hidden2normal,
freq,
activate_name=act_func1)
freqs = R['freqs']
if R['model2scale'] == 'PDE_DNN_scale':
U_NN_freqs = DNN_base.PDE_DNN_scale(X_it,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
ULeft_NN_freqs = DNN_base.PDE_DNN_scale(
X_left_bd,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
URight_NN_freqs = DNN_base.PDE_DNN_scale(
X_right_bd,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
elif R['model2scale'] == 'PDE_DNN_adapt_scale':
U_NN_freqs = DNN_base.PDE_DNN_adapt_scale(
X_it,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
ULeft_NN_freqs = DNN_base.PDE_DNN_adapt_scale(
X_left_bd,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
URight_NN_freqs = DNN_base.PDE_DNN_adapt_scale(
X_right_bd,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
elif R['model2scale'] == 'PDE_DNN_FourierBase':
U_NN_freqs = DNN_base.PDE_DNN_FourierBase(
X_it,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
ULeft_NN_freqs = DNN_base.PDE_DNN_FourierBase(
X_left_bd,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
URight_NN_freqs = DNN_base.PDE_DNN_FourierBase(
X_right_bd,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
elif R['model2scale'] == 'PDE_DNN_Cos_C_Sin_Base':
U_NN_freqs = DNN_base.PDE_DNN_Cos_C_Sin_Base(
X_it,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
ULeft_NN_freqs = DNN_base.PDE_DNN_Cos_C_Sin_Base(
X_left_bd,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
URight_NN_freqs = DNN_base.PDE_DNN_Cos_C_Sin_Base(
X_right_bd,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
elif R['model2scale'] == 'DNN_adaptCosSin_Base':
U_NN_freqs = DNN_base.DNN_adaptCosSin_Base(
X_it,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
ULeft_NN_freqs = DNN_base.DNN_adaptCosSin_Base(
X_left_bd,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
URight_NN_freqs = DNN_base.DNN_adaptCosSin_Base(
X_right_bd,
W2NN_freqs,
B2NN_freqs,
hidden2scale,
freqs,
activate_name=act_func2)
U_NN = U_NN_Normal + U_NN_freqs
# 变分形式的loss of interior，训练得到的 U_NN1 是 * 行 1 列, 因为 一个点对(x,y) 得到一个 u 值
dU_NN_Normal = tf.gradients(U_NN_Normal, X_it)[0] # * 行 2 列
dU_NN_freqs = tf.gradients(U_NN_freqs, X_it)[0] # * 行 2 列
if R['variational_loss'] == 1:
dU_NN = tf.add(dU_NN_Normal, dU_NN_freqs)
if R['PDE_type'] == 'general_laplace':
laplace_norm2NN = tf.reduce_sum(tf.square(dU_NN), axis=-1)
loss_it_NN = (1.0 / 2) * tf.reshape(laplace_norm2NN, shape=[-1, 1]) - \
tf.multiply(tf.reshape(f(X_it), shape=[-1, 1]), U_NN)
elif R['PDE_type'] == 'p_laplace':
# a_eps = A_eps(X_it) # * 行 1 列
a_eps = 1 / (2 + tf.cos(2 * np.pi * X_it / epsilon))
laplace_p_pow2NN = tf.reduce_sum(
a_eps * tf.pow(tf.abs(dU_NN), p_index), axis=-1)
loss_it_NN = (1.0 / p_index) * tf.reshape(laplace_p_pow2NN, shape=[-1, 1]) - \
tf.multiply(tf.reshape(f(X_it), shape=[-1, 1]), U_NN)
Loss_it2NN = tf.reduce_mean(loss_it_NN) * (region_r - region_l)
if R['wavelet'] == 1:
# |Uc*Uf|^2-->0
norm2UdU = tf.square(tf.multiply(U_NN_Normal, U_NN_freqs))
UNN_dot_UNN = tf.reduce_mean(norm2UdU, axis=0)
elif R['wavelet'] == 2:
# |a(x)*(grad Uc)*(grad Uf)|^2-->0
dU_dot_dU = tf.multiply(dU_NN_Normal, dU_NN_freqs)
sum2dUdU = tf.reshape(tf.reduce_sum(dU_dot_dU, axis=-1),
shape=[-1, 1])
norm2AdUdU = tf.square(tf.multiply(a_eps, sum2dUdU))
# norm2AdUdU = tf.square(sum2dUdU)
UNN_dot_UNN = tf.reduce_mean(norm2AdUdU, axis=0)
else:
U_dot_U = tf.reduce_mean(tf.square(
tf.multiply(U_NN_Normal, U_NN_freqs)),
axis=0)
dU_dot_dU = tf.multiply(dU_NN_Normal, dU_NN_freqs)
sum2dUdU = tf.reshape(tf.reduce_sum(dU_dot_dU, axis=-1),
shape=[-1, 1])
norm2AdUdU = tf.square(tf.multiply(a_eps, sum2dUdU))
UNN_dot_UNN = tf.reduce_mean(norm2AdUdU, axis=0) + U_dot_U
elif R['variational_loss'] == 2:
dU_NN = tf.add(dU_NN_Normal, dU_NN_freqs)
if R['PDE_type'] == 'general_laplace':
laplace_norm2NN = tf.reduce_sum(tf.square(dU_NN), axis=-1)
loss_it_NN = (1.0 / 2) * tf.reshape(laplace_norm2NN, shape=[-1, 1]) - \
tf.multiply(tf.reshape(f(X_it), shape=[-1, 1]), U_NN)
elif R['PDE_type'] == 'p_laplace':
# a_eps = A_eps(X_it) # * 行 1 列
a_eps = 1 / (2 + tf.cos(2 * np.pi * X_it / epsilon))
laplace_p_pow2NN = tf.reduce_sum(
a_eps * tf.pow(tf.abs(dU_NN), p_index), axis=-1)
loss_it_NN = (1.0 / p_index) * tf.reshape(laplace_p_pow2NN, shape=[-1, 1]) - \
tf.multiply(tf.reshape(f(X_it), shape=[-1, 1]), U_NN)
Loss_it2NN = tf.reduce_mean(loss_it_NN) * (region_r - region_l)
if R['wavelet'] == 1:
norm2UdU = tf.square(tf.multiply(U_NN_Normal, U_NN_freqs))
UNN_dot_UNN = tf.reduce_mean(norm2UdU, axis=0)
else:
UNN_dot_UNN = tf.constant(0.0)
Loss2UNN_dot_UNN = penalty2powU * UNN_dot_UNN
U_left = tf.reshape(u_left(X_left_bd), shape=[-1, 1])
U_right = tf.reshape(u_right(X_right_bd), shape=[-1, 1])
loss_bd_Normal = tf.square(ULeft_NN_Normal -
U_left) + tf.square(URight_NN_Normal -
U_right)
loss_bd_Freqs = tf.square(ULeft_NN_freqs -
U_left) + tf.square(URight_NN_freqs -
U_right)
Loss_bd2NN = tf.reduce_mean(loss_bd_Normal) + tf.reduce_mean(
loss_bd_Freqs)
if R['regular_weight_model'] == 'L1':
regular_WB_Normal = DNN_base.regular_weights_biases_L1(
W2NN_Normal, B2NN_Normal) # 正则化权重和偏置 L1正则化
regular_WB_Scale = DNN_base.regular_weights_biases_L1(
W2NN_freqs, B2NN_freqs) # 正则化权重和偏置 L1正则化
elif R['regular_weight_model'] == 'L2':
regular_WB_Normal = DNN_base.regular_weights_biases_L2(
W2NN_Normal, B2NN_Normal) # 正则化权重和偏置 L2正则化
regular_WB_Scale = DNN_base.regular_weights_biases_L2(
W2NN_freqs, B2NN_freqs) # 正则化权重和偏置 L2正则化
else:
regular_WB_Normal = tf.constant(0.0) # 无正则化权重参数
regular_WB_Scale = tf.constant(0.0)
penalty_Weigth_Bias = wb_regular * (regular_WB_Normal +
regular_WB_Scale)
Loss2NN = Loss_it2NN + bd_penalty * Loss_bd2NN + Loss2UNN_dot_UNN + penalty_Weigth_Bias
my_optimizer = tf.train.AdamOptimizer(in_learning_rate)
if R['variational_loss'] == 1:
if R['train_group'] == 1:
train_op1 = my_optimizer.minimize(Loss_it2NN,
global_step=global_steps)
train_op2 = my_optimizer.minimize(Loss_bd2NN,
global_step=global_steps)
train_op3 = my_optimizer.minimize(Loss2UNN_dot_UNN,
global_step=global_steps)
train_op4 = my_optimizer.minimize(Loss2NN,
global_step=global_steps)
train_Loss2NN = tf.group(train_op1, train_op2, train_op3,
train_op4)
elif R['train_group'] == 2:
train_op1 = my_optimizer.minimize(Loss2NN,
global_step=global_steps)
train_op2 = my_optimizer.minimize(Loss_bd2NN,
global_step=global_steps)
train_op3 = my_optimizer.minimize(Loss2UNN_dot_UNN,
global_step=global_steps)
train_Loss2NN = tf.group(train_op1, train_op2, train_op3)
else:
train_Loss2NN = my_optimizer.minimize(
Loss2NN, global_step=global_steps)
elif R['variational_loss'] == 2:
if R['train_group'] == 1:
train_op1 = my_optimizer.minimize(Loss_it2NN,
global_step=global_steps)
train_op2 = my_optimizer.minimize(Loss_bd2NN,
global_step=global_steps)
train_op3 = my_optimizer.minimize(Loss2NN,
global_step=global_steps)
train_Loss2NN = tf.group(train_op1, train_op2, train_op3)
elif R['train_group'] == 2:
train_op1 = my_optimizer.minimize(Loss2NN,
global_step=global_steps)
train_op2 = my_optimizer.minimize(Loss_bd2NN,
global_step=global_steps)
train_Loss2NN = tf.group(train_op1, train_op2)
else:
train_Loss2NN = my_optimizer.minimize(
Loss2NN, global_step=global_steps)
# 训练上的真解值和训练结果的误差
U_true = u_true(X_it)
train_mse_NN = tf.reduce_mean(tf.square(U_true - U_NN))
train_rel_NN = train_mse_NN / tf.reduce_mean(tf.square(U_true))
t0 = time.time()
# 空列表, 使用 append() 添加元素
lossIt_all2NN, lossBD_all2NN, loss_all2NN, UDU_NN, train_mse_all2NN, train_rel_all2NN = [], [], [], [], [], []
test_mse_all2NN, test_rel_all2NN = [], []
test_epoch = []
test_batch_size = 1000
test_x_bach = np.reshape(
np.linspace(region_l, region_r, num=test_batch_size), [-1, 1])
saveData.save_testData_or_solus2mat(test_x_bach,
dataName='testx',
outPath=R['FolderName'])
# ConfigProto 加上allow_soft_placement=True就可以使用 gpu 了
config = tf.ConfigProto(allow_soft_placement=True) # 创建sess的时候对sess进行参数配置
config.gpu_options.allow_growth = True # True是让TensorFlow在运行过程中动态申请显存，避免过多的显存占用。
config.allow_soft_placement = True # 当指定的设备不存在时，允许选择一个存在的设备运行。比如gpu不存在，自动降到cpu上运行
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
tmp_lr = learning_rate
for i_epoch in range(R['max_epoch'] + 1):
x_it_batch = DNN_data.rand_it(batchsize_it,
input_dim,
region_a=region_l,
region_b=region_r)
xl_bd_batch, xr_bd_batch = DNN_data.rand_bd_1D(batchsize_bd,
input_dim,
region_a=region_l,
region_b=region_r)
tmp_lr = tmp_lr * (1 - lr_decay)
if R['activate_penalty2bd_increase'] == 1:
if i_epoch < int(R['max_epoch'] / 10):
temp_penalty_bd = bd_penalty_init
elif i_epoch < int(R['max_epoch'] / 5):
temp_penalty_bd = 10 * bd_penalty_init
elif i_epoch < int(R['max_epoch'] / 4):
temp_penalty_bd = 50 * bd_penalty_init
elif i_epoch < int(R['max_epoch'] / 2):
temp_penalty_bd = 100 * bd_penalty_init
elif i_epoch < int(3 * R['max_epoch'] / 4):
temp_penalty_bd = 200 * bd_penalty_init
else:
temp_penalty_bd = 500 * bd_penalty_init
elif R['activate_penalty2bd_increase'] == 2:
if i_epoch < int(R['max_epoch'] / 10):
temp_penalty_bd = 5 * bd_penalty_init
elif i_epoch < int(R['max_epoch'] / 5):
temp_penalty_bd = 1 * bd_penalty_init
elif i_epoch < int(R['max_epoch'] / 4):
temp_penalty_bd = 0.5 * bd_penalty_init
elif i_epoch < int(R['max_epoch'] / 2):
temp_penalty_bd = 0.1 * bd_penalty_init
elif i_epoch < int(3 * R['max_epoch'] / 4):
temp_penalty_bd = 0.05 * bd_penalty_init
else:
temp_penalty_bd = 0.02 * bd_penalty_init
else:
temp_penalty_bd = bd_penalty_init
if R['activate_powSolus_increase'] == 1:
if i_epoch < int(R['max_epoch'] / 10):
temp_penalty_powU = init_penalty2powU
elif i_epoch < int(R['max_epoch'] / 5):
temp_penalty_powU = 10 * init_penalty2powU
elif i_epoch < int(R['max_epoch'] / 4):
temp_penalty_powU = 50 * init_penalty2powU
elif i_epoch < int(R['max_epoch'] / 2):
temp_penalty_powU = 100 * init_penalty2powU
elif i_epoch < int(3 * R['max_epoch'] / 4):
temp_penalty_powU = 200 * init_penalty2powU
else:
temp_penalty_powU = 500 * init_penalty2powU
elif R['activate_powSolus_increase'] == 2:
if i_epoch < int(R['max_epoch'] / 10):
temp_penalty_powU = 5 * init_penalty2powU
elif i_epoch < int(R['max_epoch'] / 5):
temp_penalty_powU = 1 * init_penalty2powU
elif i_epoch < int(R['max_epoch'] / 4):
temp_penalty_powU = 0.5 * init_penalty2powU
elif i_epoch < int(R['max_epoch'] / 2):
temp_penalty_powU = 0.1 * init_penalty2powU
elif i_epoch < int(3 * R['max_epoch'] / 4):
temp_penalty_powU = 0.05 * init_penalty2powU
else:
temp_penalty_powU = 0.02 * init_penalty2powU
else:
temp_penalty_powU = init_penalty2powU
p_WB = 0.0
_, loss_it_nn, loss_bd_nn, loss_nn, udu_nn, train_mse_nn, train_rel_nn = sess.run(
[
train_Loss2NN, Loss_it2NN, Loss_bd2NN, Loss2NN,
UNN_dot_UNN, train_mse_NN, train_rel_NN
],
feed_dict={
X_it: x_it_batch,
X_left_bd: xl_bd_batch,
X_right_bd: xr_bd_batch,
in_learning_rate: tmp_lr,
bd_penalty: temp_penalty_bd,
penalty2powU: temp_penalty_powU
})
lossIt_all2NN.append(loss_it_nn)
lossBD_all2NN.append(loss_bd_nn)
loss_all2NN.append(loss_nn)
UDU_NN.append(udu_nn)
train_mse_all2NN.append(train_mse_nn)
train_rel_all2NN.append(train_rel_nn)
if i_epoch % 1000 == 0:
run_times = time.time() - t0
DNN_tools.print_and_log_train_one_epoch(i_epoch,
run_times,
tmp_lr,
temp_penalty_bd,
temp_penalty_powU,
p_WB,
loss_it_nn,
loss_bd_nn,
loss_nn,
udu_nn,
train_mse_nn,
train_rel_nn,
log_out=log_fileout_NN)
# --------------------------- test network ----------------------------------------------
test_epoch.append(i_epoch / 1000)
train_option = False
u_true2test, utest_nn, u_nn_normal, u_nn_scale = sess.run(
[U_true, U_NN, U_NN_Normal, U_NN_freqs],
feed_dict={
X_it: test_x_bach,
train_opt: train_option
})
test_mse2nn = np.mean(np.square(u_true2test - utest_nn))
test_mse_all2NN.append(test_mse2nn)
test_rel2nn = test_mse2nn / np.mean(np.square(u_true2test))
test_rel_all2NN.append(test_rel2nn)
DNN_tools.print_and_log_test_one_epoch(test_mse2nn,
test_rel2nn,
log_out=log_fileout_NN)
# ----------------------- save training results to mat files, then plot them ---------------------------------
saveData.save_trainLoss2mat_1actFunc(lossIt_all2NN,
lossBD_all2NN,
loss_all2NN,
actName=act_func1,
outPath=R['FolderName'])
saveData.save_train_MSE_REL2mat(train_mse_all2NN,
train_rel_all2NN,
actName=act_func1,
outPath=R['FolderName'])
plotData.plotTrain_loss_1act_func(lossIt_all2NN,
lossType='loss_it',
seedNo=R['seed'],
outPath=R['FolderName'])
plotData.plotTrain_loss_1act_func(lossBD_all2NN,
lossType='loss_bd',
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
plotData.plotTrain_loss_1act_func(loss_all2NN,
lossType='loss',
seedNo=R['seed'],
outPath=R['FolderName'])
plotData.plotTrain_MSE_REL_1act_func(train_mse_all2NN,
train_rel_all2NN,
actName=act_func2,
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
# ---------------------- save testing results to mat files, then plot them --------------------------------
saveData.save_testData_or_solus2mat(u_true2test,
dataName='Utrue',
outPath=R['FolderName'])
saveData.save_testData_or_solus2mat(utest_nn,
dataName=act_func1,
outPath=R['FolderName'])
saveData.save_testData_or_solus2mat(u_nn_normal,
dataName='normal',
outPath=R['FolderName'])
saveData.save_testData_or_solus2mat(u_nn_scale,
dataName='scale',
outPath=R['FolderName'])
saveData.save_testMSE_REL2mat(test_mse_all2NN,
test_rel_all2NN,
actName=act_func2,
outPath=R['FolderName'])
plotData.plotTest_MSE_REL(test_mse_all2NN,
test_rel_all2NN,
test_epoch,
actName=act_func2,
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
def solve_SIR2COVID(R):
log_out_path = R['FolderName'] # 将路径从字典 R 中提取出来
if not os.path.exists(log_out_path): # 判断路径是否已经存在
os.mkdir(log_out_path) # 无 log_out_path 路径，创建一个 log_out_path 路径
log_fileout = open(os.path.join(log_out_path, 'log_train.txt'),
'w') # 在这个路径下创建并打开一个可写的 log_train.txt文件
DNN_LogPrint.dictionary_out2file(R, log_fileout)
log2trianSolus = open(os.path.join(log_out_path, 'train_Solus.txt'),
'w') # 在这个路径下创建并打开一个可写的 log_train.txt文件
log2testSolus = open(os.path.join(log_out_path, 'test_Solus.txt'),
'w') # 在这个路径下创建并打开一个可写的 log_train.txt文件
log2testSolus2 = open(os.path.join(log_out_path, 'test_Solus_temp.txt'),
'w') # 在这个路径下创建并打开一个可写的 log_train.txt文件
log2testParas = open(os.path.join(log_out_path, 'test_Paras.txt'),
'w') # 在这个路径下创建并打开一个可写的 log_train.txt文件
trainSet_szie = R['size2train']
batchSize_train = R['batch_size2train']
batchSize_test = R['batch_size2test']
pt_penalty_init = R[
'init_penalty2predict_true'] # Regularization parameter for difference of predict and true
wb_penalty = R['regular_weight'] # Regularization parameter for weights
lr_decay = R['lr_decay']
learning_rate = R['learning_rate']
act_func2SIR = R['act2sir']
act_func2paras = R['act2paras']
input_dim = R['input_dim']
out_dim = R['output_dim']
flag2S = 'WB2S'
flag2I = 'WB2I'
flag2R = 'WB2R'
flag2beta = 'WB2beta'
flag2gamma = 'WB2gamma'
hidden_sir = R['hidden2SIR']
hidden_para = R['hidden2para']
Weight2S, Bias2S = DNN.init_DNN(size2in=input_dim,
size2out=out_dim,
hiddens=hidden_sir,
scope=flag2S,
opt2Init=R['SIR_opt2init_NN'])
Weight2I, Bias2I = DNN.init_DNN(size2in=input_dim,
size2out=out_dim,
hiddens=hidden_sir,
scope=flag2I,
opt2Init=R['SIR_opt2init_NN'])
Weight2R, Bias2R = DNN.init_DNN(size2in=input_dim,
size2out=out_dim,
hiddens=hidden_sir,
scope=flag2R,
opt2Init=R['SIR_opt2init_NN'])
Weight2beta, Bias2beta = DNN.init_DNN(size2in=input_dim,
size2out=out_dim,
hiddens=hidden_para,
scope=flag2beta,
opt2Init=R['Para_opt2init_NN'])
Weight2gamma, Bias2gamma = DNN.init_DNN(size2in=input_dim,
size2out=out_dim,
hiddens=hidden_para,
scope=flag2gamma,
opt2Init=R['Para_opt2init_NN'])
global_steps = tf.Variable(0, trainable=False)
with tf.device('/gpu:%s' % (R['gpuNo'])):
with tf.variable_scope('vscope', reuse=tf.AUTO_REUSE):
T_it = tf.placeholder(tf.float32,
name='T_it',
shape=[None, input_dim])
I_observe = tf.placeholder(tf.float32,
name='I_observe',
shape=[None, input_dim])
N_observe = tf.placeholder(tf.float32,
name='N_observe',
shape=[None, input_dim])
predict_true_penalty = tf.placeholder_with_default(input=1e3,
shape=[],
name='bd_p')
in_learning_rate = tf.placeholder_with_default(input=1e-5,
shape=[],
name='lr')
train_opt = tf.placeholder_with_default(input=True,
shape=[],
name='train_opt')
SNN_temp = DNN.PDE_DNN(x=T_it,
hiddenLayer=hidden_sir,
Weigths=Weight2S,
Biases=Bias2S,
DNNmodel=R['model2sir'],
activation=act_func2SIR,
freqs=R['freqs'])
INN_temp = DNN.PDE_DNN(x=T_it,
hiddenLayer=hidden_sir,
Weigths=Weight2I,
Biases=Bias2I,
DNNmodel=R['model2sir'],
activation=act_func2SIR,
freqs=R['freqs'])
RNN_temp = DNN.PDE_DNN(x=T_it,
hiddenLayer=hidden_sir,
Weigths=Weight2R,
Biases=Bias2R,
DNNmodel=R['model2sir'],
activation=act_func2SIR,
freqs=R['freqs'])
in_beta = DNN.PDE_DNN(x=T_it,
hiddenLayer=hidden_para,
Weigths=Weight2beta,
Biases=Bias2beta,
DNNmodel=R['model2paras'],
activation=act_func2paras,
freqs=R['freqs'])
in_gamma = DNN.PDE_DNN(x=T_it,
hiddenLayer=hidden_para,
Weigths=Weight2gamma,
Biases=Bias2gamma,
DNNmodel=R['model2paras'],
activation=act_func2paras,
freqs=R['freqs'])
# Remark: beta, gamma,S_NN.I_NN,R_NN都应该是正的. beta.1--15之间，gamma在(0,1）使用归一化的话S_NN.I_NN,R_NN都在[0,1)范围内
if (R['total_population']
== R['scale_population']) and R['scale_population'] != 1:
beta = in_beta
gamma = in_gamma
# SNN = SNN_temp
# INN = INN_temp
# RNN = RNN_temp
# SNN = tf.nn.relu(SNN_temp)
# INN = tf.nn.relu(INN_temp)
# RNN = tf.nn.relu(RNN_temp)
# SNN = tf.abs(SNN_temp)
# INN = tf.abs(INN_temp)
# RNN = tf.abs(RNN_temp)
# SNN = DNN_base.gauss(SNN_temp)
# INN = tf.square(INN_temp)
# RNN = tf.square(RNN_temp)
# SNN = DNN_base.gauss(SNN_temp)
# INN = tf.square(INN_temp)
# RNN = tf.nn.sigmoid(RNN_temp)
# SNN = DNN_base.gauss(SNN_temp)
# INN = tf.nn.sigmoid(INN_temp)
# RNN = tf.square(RNN_temp)
# SNN = tf.sqrt(tf.square(SNN_temp))
# INN = tf.sqrt(tf.square(INN_temp))
# RNN = tf.sqrt(tf.square(RNN_temp))
SNN = tf.nn.sigmoid(SNN_temp)
INN = tf.nn.sigmoid(INN_temp)
RNN = tf.nn.sigmoid(RNN_temp)
else:
beta = in_beta
gamma = in_gamma
# SNN = SNN_temp
# INN = INN_temp
# RNN = RNN_temp
# SNN = tf.nn.relu(SNN_temp)
# INN = tf.nn.relu(INN_temp)
# RNN = tf.nn.relu(RNN_temp)
SNN = tf.nn.sigmoid(SNN_temp)
INN = tf.nn.sigmoid(INN_temp)
RNN = tf.nn.sigmoid(RNN_temp)
N_NN = SNN + INN + RNN
dSNN2t = tf.gradients(SNN, T_it)[0]
dINN2t = tf.gradients(INN, T_it)[0]
dRNN2t = tf.gradients(RNN, T_it)[0]
dN_NN2t = tf.gradients(N_NN, T_it)[0]
temp_snn2t = -beta * SNN * INN
temp_inn2t = beta * SNN * INN - gamma * INN
temp_rnn2t = gamma * INN
if str.lower(
R['loss_function']) == 'l2_loss' and R['scale_up'] == 0:
# LossS_Net_obs = tf.reduce_mean(tf.square(SNN - S_observe))
LossI_Net_obs = tf.reduce_mean(tf.square(INN - I_observe))
# LossR_Net_obs = tf.reduce_mean(tf.square(RNN - R_observe))
LossN_Net_obs = tf.reduce_mean(tf.square(N_NN - N_observe))
Loss2dS = tf.reduce_mean(tf.square(dSNN2t - temp_snn2t))
Loss2dI = tf.reduce_mean(tf.square(dINN2t - temp_inn2t))
Loss2dR = tf.reduce_mean(tf.square(dRNN2t - temp_rnn2t))
Loss2dN = tf.reduce_mean(tf.square(dN_NN2t))
elif str.lower(
R['loss_function']) == 'l2_loss' and R['scale_up'] == 1:
scale_up = R['scale_factor']
# LossS_Net_obs = tf.reduce_mean(tf.square(scale_up*SNN - scale_up*S_observe))
LossI_Net_obs = tf.reduce_mean(
tf.square(scale_up * INN - scale_up * I_observe))
# LossR_Net_obs = tf.reduce_mean(tf.square(scale_up*RNN - scale_up*R_observe))
LossN_Net_obs = tf.reduce_mean(
tf.square(scale_up * N_NN - scale_up * N_observe))
Loss2dS = tf.reduce_mean(
tf.square(scale_up * dSNN2t - scale_up * temp_snn2t))
Loss2dI = tf.reduce_mean(
tf.square(scale_up * dINN2t - scale_up * temp_inn2t))
Loss2dR = tf.reduce_mean(
tf.square(scale_up * dRNN2t - scale_up * temp_rnn2t))
Loss2dN = tf.reduce_mean(tf.square(scale_up * dN_NN2t))
elif str.lower(R['loss_function']) == 'lncosh_loss':
# LossS_Net_obs = tf.reduce_mean(tf.ln(tf.cosh(SNN - S_observe)))
LossI_Net_obs = tf.reduce_mean(tf.log(tf.cosh(INN -
I_observe)))
# LossR_Net_obs = tf.reduce_mean(tf.log(tf.cosh(RNN - R_observe)))
LossN_Net_obs = tf.reduce_mean(
tf.log(tf.cosh(N_NN - N_observe)))
Loss2dS = tf.reduce_mean(tf.log(tf.cosh(dSNN2t - temp_snn2t)))
Loss2dI = tf.reduce_mean(tf.log(tf.cosh(dINN2t - temp_inn2t)))
Loss2dR = tf.reduce_mean(tf.log(tf.cosh(dRNN2t - temp_rnn2t)))
Loss2dN = tf.reduce_mean(tf.log(tf.cosh(dN_NN2t)))
if R['regular_weight_model'] == 'L1':
regular_WB2S = DNN_base.regular_weights_biases_L1(
Weight2S, Bias2S)
regular_WB2I = DNN_base.regular_weights_biases_L1(
Weight2I, Bias2I)
regular_WB2R = DNN_base.regular_weights_biases_L1(
Weight2R, Bias2R)
regular_WB2Beta = DNN_base.regular_weights_biases_L1(
Weight2beta, Bias2beta)
regular_WB2Gamma = DNN_base.regular_weights_biases_L1(
Weight2gamma, Bias2gamma)
elif R['regular_weight_model'] == 'L2':
regular_WB2S = DNN_base.regular_weights_biases_L2(
Weight2S, Bias2S)
regular_WB2I = DNN_base.regular_weights_biases_L2(
Weight2I, Bias2I)
regular_WB2R = DNN_base.regular_weights_biases_L2(
Weight2R, Bias2R)
regular_WB2Beta = DNN_base.regular_weights_biases_L2(
Weight2beta, Bias2beta)
regular_WB2Gamma = DNN_base.regular_weights_biases_L2(
Weight2gamma, Bias2gamma)
else:
regular_WB2S = tf.constant(0.0)
regular_WB2I = tf.constant(0.0)
regular_WB2R = tf.constant(0.0)
regular_WB2Beta = tf.constant(0.0)
regular_WB2Gamma = tf.constant(0.0)
PWB2S = wb_penalty * regular_WB2S
PWB2I = wb_penalty * regular_WB2I
PWB2R = wb_penalty * regular_WB2R
PWB2Beta = wb_penalty * regular_WB2Beta
PWB2Gamma = wb_penalty * regular_WB2Gamma
Loss2S = Loss2dS + PWB2S
Loss2I = predict_true_penalty * LossI_Net_obs + Loss2dI + PWB2I
Loss2R = Loss2dR + PWB2R
Loss2N = predict_true_penalty * LossN_Net_obs + Loss2dN
Loss = Loss2S + Loss2I + Loss2R + Loss2N + PWB2Beta + PWB2Gamma
my_optimizer = tf.train.AdamOptimizer(in_learning_rate)
if R['train_model'] == 'train_group':
train_Loss2S = my_optimizer.minimize(Loss2S,
global_step=global_steps)
train_Loss2I = my_optimizer.minimize(Loss2I,
global_step=global_steps)
train_Loss2R = my_optimizer.minimize(Loss2R,
global_step=global_steps)
train_Loss2N = my_optimizer.minimize(Loss2N,
global_step=global_steps)
train_Loss = my_optimizer.minimize(Loss,
global_step=global_steps)
train_Losses = tf.group(train_Loss2S, train_Loss2I,
train_Loss2R, train_Loss2N, train_Loss)
elif R['train_model'] == 'train_union_loss':
train_Losses = my_optimizer.minimize(Loss,
global_step=global_steps)
t0 = time.time()
loss_s_all, loss_i_all, loss_r_all, loss_n_all, loss_all = [], [], [], [], []
test_epoch = []
test_mse2I_all, test_rel2I_all = [], []
# filename = 'data2csv/Wuhan.csv'
# filename = 'data2csv/Italia_data.csv'
filename = 'data2csv/Korea_data.csv'
date, data = DNN_data.load_csvData(filename)
assert (trainSet_szie + batchSize_test <= len(data))
train_date, train_data2i, test_date, test_data2i = \
DNN_data.split_csvData2train_test(date, data, size2train=trainSet_szie, normalFactor=R['scale_population'])
if R['scale_population'] == 1:
nbatch2train = np.ones(batchSize_train, dtype=np.float32) * float(
R['total_population'])
elif (R['total_population'] !=
R['scale_population']) and R['scale_population'] != 1:
nbatch2train = np.ones(batchSize_train, dtype=np.float32) * (
float(R['total_population']) / float(R['scale_population']))
elif (R['total_population']
== R['scale_population']) and R['scale_population'] != 1:
nbatch2train = np.ones(batchSize_train, dtype=np.float32)
# 对于时间数据来说，验证模型的合理性，要用连续的时间数据验证
test_t_bach = DNN_data.sample_testDays_serially(test_date, batchSize_test)
i_obs_test = DNN_data.sample_testData_serially(test_data2i,
batchSize_test,
normalFactor=1.0)
print('The test data about i:\n', str(np.transpose(i_obs_test)))
print('\n')
DNN_tools.log_string(
'The test data about i:\n%s\n' % str(np.transpose(i_obs_test)),
log_fileout)
# ConfigProto 加上allow_soft_placement=True就可以使用 gpu 了
config = tf.ConfigProto(allow_soft_placement=True) # 创建sess的时候对sess进行参数配置
config.gpu_options.allow_growth = True # True是让TensorFlow在运行过程中动态申请显存，避免过多的显存占用。
config.allow_soft_placement = True # 当指定的设备不存在时，允许选择一个存在的设备运行。比如gpu不存在，自动降到cpu上运行
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
tmp_lr = learning_rate
for i_epoch in range(R['max_epoch'] + 1):
t_batch, i_obs = \
DNN_data.randSample_Normalize_existData(train_date, train_data2i, batchsize=batchSize_train,
normalFactor=1.0, sampling_opt=R['opt2sample'])
n_obs = nbatch2train.reshape(batchSize_train, 1)
tmp_lr = tmp_lr * (1 - lr_decay)
train_option = True
if R['activate_stage_penalty'] == 1:
if i_epoch < int(R['max_epoch'] / 10):
temp_penalty_pt = pt_penalty_init
elif i_epoch < int(R['max_epoch'] / 5):
temp_penalty_pt = 10 * pt_penalty_init
elif i_epoch < int(R['max_epoch'] / 4):
temp_penalty_pt = 50 * pt_penalty_init
elif i_epoch < int(R['max_epoch'] / 2):
temp_penalty_pt = 100 * pt_penalty_init
elif i_epoch < int(3 * R['max_epoch'] / 4):
temp_penalty_pt = 200 * pt_penalty_init
else:
temp_penalty_pt = 500 * pt_penalty_init
elif R['activate_stage_penalty'] == 2:
if i_epoch < int(R['max_epoch'] / 3):
temp_penalty_pt = pt_penalty_init
elif i_epoch < 2 * int(R['max_epoch'] / 3):
temp_penalty_pt = 10 * pt_penalty_init
else:
temp_penalty_pt = 50 * pt_penalty_init
else:
temp_penalty_pt = pt_penalty_init
_, loss_s, loss_i, loss_r, loss_n, loss, pwb2s, pwb2i, pwb2r = sess.run(
[
train_Losses, Loss2S, Loss2I, Loss2R, Loss2N, Loss, PWB2S,
PWB2I, PWB2R
],
feed_dict={
T_it: t_batch,
I_observe: i_obs,
N_observe: n_obs,
in_learning_rate: tmp_lr,
train_opt: train_option,
predict_true_penalty: temp_penalty_pt
})
loss_s_all.append(loss_s)
loss_i_all.append(loss_i)
loss_r_all.append(loss_r)
loss_n_all.append(loss_n)
loss_all.append(loss)
if i_epoch % 1000 == 0:
# 以下代码为输出训练过程中 S_NN, I_NN, R_NN, beta, gamma 的训练结果
DNN_LogPrint.print_and_log2train(i_epoch,
time.time() - t0,
tmp_lr,
temp_penalty_pt,
pwb2s,
pwb2i,
pwb2r,
loss_s,
loss_i,
loss_r,
loss_n,
loss,
log_out=log_fileout)
s_nn2train, i_nn2train, r_nn2train = sess.run(
[SNN, INN, RNN],
feed_dict={T_it: np.reshape(train_date, [-1, 1])})
# 以下代码为输出训练过程中 S_NN, I_NN, R_NN, beta, gamma 的测试结果
test_epoch.append(i_epoch / 1000)
train_option = False
s_nn2test, i_nn2test, r_nn2test, beta_test, gamma_test = sess.run(
[SNN, INN, RNN, beta, gamma],
feed_dict={
T_it: test_t_bach,
train_opt: train_option
})
point_ERR2I = np.square(i_nn2test - i_obs_test)
test_mse2I = np.mean(point_ERR2I)
test_mse2I_all.append(test_mse2I)
test_rel2I = test_mse2I / np.mean(np.square(i_obs_test))
test_rel2I_all.append(test_rel2I)
DNN_tools.print_and_log_test_one_epoch(test_mse2I,
test_rel2I,
log_out=log_fileout)
DNN_tools.log_string(
'------------------The epoch----------------------: %s\n' %
str(i_epoch), log2testSolus)
DNN_tools.log_string(
'The test result for s:\n%s\n' %
str(np.transpose(s_nn2test)), log2testSolus)
DNN_tools.log_string(
'The test result for i:\n%s\n' %
str(np.transpose(i_nn2test)), log2testSolus)
DNN_tools.log_string(
'The test result for r:\n%s\n\n' %
str(np.transpose(r_nn2test)), log2testSolus)
# --------以下代码为输出训练过程中 S_NN_temp, I_NN_temp, R_NN_temp, in_beta, in_gamma 的测试结果-------------
s_nn_temp2test, i_nn_temp2test, r_nn_temp2test, in_beta_test, in_gamma_test = sess.run(
[SNN_temp, INN_temp, RNN_temp, in_beta, in_gamma],
feed_dict={
T_it: test_t_bach,
train_opt: train_option
})
DNN_tools.log_string(
'------------------The epoch----------------------: %s\n' %
str(i_epoch), log2testSolus2)
DNN_tools.log_string(
'The test result for s_temp:\n%s\n' %
str(np.transpose(s_nn_temp2test)), log2testSolus2)
DNN_tools.log_string(
'The test result for i_temp:\n%s\n' %
str(np.transpose(i_nn_temp2test)), log2testSolus2)
DNN_tools.log_string(
'The test result for r_temp:\n%s\n\n' %
str(np.transpose(r_nn_temp2test)), log2testSolus2)
DNN_tools.log_string(
'------------------The epoch----------------------: %s\n' %
str(i_epoch), log2testParas)
DNN_tools.log_string(
'The test result for in_beta:\n%s\n' %
str(np.transpose(in_beta_test)), log2testParas)
DNN_tools.log_string(
'The test result for in_gamma:\n%s\n' %
str(np.transpose(in_gamma_test)), log2testParas)
DNN_tools.log_string(
'The train result for S:\n%s\n' % str(np.transpose(s_nn2train)),
log2trianSolus)
DNN_tools.log_string(
'The train result for I:\n%s\n' % str(np.transpose(i_nn2train)),
log2trianSolus)
DNN_tools.log_string(
'The train result for R:\n%s\n\n' % str(np.transpose(r_nn2train)),
log2trianSolus)
saveData.true_value2convid(train_data2i,
name2Array='itrue2train',
outPath=R['FolderName'])
saveData.save_Solu2mat_Covid(s_nn2train,
name2solus='s2train',
outPath=R['FolderName'])
saveData.save_Solu2mat_Covid(i_nn2train,
name2solus='i2train',
outPath=R['FolderName'])
saveData.save_Solu2mat_Covid(r_nn2train,
name2solus='r2train',
outPath=R['FolderName'])
saveData.save_SIR_trainLoss2mat_Covid(loss_s_all,
loss_i_all,
loss_r_all,
loss_n_all,
actName=act_func2SIR,
outPath=R['FolderName'])
plotData.plotTrain_loss_1act_func(loss_s_all,
lossType='loss2s',
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
plotData.plotTrain_loss_1act_func(loss_i_all,
lossType='loss2i',
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
plotData.plotTrain_loss_1act_func(loss_r_all,
lossType='loss2r',
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
plotData.plotTrain_loss_1act_func(loss_n_all,
lossType='loss2n',
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
saveData.true_value2convid(i_obs_test,
name2Array='i_true2test',
outPath=R['FolderName'])
saveData.save_testMSE_REL2mat(test_mse2I_all,
test_rel2I_all,
actName='Infected',
outPath=R['FolderName'])
plotData.plotTest_MSE_REL(test_mse2I_all,
test_rel2I_all,
test_epoch,
actName='Infected',
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
saveData.save_SIR_testSolus2mat_Covid(s_nn2test,
i_nn2test,
r_nn2test,
name2solus1='snn2test',
name2solus2='inn2test',
name2solus3='rnn2test',
outPath=R['FolderName'])
saveData.save_SIR_testParas2mat_Covid(beta_test,
gamma_test,
name2para1='beta2test',
name2para2='gamma2test',
outPath=R['FolderName'])
plotData.plot_testSolu2convid(i_obs_test,
name2solu='i_true',
coord_points2test=test_t_bach,
outPath=R['FolderName'])
plotData.plot_testSolu2convid(s_nn2test,
name2solu='s_test',
coord_points2test=test_t_bach,
outPath=R['FolderName'])
plotData.plot_testSolu2convid(i_nn2test,
name2solu='i_test',
coord_points2test=test_t_bach,
outPath=R['FolderName'])
plotData.plot_testSolu2convid(r_nn2test,
name2solu='r_test',
coord_points2test=test_t_bach,
outPath=R['FolderName'])
plotData.plot_testSolus2convid(i_obs_test,
i_nn2test,
name2solu1='i_true',
name2solu2='i_test',
coord_points2test=test_t_bach,
seedNo=R['seed'],
outPath=R['FolderName'])
plotData.plot_testSolu2convid(beta_test,
name2solu='beta_test',
coord_points2test=test_t_bach,
outPath=R['FolderName'])
plotData.plot_testSolu2convid(gamma_test,
name2solu='gamma_test',
coord_points2test=test_t_bach,
outPath=R['FolderName'])
