def print_and_log_test_one_epoch(mse2test, res2test, log_out=None):
# 将运行结果打印出来
print('mean square error of predict and real for testing: %.10f' %
mse2test)
print('residual error of predict and real for testing: %.10f\n' % res2test)
DNN_tools.log_string(
'mean square error of predict and real for testing: %.10f' % mse2test,
log_out)
DNN_tools.log_string(
'residual error of predict and real for testing: %.10f\n\n' % res2test,
log_out)
def dictionary_out2file(R_dic,
log_fileout,
actName2normal=None,
actName2scale=None):
DNN_tools.log_string('PDE type for problem: %s\n' % (R_dic['PDE_type']),
log_fileout)
DNN_tools.log_string(
'Equation name for problem: %s\n' % (R_dic['equa_name']), log_fileout)
DNN_tools.log_string(
'The order to p-laplace: %s\n' % (R_dic['order2laplace']), log_fileout)
DNN_tools.log_string('The epsilon to p-laplace: %s\n' % (R_dic['epsilon']),
log_fileout)
DNN_tools.log_string(
'Network model of solving normal-part: %s\n' %
str(R_dic['model2normal']), log_fileout)
DNN_tools.log_string(
'Network model of solving scale-part: %s\n' %
str(R_dic['model2scale']), log_fileout)
DNN_tools.log_string(
'Activate function for normal-part network: %s\n' %
str(actName2normal), log_fileout)
DNN_tools.log_string(
'Activate function for scale-part network: %s\n' % str(actName2scale),
log_fileout)
DNN_tools.log_string(
'hidden layer to normal:%s\n' % str(R_dic['hidden2normal']),
log_fileout)
DNN_tools.log_string(
'hidden layer to scale :%s\n' % str(R_dic['hidden2scale']),
log_fileout)
DNN_tools.log_string(
'The frequency to scale-part network: %s\n' % (R_dic['freqs']),
log_fileout)
if (R_dic['optimizer_name']).title() == 'Adam':
DNN_tools.log_string('optimizer:%s\n' % str(R_dic['optimizer_name']),
log_fileout)
else:
DNN_tools.log_string(
'optimizer:%s with momentum=%f\n' %
(R_dic['optimizer_name'], R_dic['momentum']), log_fileout)
if R_dic['variational_loss'] == 1 or R_dic['variational_loss'] == 2:
DNN_tools.log_string(
'Loss function: variational loss ' +
str(R_dic['variational_loss']) + '\n', log_fileout)
else:
DNN_tools.log_string('Loss function: original function loss\n',
log_fileout)
if R_dic['variational_loss'] == 1:
if R_dic['wavelet'] == 1:
DNN_tools.log_string(
'Option of loss for coarse and fine is: L2 wavelet. \n',
log_fileout)
elif R_dic['wavelet'] == 2:
DNN_tools.log_string(
'Option of loss for coarse and fine is: Energy minimization. \n',
log_fileout)
else:
DNN_tools.log_string(
'Option of loss for coarse and fine is: L2 wavelet + Energy minimization. \n',
log_fileout)
if R_dic['variational_loss'] == 2:
if R_dic['wavelet'] == 1:
DNN_tools.log_string(
'Option of loss for coarse and fine is: L2 wavelet. \n',
log_fileout)
if R_dic['activate_stop'] != 0:
DNN_tools.log_string(
'activate the stop_step and given_step= %s\n' %
str(R_dic['max_epoch']), log_fileout)
else:
DNN_tools.log_string(
'no activate the stop_step and given_step = default: %s\n' %
str(R_dic['max_epoch']), log_fileout)
DNN_tools.log_string(
'Init learning rate: %s\n' % str(R_dic['learning_rate']), log_fileout)
DNN_tools.log_string(
'Decay to learning rate: %s\n' % str(R_dic['learning_rate_decay']),
log_fileout)
DNN_tools.log_string(
'Batch-size 2 interior: %s\n' % str(R_dic['batch_size2interior']),
log_fileout)
DNN_tools.log_string(
'Batch-size 2 boundary: %s\n' % str(R_dic['batch_size2boundary']),
log_fileout)
DNN_tools.log_string(
'Initial boundary penalty: %s\n' % str(R_dic['init_boundary_penalty']),
log_fileout)
if R_dic['activate_penalty2bd_increase'] == 1:
DNN_tools.log_string(
'The penalty of boundary will increase with training going on.\n',
log_fileout)
elif R_dic['activate_penalty2bd_increase'] == 2:
DNN_tools.log_string(
'The penalty of boundary will decrease with training going on.\n',
log_fileout)
else:
DNN_tools.log_string(
'The penalty of boundary will keep unchanged with training going on.\n',
log_fileout)
def solve_Integral_Equa(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
outFile2para_name = '%s_%s.txt' % ('para2', 'beta')
logfile_name = '%s%s.txt' % ('log2', R['activate_func'])
log_fileout = open(os.path.join(log_out_path, logfile_name),
'w') # 在这个路径下创建并打开一个可写的 log_train.txt文件
para_outFile = open(os.path.join(log_out_path, outFile2para_name),
'w') # 在这个路径下创建并打开一个可写的 para_outFile.txt文件
DNN_Log_Print.dictionary_out2file(R, log_fileout)
# 方程问题需要的设置
batchsize2bS = R['batch_size2bS']
batchsize2b = R['batch_size2y_b']
batchsize2integral = R['batch_size2integral']
wb_regular = R['regular_weight_biases']
if R['training_strategy'] == 'Alter_train':
init_lr2b = R['init_learning_rate2b']
init_lr2S = R['init_learning_rate2S']
lr_decay2b = R['learning_rate_decay2b']
lr_decay2S = R['learning_rate_decay2S']
else:
lr_decay = R['learning_rate_decay']
init_lr = R['init_learning_rate']
hidden_layers = R['hidden_layers']
act_func = R['activate_func']
# ------- set the problem ---------
data_indim = R['input_dim']
para_dim = R['estimate_para_dim']
# bNN是否和参数 beta 显式相关，即参数 beta 作为bNN的一个输入。是：数据维数+参数维数；否：数据维数
if R['bnn2beta'] == 'explicitly_related':
bNN_inDim = data_indim + para_dim
else:
bNN_inDim = data_indim
# 初始化权重和和偏置的模式。bNN是一个整体的网络表示，还是分为几个独立的网格表示 b 的每一个分量 bi. b=(b0,b1,b2,......)
if R['sub_networks'] == 'subDNNs':
flag_b0 = 'bnn0'
flag_b1 = 'bnn1'
bNN_outDim = 1
if R['name2network'] == 'DNN_Cos_C_Sin_Base':
W2b0, B2b0 = DNN_base.initialize_NN_random_normal2_CS(
bNN_inDim,
bNN_outDim,
hidden_layers,
flag_b0,
unit_const_Init1layer=False)
W2b1, B2b1 = DNN_base.initialize_NN_random_normal2_CS(
bNN_inDim,
bNN_outDim,
hidden_layers,
flag_b1,
unit_const_Init1layer=False)
else:
W2b0, B2b0 = DNN_base.initialize_NN_random_normal2(
bNN_inDim,
bNN_outDim,
hidden_layers,
flag_b0,
unit_const_Init1layer=False)
W2b1, B2b1 = DNN_base.initialize_NN_random_normal2(
bNN_inDim,
bNN_outDim,
hidden_layers,
flag_b0,
unit_const_Init1layer=False)
else:
bNN_outDim = para_dim
flag_b = 'bnn'
if R['name2network'] == 'DNN_Cos_C_Sin_Base':
W2b, B2b = DNN_base.initialize_NN_random_normal2_CS(
bNN_inDim,
bNN_outDim,
hidden_layers,
flag_b,
unit_const_Init1layer=False)
else:
W2b, B2b = DNN_base.initialize_NN_random_normal2(
bNN_inDim,
bNN_outDim,
hidden_layers,
flag_b,
unit_const_Init1layer=False)
global_steps = tf.Variable(0, trainable=False)
with tf.device('/gpu:%s' % (R['gpuNo'])):
with tf.variable_scope('vscope', reuse=tf.AUTO_REUSE):
X = tf.placeholder(tf.float32,
name='X_recv',
shape=[batchsize2bS, data_indim])
Y = tf.placeholder(tf.float32,
name='Y_recv',
shape=[batchsize2bS, data_indim])
R2XY = tf.placeholder(tf.float32,
name='R2XY_recv',
shape=[batchsize2bS, data_indim])
tfOne = tf.placeholder(tf.float32,
shape=[batchsize2bS, data_indim],
name='tfOne')
y = tf.placeholder(tf.float32,
name='y_recv',
shape=[batchsize2b, data_indim])
t_inte = tf.placeholder(tf.float32,
name='t_integral',
shape=[batchsize2integral, data_indim])
beta = tf.Variable(tf.random.uniform([1, para_dim]),
dtype='float32',
name='beta')
# beta = tf.Variable([[0.25, -0.5]], dtype='float32', name='beta')
# beta = tf.constant([[0.25, -0.5]], dtype=tf.float32, name='beta')
inline_lr = tf.placeholder_with_default(
input=1e-2, shape=[], name='inline_learning_rate')
inline_lr2b = tf.placeholder_with_default(
input=1e-2, shape=[], name='inline_learning_rate2b')
inline_lr2S = tf.placeholder_with_default(
input=1e-2, shape=[], name='inline_learning_rate2S')
if R['bnn2beta'] == 'explicitly_related':
Repeat_beta2t = tf.tile(beta, [batchsize2integral, 1])
tInte_beta = tf.concat([t_inte, Repeat_beta2t], axis=-1)
Repeat_beta2y = tf.tile(beta, [batchsize2b, 1])
y_beta = tf.concat([y, Repeat_beta2y], axis=-1)
Repeat_beta2Y = tf.tile(beta, [batchsize2bS, 1])
Y_beta = tf.concat([Y, Repeat_beta2Y], axis=-1)
else:
tInte_beta = t_inte
y_beta = y
Y_beta = Y
if R['sub_networks'] == 'subDNNs':
if R['name2network'] == str('DNN'):
b0_NN2t = DNN_base.PDE_DNN(tInte_beta,
W2b0,
B2b0,
hidden_layers,
activate_name=act_func)
b1_NN2t = DNN_base.PDE_DNN(tInte_beta,
W2b1,
B2b1,
hidden_layers,
activate_name=act_func)
b0_NN2y = DNN_base.PDE_DNN(y_beta,
W2b0,
B2b0,
hidden_layers,
activate_name=act_func)
b1_NN2y = DNN_base.PDE_DNN(y_beta,
W2b1,
B2b1,
hidden_layers,
activate_name=act_func)
b0_NN2Y = DNN_base.PDE_DNN(Y_beta,
W2b0,
B2b0,
hidden_layers,
activate_name=act_func)
b1_NN2Y = DNN_base.PDE_DNN(Y_beta,
W2b1,
B2b1,
hidden_layers,
activate_name=act_func)
elif R['name2network'] == 'DNN_Cos_C_Sin_Base':
freq = R['freqs']
b0_NN2t = DNN_base.PDE_DNN_Cos_C_Sin_Base(
tInte_beta,
W2b0,
B2b0,
hidden_layers,
freq,
activate_name=act_func)
b1_NN2t = DNN_base.PDE_DNN_Cos_C_Sin_Base(
tInte_beta,
W2b1,
B2b1,
hidden_layers,
freq,
activate_name=act_func)
b0_NN2y = DNN_base.PDE_DNN_Cos_C_Sin_Base(
y_beta,
W2b0,
B2b0,
hidden_layers,
freq,
activate_name=act_func)
b1_NN2y = DNN_base.PDE_DNN_Cos_C_Sin_Base(
y_beta,
W2b1,
B2b1,
hidden_layers,
freq,
activate_name=act_func)
b0_NN2Y = DNN_base.PDE_DNN_Cos_C_Sin_Base(
Y_beta,
W2b0,
B2b0,
hidden_layers,
freq,
activate_name=act_func)
b1_NN2Y = DNN_base.PDE_DNN_Cos_C_Sin_Base(
Y_beta,
W2b1,
B2b1,
hidden_layers,
freq,
activate_name=act_func)
b_NN2t = tf.concat([b0_NN2t, b1_NN2t], axis=-1)
b_NN2y = tf.concat([b0_NN2y, b1_NN2y], axis=-1)
b_NN2Y = tf.concat([b0_NN2Y, b1_NN2Y], axis=-1)
else:
if R['name2network'] == str('DNN'):
b_NN2t = DNN_base.PDE_DNN(tInte_beta,
W2b,
B2b,
hidden_layers,
activate_name=act_func)
b_NN2y = DNN_base.PDE_DNN(y_beta,
W2b,
B2b,
hidden_layers,
activate_name=act_func)
b_NN2Y = DNN_base.PDE_DNN(Y_beta,
W2b,
B2b,
hidden_layers,
activate_name=act_func)
elif R['name2network'] == 'DNN_Cos_C_Sin_Base':
freq = R['freqs']
b_NN2t = DNN_base.PDE_DNN_Cos_C_Sin_Base(
tInte_beta,
W2b,
B2b,
hidden_layers,
freq,
activate_name=act_func)
b_NN2y = DNN_base.PDE_DNN_Cos_C_Sin_Base(
y_beta,
W2b,
B2b,
hidden_layers,
freq,
activate_name=act_func)
b_NN2Y = DNN_base.PDE_DNN_Cos_C_Sin_Base(
Y_beta,
W2b,
B2b,
hidden_layers,
freq,
activate_name=act_func)
# 如下代码说明中 K 代表y的数目， N 代表 X 和 Y 的数目，M 代表 t 的数目
# 求 b 的代码，先处理等号左边，再处理等号右边
OneX = tf.concat([tfOne, X], axis=-1) # N 行 (1+dim) 列，N代表X数据数目
betaT = tf.transpose(beta, perm=[1, 0]) # (1+dim) 行 1 列
oneX_betaT = tf.matmul(
OneX, betaT) # N 行 1 列。每个 Xi 都会对应 beta。 beta 是 (1+dim) 行 1 列的
tInte_beraXT = tf.transpose(t_inte, perm=[
1, 0
]) - oneX_betaT # N 行 M 列，t_inte 是 M 行 dim 列的
# b方程左边系数分母，关于t求和平均代替积分
fYX_t = fYX(z=tInte_beraXT,
func_name='gaussian') # fY|X在t处的取值 # N 行 M 列
phi_1 = tf.transpose(1 - pi_star(t=t_inte),
perm=[1, 0]) # M 行 dim 列转置为 dim 行 M 列
fYXt_1phi = tf.multiply(fYX_t, phi_1) # N 行 M 列
fYXt_1phi_intergal = tf.reduce_mean(fYXt_1phi, axis=-1)
fYXt_1phi_intergal = tf.reshape(fYXt_1phi_intergal,
shape=[-1, 1]) # N 行 dim 列
# b方程左边系数分子，关于t求和平均代替积分
dbeta2fYX_t = fYX_t * tInte_beraXT # N 行 M 列
expand_dbeta2fYX_t = tf.expand_dims(dbeta2fYX_t,
axis=1) # N 页 1 行 M 列
tilde_Edbeta2fYX_t = tf.tile(
expand_dbeta2fYX_t,
[1, data_indim + 1, 1]) # N 页 (1+dim) 行 M 列
dfYX_beta_t = tf.multiply(tilde_Edbeta2fYX_t,
tf.expand_dims(
OneX, axis=-1)) # N 页 (1+dim) 行 M 列
phi_t = tf.transpose(pi_star(t=t_inte),
perm=[1, 0]) # M 行 1 列转置为 1 行 M 列
dfYX_beta_phi_t = tf.multiply(dfYX_beta_t,
phi_t) # N 页 (1+dim) 行 M 列
dfYX_beta_integral = tf.reduce_mean(dfYX_beta_phi_t,
axis=-1) # N 行 (1+dim) 列
# b方程左边的系数
ceof2left = dfYX_beta_integral / fYXt_1phi_intergal # N 行 (1+dim) 列
# b方程左边积分系数的匹配项
yaux_beraXT = tf.transpose(
y, perm=[1, 0]) - oneX_betaT # N 行 K 列， y 是 K 行 data_dim 列
fYX_y = fYX(z=yaux_beraXT,
func_name='gaussian') # fY|X在y处的取值, N 行 K 列
expand_fYX_y = tf.expand_dims(fYX_y, axis=1) # N 页 1 行 K 列
bleft_fYX_y = tf.multiply(tf.expand_dims(ceof2left, axis=-1),
expand_fYX_y) # N 页 (1+dim) 行 K 列
# b方程左边的第一个关于beta的导数项， 即dfYX2beta(y,Xi,beta)
dbeta2fYX_y = fYX_y * yaux_beraXT # N 行 K 列
expand_dbeta2fYX_y = tf.expand_dims(dbeta2fYX_y,
axis=1) # N 页 1 行 K 列
dfYX_beta_y = tf.multiply(expand_dbeta2fYX_y,
tf.expand_dims(
OneX, axis=-1)) # N 页 (1+dim) 行 K 列
# b方程左边的组合结果
sum2bleft = bleft_fYX_y + dfYX_beta_y # N 页 (1+dim) 行 K 列
bleft = tf.reduce_mean(sum2bleft, axis=0) # 1+dim 行 K 列
# b 方程的右边。右边的第一项，如下两行计算
trans_b_NN2y = tf.transpose(
b_NN2y, perm=[1, 0]) # K 行 2 列转置为 2 行 K 列,然后扩为 1 X 2 X K 的
by_fYX_y = tf.multiply(tf.expand_dims(trans_b_NN2y, axis=0),
tf.expand_dims(fYX_y,
axis=1)) # N 页 1+dim 行 K 列
# b方程右边的系数的分子
expand_fYX_t = tf.tile(
tf.expand_dims(fYX_t, axis=1),
[1, 1 + data_indim, 1]) # N 页 (1+data_indim) 行 M 列
bt_fYXt = tf.multiply(tf.transpose(b_NN2t, perm=[1, 0]),
expand_fYX_t) # N 页 (1+data_indim) 行 M 列
bt_fYXt_phi = tf.multiply(bt_fYXt,
phi_t) # N 页 (1+data_indim) 行 M 列
bt_fYXt_phi_integral = tf.reduce_mean(
bt_fYXt_phi, axis=-1) # N 行 (1+data_indim) 列
# b方程右边的系数。分母为共用b方程左边的分母
ceof2fight = bt_fYXt_phi_integral / fYXt_1phi_intergal # N 行 (1+data_indim) 列
# b方程系数与匹配项的线性积
expand_fYX_y_right = tf.expand_dims(fYX_y, axis=1) # N 页 1 行 K 列
bright_fYX_y = tf.multiply(
tf.expand_dims(ceof2fight, axis=-1),
expand_fYX_y_right) # N 页 (1+data_indim) 行 K 列
# b方程右边的组合结果
sum2right = by_fYX_y + bright_fYX_y # N 页 (1+dim) 行 K 列
bright = tf.reduce_mean(sum2right, axis=0) # 1+dim 行 K 列
# b方程左边和右边要相等，使用差的平方来估计
loss2b = tf.reduce_mean(
tf.reduce_mean(tf.square(bleft - bright), axis=-1))
# 求 Seff 的代码
Y_beraXT = Y - oneX_betaT # N 行 dim 列， Y 和 oneX_betaT 都是 N 行 dim 列的
fYX_Y = fYX(z=Y_beraXT,
func_name='gaussian') # fY|X在t处的取值 # N 行 dim 列
ceof2R = (R2XY / fYX_Y) # N 行 dim 列， R2XY 是 N 行 dim 列的
dbeta2fYX_Y = tf.multiply(fYX_Y, Y_beraXT) # N 行 dim 列
dfYX_beta_Y = tf.multiply(
dbeta2fYX_Y,
OneX) # fY|X(t)对beta的导数，N行(1+dim)列，OneX 是 N 行 (1+dim) 列
Sbeta_star1 = tf.multiply(ceof2R, dfYX_beta_Y) # N 行 (1+dim)
Seff_temp = tf.reshape(tf.reduce_mean(fYXt_1phi_intergal, axis=-1),
shape=[-1, 1]) # N 行 dim 列
ceof21R = (1 - R2XY) / Seff_temp # N 行 dim 列
Sbeta_star2 = tf.multiply(ceof21R,
dfYX_beta_integral) # N 行 (1+dim)
Sbeta_star = Sbeta_star1 - Sbeta_star2 # N 行 (1+dim)
Seff2 = tf.multiply(R2XY, b_NN2Y) # N 行 (1+dim)
Seff3 = tf.multiply(1 - R2XY, bt_fYXt_phi_integral /
fYXt_1phi_intergal) # N 行 (1+dim)
squareSeff = tf.square(Sbeta_star - Seff2 + Seff3) # N 行 (1+dim)
loss2Seff = tf.reduce_mean(tf.reduce_mean(squareSeff, axis=0))
if R['sub_networks'] == 'subDNNs':
if R['regular_weight_model'] == 'L1':
regular_WB2b0 = DNN_base.regular_weights_biases_L1(
W2b0, B2b0)
regular_WB2b1 = DNN_base.regular_weights_biases_L1(
W2b1, B2b1)
elif R['regular_weight_model'] == 'L2':
regular_WB2b0 = DNN_base.regular_weights_biases_L2(
W2b0, B2b0)
regular_WB2b1 = DNN_base.regular_weights_biases_L2(
W2b1, B2b1)
else:
regular_WB2b0 = tf.constant(0.0)
regular_WB2b1 = tf.constant(0.0)
regular_WB2b = regular_WB2b0 + regular_WB2b1
else:
if R['regular_weight_model'] == 'L1':
regular_WB2b = DNN_base.regular_weights_biases_L1(W2b, B2b)
elif R['regular_weight_model'] == 'L2':
regular_WB2b = DNN_base.regular_weights_biases_L2(W2b, B2b)
else:
regular_WB2b = tf.constant(0.0)
penalty_WB = wb_regular * regular_WB2b
if R['training_strategy'] == 'Alter_train':
lossB = loss2b + penalty_WB
lossSeff = loss2Seff + penalty_WB
if R['sub_networks'] == 'subDNNs':
WB = [W2b0, B2b0, W2b1, B2b1]
else:
WB = [W2b, B2b]
my_optimizer2b = tf.train.AdamOptimizer(inline_lr2b)
my_optimizer2Seff = tf.train.AdamOptimizer(inline_lr2S)
train_loss2b = my_optimizer2b.minimize(
lossB, global_step=global_steps, var_list=WB)
train_loss2Seff = my_optimizer2Seff.minimize(
lossSeff, global_step=global_steps, var_list=beta)
else:
loss = 5 * loss2b + 10 * loss2Seff + penalty_WB # 要优化的loss function
my_optimizer = tf.train.AdamOptimizer(inline_lr)
if R['train_group'] == 0:
train_my_loss = my_optimizer.minimize(
loss, global_step=global_steps)
if R['train_group'] == 1:
if R['sub_networks'] == 'subDNNs':
WB = [W2b0, B2b0, W2b1, B2b1]
else:
WB = [W2b, B2b]
train_op1 = my_optimizer.minimize(loss2b,
global_step=global_steps,
var_list=WB)
train_op2 = my_optimizer.minimize(loss2Seff,
global_step=global_steps,
var_list=beta)
train_op3 = my_optimizer.minimize(loss,
global_step=global_steps)
train_my_loss = tf.group(train_op1, train_op2, train_op3)
elif R['train_group'] == 2:
if R['sub_networks'] == 'subDNNs':
WB = [W2b0, B2b0, W2b1, B2b1]
else:
WB = [W2b, B2b]
train_op1 = my_optimizer.minimize(loss2b,
global_step=global_steps,
var_list=WB)
train_op2 = my_optimizer.minimize(loss2Seff,
global_step=global_steps,
var_list=beta)
train_my_loss = tf.group(train_op1, train_op2)
t0 = time.time()
loss_b_all, loss_seff_all, loss_all = [], [], [] # 空列表, 使用 append() 添加元素
# 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
X_batch = DNN_data.randnormal_mu_sigma(size=batchsize2bS,
mu=0.5,
sigma=0.5)
Y_init = 0.25 - 0.5 * X_batch + np.reshape(
np.random.randn(batchsize2bS, 1), (-1, 1))
# y_batch = DNN_data.randnormal_mu_sigma(size=batchsize2aux, mu=0.0, sigma=1.5)
# x_aux_batch = DNN_data.randnormal_mu_sigma(size=batchsize2aux, mu=0.5, sigma=0.5)
# y_batch = 0.25 - 0.5 * x_aux_batch + np.reshape(np.random.randn(batchsize2aux, 1), (-1, 1))
relate2XY = np.reshape(np.random.randint(0, 2, batchsize2bS), (-1, 1))
Y_batch = np.multiply(Y_init, relate2XY)
one2train = np.ones((batchsize2bS, 1))
y_batch = DNN_data.rand_it(batch_size=batchsize2b,
variable_dim=data_indim,
region_a=-2,
region_b=2)
t_batch = DNN_data.rand_it(batch_size=batchsize2integral,
variable_dim=data_indim,
region_a=-2,
region_b=2)
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
if R['training_strategy'] == 'Alter_train':
tmp_lr2b = init_lr2b
tmp_lr2S = init_lr2S
for i_epoch in range(R['max_epoch'] + 1):
if i_epoch % 10 == 0 and i_epoch != 0: # 10, 20, 30, 40,.....
tmp_lr2S = tmp_lr2S * (1 - lr_decay2S)
_S, loss2seff_tmp, loss2b_tmp, p_WB, beta_temp = sess.run(
[train_loss2Seff, loss2Seff, loss2b, penalty_WB, beta],
feed_dict={
X: X_batch,
Y: Y_batch,
R2XY: relate2XY,
y: y_batch,
tfOne: one2train,
t_inte: t_batch,
inline_lr2S: tmp_lr2S
})
else: # 0,1,2,3,4,5,6,7,8,9, 11,12,13,....,19, 21,22,....
tmp_lr2b = tmp_lr2b * (1 - lr_decay2b)
_b, loss2b_tmp, loss2seff_tmp, p_WB, beta_temp = sess.run(
[train_loss2b, loss2b, loss2Seff, penalty_WB, beta],
feed_dict={
X: X_batch,
Y: Y_batch,
R2XY: relate2XY,
y: y_batch,
tfOne: one2train,
t_inte: t_batch,
inline_lr2b: tmp_lr2b
})
loss_seff_all.append(loss2seff_tmp)
loss_b_all.append(loss2b_tmp)
if (i_epoch % 10) == 0:
DNN_tools.log_string(
'*************** epoch: %d*10 ****************' %
int(i_epoch / 10), log_fileout)
DNN_tools.log_string(
'lossb for training: %.10f\n' % loss2b_tmp,
log_fileout)
DNN_tools.log_string(
'lossS for training: %.10f\n' % loss2seff_tmp,
log_fileout)
DNN_tools.log_string(
'Penalty2Weights_Bias for training: %.10f\n' % p_WB,
log_fileout)
if (i_epoch % 100) == 0:
print(
'**************** epoch: %d*100 *******************' %
int(i_epoch / 100))
print('beta:[%f %f]' % (beta_temp[0, 0], beta_temp[0, 1]))
print('\n')
DNN_tools.log_string(
'*************** epoch: %d*100 *****************' %
int(i_epoch / 100), para_outFile)
DNN_tools.log_string(
'beta:[%f, %f]' % (beta_temp[0, 0], beta_temp[0, 1]),
para_outFile)
DNN_tools.log_string('\n', para_outFile)
saveData.save_2trainLosses2mat(loss_b_all,
loss_seff_all,
data1Name='lossb',
data2Name='lossSeff',
actName=act_func,
outPath=R['FolderName'])
plotData.plotTrain_loss_1act_func(loss_b_all,
lossType='loss_b',
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
plotData.plotTrain_loss_1act_func(loss_seff_all,
lossType='loss_s',
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
else:
tmp_lr = init_lr
for i_epoch in range(R['max_epoch'] + 1):
tmp_lr = tmp_lr * (1 - lr_decay)
_, loss2b_tmp, loss2seff_tmp, loss_tmp, p_WB, beta_temp = sess.run(
[train_my_loss, loss2b, loss2Seff, loss, penalty_WB, beta],
feed_dict={
X: X_batch,
Y: Y_batch,
R2XY: relate2XY,
y: y_batch,
tfOne: one2train,
t_inte: t_batch,
inline_lr: tmp_lr
})
loss_b_all.append(loss2b_tmp)
loss_seff_all.append(loss2seff_tmp)
loss_all.append(loss_tmp)
if (i_epoch % 10) == 0:
DNN_tools.log_string(
'*************** epoch: %d*10 ****************' %
int(i_epoch / 10), log_fileout)
DNN_tools.log_string(
'lossb for training: %.10f\n' % loss2b_tmp,
log_fileout)
DNN_tools.log_string(
'lossS for training: %.10f\n' % loss2seff_tmp,
log_fileout)
DNN_tools.log_string(
'loss for training: %.10f\n' % loss_tmp, log_fileout)
DNN_tools.log_string(
'Penalty2Weights_Bias for training: %.10f\n' % p_WB,
log_fileout)
if (i_epoch % 100) == 0:
print(
'**************** epoch: %d*100 *******************' %
int(i_epoch / 100))
print('beta:[%f %f]' % (beta_temp[0, 0], beta_temp[0, 1]))
print('\n')
DNN_tools.log_string(
'*************** epoch: %d*100 *****************' %
int(i_epoch / 100), para_outFile)
DNN_tools.log_string(
'beta:[%f, %f]' % (beta_temp[0, 0], beta_temp[0, 1]),
para_outFile)
DNN_tools.log_string('\n', para_outFile)
saveData.save_3trainLosses2mat(loss_b_all,
loss_seff_all,
loss_all,
data1Name='lossb',
data2Name='lossSeff',
data3Name='lossAll',
actName=act_func,
outPath=R['FolderName'])
plotData.plotTrain_loss_1act_func(loss_b_all,
lossType='loss_b',
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
plotData.plotTrain_loss_1act_func(loss_seff_all,
lossType='loss_s',
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
plotData.plotTrain_loss_1act_func(loss_all,
lossType='loss_all',
seedNo=R['seed'],
outPath=R['FolderName'],
yaxis_scale=True)
def print_and_log2train(i_epoch,
run_time,
tmp_lr,
temp_penalty_nt,
penalty_wb2s,
penalty_wb2i,
penalty_wb2r,
loss_s,
loss_i,
loss_r,
loss_n,
loss,
log_out=None):
print('train epoch: %d, time: %.3f' % (i_epoch, run_time))
print('learning rate: %f' % tmp_lr)
print('penalty for difference of predict and true : %f' % temp_penalty_nt)
print('penalty weights and biases for S: %f' % penalty_wb2s)
print('penalty weights and biases for I: %f' % penalty_wb2i)
print('penalty weights and biases for R: %f' % penalty_wb2r)
print('loss for S: %.16f' % loss_s)
print('loss for I: %.16f' % loss_i)
print('loss for R: %.16f' % loss_r)
print('loss for N: %.16f\n' % loss_n)
print('total loss: %.16f\n' % loss)
DNN_tools.log_string('train epoch: %d,time: %.3f' % (i_epoch, run_time),
log_out)
DNN_tools.log_string('learning rate: %f' % tmp_lr, log_out)
DNN_tools.log_string(
'penalty for difference of predict and true : %f' % temp_penalty_nt,
log_out)
DNN_tools.log_string('penalty weights and biases for S: %f' % penalty_wb2s,
log_out)
DNN_tools.log_string('penalty weights and biases for I: %f' % penalty_wb2i,
log_out)
DNN_tools.log_string(
'penalty weights and biases for R: %.10f' % penalty_wb2r, log_out)
DNN_tools.log_string('loss for S: %.16f' % loss_s, log_out)
DNN_tools.log_string('loss for I: %.16f' % loss_i, log_out)
DNN_tools.log_string('loss for R: %.16f' % loss_r, log_out)
DNN_tools.log_string('loss for N: %.16f' % loss_n, log_out)
DNN_tools.log_string('total loss: %.16f \n\n' % loss, log_out)
def dictionary_out2file(R_dic, log_fileout):
DNN_tools.log_string(
'Equation name for problem: %s\n' % (R_dic['eqs_name']), log_fileout)
DNN_tools.log_string(
'Network model for SIR: %s\n' % str(R_dic['model2sir']), log_fileout)
DNN_tools.log_string(
'Network model for parameters: %s\n' % str(R_dic['model2paras']),
log_fileout)
DNN_tools.log_string(
'activate function for SIR : %s\n' % str(R_dic['act2sir']),
log_fileout)
DNN_tools.log_string(
'activate function for parameters : %s\n' % str(R_dic['act2paras']),
log_fileout)
DNN_tools.log_string(
'hidden layers for SIR: %s\n' % str(R_dic['hidden2SIR']), log_fileout)
DNN_tools.log_string(
'hidden layers for parameters: %s\n' % str(R_dic['hidden2para']),
log_fileout)
DNN_tools.log_string(
'Init learning rate: %s\n' % str(R_dic['learning_rate']), log_fileout)
DNN_tools.log_string(
'Decay to learning rate: %s\n' % str(R_dic['lr_decay']), log_fileout)
DNN_tools.log_string(
'The type for Loss function: %s\n' % str(R_dic['loss_function']),
log_fileout)
if (R_dic['optimizer_name']).title() == 'Adam':
DNN_tools.log_string('optimizer:%s\n' % str(R_dic['optimizer_name']),
log_fileout)
else:
DNN_tools.log_string(
'optimizer:%s with momentum=%f\n' %
(R_dic['optimizer_name'], R_dic['momentum']), log_fileout)
if R_dic['activate_stop'] != 0:
DNN_tools.log_string(
'activate the stop_step and given_step= %s\n' %
str(R_dic['max_epoch']), log_fileout)
else:
DNN_tools.log_string(
'no activate the stop_step and given_step = default: %s\n' %
str(R_dic['max_epoch']), log_fileout)
DNN_tools.log_string(
'Initial penalty for difference of predict and true: %s\n' %
str(R_dic['init_penalty2predict_true']), log_fileout)
DNN_tools.log_string(
'The model of regular weights and biases: %s\n' %
str(R_dic['regular_weight_model']), log_fileout)
DNN_tools.log_string(
'Regularization parameter for weights and biases: %s\n' %
str(R_dic['regular_weight']), log_fileout)
DNN_tools.log_string(
'Size 2 training set: %s\n' % str(R_dic['size2train']), log_fileout)
DNN_tools.log_string(
'Batch-size 2 training: %s\n' % str(R_dic['batch_size2train']),
log_fileout)
DNN_tools.log_string(
'Batch-size 2 testing: %s\n' % str(R_dic['batch_size2test']),
log_fileout)
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'])
def solve_Integral_Equa(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
outFile2para_name = '%s_%s.txt' % ('para2', 'beta')
logfile_name = '%s%s.txt' % ('log2', R['activate_func'])
log_fileout = open(os.path.join(log_out_path, logfile_name), 'w') # 在这个路径下创建并打开一个可写的 log_train.txt文件
para_outFile = open(os.path.join(log_out_path, outFile2para_name), 'w') # 在这个路径下创建并打开一个可写的 para_outFile.txt文件
dictionary_out2file(R, log_fileout)
# 积分方程问题需要的设置
batchsize = R['batch_size2integral']
batchsize2aux = R['batch_size2auxiliary']
wb_regular = R['regular_weight_biases']
lr_decay = R['learning_rate_decay']
learning_rate = R['learning_rate']
hidden_layers = R['hidden_layers']
act_func = R['activate_func']
# ------- set the problem ---------
data_indim = R['input_dim']
data_outdim = R['output_dim']
para_dim = R['estimate_para_dim']
# 初始化权重和和偏置的模式
flag2bnn = 'bnn'
input_dim = 1
if R['model'] == 'PDE_DNN_Cos_C_Sin_Base':
W2b, B2b = DNN_base.initialize_NN_random_normal2_CS(input_dim, para_dim, hidden_layers, flag2bnn)
else:
W2b, B2b = DNN_base.initialize_NN_random_normal2(input_dim, para_dim, hidden_layers, flag2bnn)
global_steps = tf.Variable(0, trainable=False)
with tf.device('/gpu:%s' % (R['gpuNo'])):
with tf.variable_scope('vscope', reuse=tf.AUTO_REUSE):
X = tf.placeholder(tf.float32, name='X_recv', shape=[batchsize, data_indim])
Y = tf.placeholder(tf.float32, name='Y_recv', shape=[batchsize, data_indim])
R2XY = tf.placeholder(tf.float32, name='R2XY_recv', shape=[batchsize, data_indim])
y_aux = tf.placeholder(tf.float32, name='y_auxiliary', shape=[batchsize2aux, 1])
beta = tf.Variable(tf.random.uniform([1, para_dim]), dtype='float32', name='beta')
# beta = tf.Variable([[0.25, -0.5]], dtype='float32', name='beta')
# beta = tf.constant([[0.25, -0.5]], dtype=tf.float32, name='beta')
tfOne = tf.placeholder(tf.float32, shape=[1, 1], name='tfOne')
inline_lr = tf.placeholder_with_default(input=1e-5, shape=[], name='inline_learning_rate')
# 供选择的网络模式
if R['model'] == str('DNN'):
b_NN2y = DNN_base.PDE_DNN(y_aux, W2b, B2b, hidden_layers, activate_name=act_func)
# b_NN2Y = DNN_base.PDE_DNN(Y, W2b, B2b, hidden_layers, activate_name=act_func)
elif R['model'] == 'DNN_scale':
freq = R['freqs']
b_NN2y = DNN_base.PDE_DNN_scale(y_aux, W2b, B2b, hidden_layers, freq, activate_name=act_func)
# b_NN2Y = DNN_base.PDE_DNN_scale(Y, W2b, B2b, hidden_layers, freq, activate_name=act_func)
elif R['model'] == 'DNN_adapt_scale':
freq = R['freqs']
b_NN2y = DNN_base.PDE_DNN_adapt_scale(y_aux, W2b, B2b, hidden_layers, freq, activate_name=act_func)
# b_NN2Y = DNN_base.PDE_DNN_adapt_scale(Y, W2b, B2b, hidden_layers, freq, activate_name=act_func)
elif R['model'] == 'DNN_FourierBase':
freq = R['freqs']
b_NN2y = DNN_base.PDE_DNN_FourierBase(y_aux, W2b, B2b, hidden_layers, freq, activate_name=act_func)
# b_NN2Y = DNN_base.PDE_DNN_FourierBase(Y, W2b, B2b, hidden_layers, freq, activate_name=act_func)
elif R['model'] == 'DNN_Cos_C_Sin_Base':
freq = R['freqs']
b_NN2y = DNN_base.PDE_DNN_Cos_C_Sin_Base(y_aux, W2b, B2b, hidden_layers, freq, activate_name=act_func)
# b_NN2Y = DNN_base.PDE_DNN_Cos_C_Sin_Base(Y, W2b, B2b, hidden_layers, freq, activate_name=act_func)
# 下面怎么把循环改为向量操作呢？
sum2bleft = tf.zeros(shape=[1, 1], dtype=tf.float32, name='01')
sum2bright = tf.zeros(shape=[1, 1], dtype=tf.float32, name='02')
# 使用循环将Xi取出来,然后带入方程计算b(·:beta),需要对i累加，最后取均值
for i in range(batchsize):
Xtemp = tf.reshape(X[i], shape=[1, 1]) # Xi取出
OneX = tf.concat([tfOne, Xtemp], axis=-1) # 1 行 (1+dim) 列
XiTrans = tf.transpose(OneX, [1, 0]) # (1,Xi)
fYX_y = my_normal(t=y_aux-tf.matmul(beta, XiTrans)) # fY|X在y处的取值，fY|X(y)
dfYX_beta = tf.matmul(my_normal(t=y_aux-tf.matmul(beta, XiTrans))*(y_aux-tf.matmul(beta, XiTrans)), OneX) # fY|X(y)对beta的导数
# beta 是 1 行 para_dim 列
fyx_1minus_phi_integral = tf.reduce_mean(fYX_y * (1 - pi_star(t=y_aux)), axis=0) # fY|X(t)*(1-pi(t))的积分
dfyx_phi_integral = tf.reduce_mean(dfYX_beta * pi_star(t=y_aux), axis=0) # diff_fY|X(y)*pi(t)的积分
ceof_vec2left = dfyx_phi_integral/fyx_1minus_phi_integral
sum2bleft = sum2bleft + dfYX_beta + ceof_vec2left*fYX_y
b_fyx_phi_integral = tf.reduce_mean(b_NN2y*fYX_y*pi_star(t=y_aux), axis=0) # b(t, beta)*fY|X(t)*pi(t)的积分
ceof_vec2right = b_fyx_phi_integral / fyx_1minus_phi_integral
sum2bright = sum2bright + b_NN2y * fYX_y + ceof_vec2right * fYX_y
bleft = sum2bleft / batchsize # (1/N)sum{i=1:i=N(·)}
bright = sum2bright / batchsize # (1/N)sum{i=1:i=N(·)}
loss2b = tf.reduce_mean(tf.reduce_mean(tf.square(bleft - bright), axis=0))
sum2Seff = tf.zeros(shape=[1, 1], dtype=tf.float32, name='03')
for i in range(batchsize):
Xtemp = tf.reshape(X[i], shape=[1, 1])
OneX = tf.concat([tfOne, Xtemp], axis=-1) # 1 行 (1+dim) 列
XiTrans = tf.transpose(OneX, [1, 0])
fYX2y = my_normal(t=y_aux - tf.matmul(beta, XiTrans)) # fY|X在y处的取值，fY|X(y)
Yi = tf.reshape(Y[i], shape=[1, 1]) # Yi
fYX2Y = my_normal(t=Yi-tf.matmul(beta, XiTrans)) # fY|X在Yi处的取值，fY|X(Yi)
dfYX_beta2Y = tf.matmul(my_normal(t=Yi-tf.matmul(beta, XiTrans))*(Yi-tf.matmul(beta, XiTrans)), OneX) # diff_fY|X(Yi)
dfYX_beta2y = tf.matmul(my_normal(t=y_aux - tf.matmul(beta, XiTrans)) * (y_aux - tf.matmul(beta, XiTrans)), OneX) # diff_fY|X(t)
fyx_1minus_phi_integral = tf.reduce_mean(fYX2y * (1 - pi_star(t=y_aux)), axis=0) # fY|X(t)*(1-pi(t))的积分
dfyx_phi_integral = tf.reduce_mean(dfYX_beta2y * pi_star(t=y_aux), axis=0) # diff_fY|X(y)*pi(t)的积分
fyx_b_phi_integral = tf.reduce_mean(fYX2y * b_NN2y * pi_star(t=y_aux), axis=0) # fY|X(t)*b(t, beta)*pi(t)的积分
R2XY_i = tf.reshape(R2XY[i], shape=[1, -1]) # Ri
Seff1 = (R2XY_i/fYX2Y) * dfYX_beta2Y - ((1-R2XY_i)/fyx_1minus_phi_integral) * dfyx_phi_integral # S^*_beta
if R['model'] == 'DNN':
b_NN2Yi = DNN_base.PDE_DNN(Yi, W2b, B2b, hidden_layers, activate_name=act_func)
elif R['model'] == 'DNN_scale':
freq = R['freqs']
b_NN2Yi = DNN_base.PDE_DNN_scale(Yi, W2b, B2b, hidden_layers, freq, activate_name=act_func)
elif R['model'] == 'DNN_adapt_scale':
freq = R['freqs']
b_NN2Yi = DNN_base.PDE_DNN_adapt_scale(Yi, W2b, B2b, hidden_layers, freq, activate_name=act_func)
elif R['model'] == 'DNN_FourierBase':
freq = R['freqs']
b_NN2Yi = DNN_base.PDE_DNN_FourierBase(Yi, W2b, B2b, hidden_layers, freq, activate_name=act_func)
elif R['model'] == 'DNN_Cos_C_Sin_Base':
freq = R['freqs']
b_NN2Yi = DNN_base.PDE_DNN_Cos_C_Sin_Base(Yi, W2b, B2b, hidden_layers, freq, activate_name=act_func)
Seff2 = R2XY_i * tf.reshape(b_NN2Yi, shape=[1, -1])
Seff3 = (1-R2XY_i) * (fyx_b_phi_integral/fyx_1minus_phi_integral)
Seff = Seff1 - Seff2 + Seff3
sum2Seff = sum2Seff + Seff
loss2s_temp = sum2Seff/batchsize
loss2Seff = tf.reduce_mean(tf.square(loss2s_temp))
if R['regular_weight_model'] == 'L1':
regular_WB2b = DNN_base.regular_weights_biases_L1(W2b, B2b)
elif R['regular_weight_model'] == 'L2':
regular_WB2b = DNN_base.regular_weights_biases_L2(W2b, B2b)
else:
regular_WB2b = tf.constant(0.0)
penalty_WB = wb_regular * regular_WB2b
loss = loss2b + loss2Seff + penalty_WB # 要优化的loss function
my_optimizer = tf.train.AdamOptimizer(inline_lr)
if R['train_group'] == 0:
train_my_loss = my_optimizer.minimize(loss, global_step=global_steps)
if R['train_group'] == 1:
train_op1 = my_optimizer.minimize(loss2b, global_step=global_steps)
train_op2 = my_optimizer.minimize(loss2Seff, global_step=global_steps)
train_op3 = my_optimizer.minimize(loss, global_step=global_steps)
train_my_loss = tf.group(train_op1, train_op2, train_op3)
elif R['train_group'] == 2:
train_op1 = my_optimizer.minimize(loss2b, global_step=global_steps)
train_op2 = my_optimizer.minimize(loss2Seff, global_step=global_steps)
train_my_loss = tf.group(train_op1, train_op2)
t0 = time.time()
loss_b_all, loss_seff_all, loss_all = [], [], [] # 空列表, 使用 append() 添加元素
# 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
x_batch = DNN_data.randnormal_mu_sigma(size=batchsize, mu=0.5, sigma=0.5)
y_init = 0.25 - 0.5 * x_batch + np.reshape(np.random.randn(batchsize, 1), (-1, 1))
y_aux_batch = DNN_data.rand_it(batch_size=batchsize2aux, variable_dim=data_indim, region_a=-2, region_b=2)
# y_aux_batch = DNN_data.randnormal_mu_sigma(size=batchsize2aux, mu=0.0, sigma=1.5)
# x_aux_batch = DNN_data.randnormal_mu_sigma(size=batchsize2aux, mu=0.5, sigma=0.5)
# y_aux_batch = 0.25 - 0.5 * x_aux_batch + np.reshape(np.random.randn(batchsize2aux, 1), (-1, 1))
relate2XY = np.reshape(np.random.randint(0, 2, batchsize), (-1, 1))
y_batch = np.multiply(y_init, relate2XY)
one2train = np.ones((1, 1))
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):
tmp_lr = tmp_lr * (1 - lr_decay)
_, loss2b_tmp, loss2seff_tmp, loss_tmp, p_WB, beta_temp = sess.run(
[train_my_loss, loss2b, loss2Seff, loss, penalty_WB, beta],
feed_dict={X: x_batch, Y: y_batch, R2XY: relate2XY, y_aux: y_aux_batch, tfOne: one2train,
inline_lr: tmp_lr})
loss_b_all.append(loss2b_tmp)
loss_seff_all.append(loss2seff_tmp)
loss_all.append(loss_tmp)
if (i_epoch % 10) == 0:
DNN_tools.log_string('*************** epoch: %d*10 ****************' % int(i_epoch / 10), log_fileout)
DNN_tools.log_string('lossb for training: %.10f\n' % loss2b_tmp, log_fileout)
DNN_tools.log_string('lossS for training: %.10f\n' % loss2seff_tmp, log_fileout)
DNN_tools.log_string('loss for training: %.10f\n' % loss_tmp, log_fileout)
if (i_epoch % 100) == 0:
print('**************** epoch: %d*100 *******************'% int(i_epoch/100))
print('beta:[%f %f]' % (beta_temp[0, 0], beta_temp[0, 1]))
print('\n')
DNN_tools.log_string('*************** epoch: %d*100 *****************' % int(i_epoch/100), para_outFile)
DNN_tools.log_string('beta:[%f, %f]' % (beta_temp[0, 0], beta_temp[0, 1]), para_outFile)
DNN_tools.log_string('\n', para_outFile)
saveData.save_trainLoss2mat_1actFunc(loss_b_all, loss_seff_all, loss_all, actName=act_func,
outPath=R['FolderName'])
plotData.plotTrain_loss_1act_func(loss_b_all, lossType='loss_b', seedNo=R['seed'], outPath=R['FolderName'],
yaxis_scale=True)
plotData.plotTrain_loss_1act_func(loss_seff_all, lossType='loss_s', seedNo=R['seed'], outPath=R['FolderName'],
yaxis_scale=True)
plotData.plotTrain_loss_1act_func(loss_all, lossType='loss_all', seedNo=R['seed'], outPath=R['FolderName'],
yaxis_scale=True)
def dictionary_out2file(R_dic, log_fileout):
DNN_tools.log_string('PDE type for problem: %s\n' % (R_dic['PDE_type']), log_fileout)
DNN_tools.log_string('Equation name for problem: %s\n' % (R_dic['eqs_name']), log_fileout)
if R_dic['activate_stop'] != 0:
DNN_tools.log_string('activate the stop_step and given_step= %s\n' % str(R_dic['max_epoch']), log_fileout)
else:
DNN_tools.log_string('no activate the stop_step and given_step = default: %s\n' % str(R_dic['max_epoch']), log_fileout)
DNN_tools.log_string('Network model of solving problem: %s\n' % str(R_dic['model']), log_fileout)
DNN_tools.log_string('Activate function for network: %s\n' % str(R_dic['activate_func']), log_fileout)
if R_dic['model'] != 'DNN':
DNN_tools.log_string('The frequency to neural network: %s\n' % (R_dic['freqs']), log_fileout)
if (R_dic['optimizer_name']).title() == 'Adam':
DNN_tools.log_string('optimizer:%s\n' % str(R_dic['optimizer_name']), log_fileout)
else:
DNN_tools.log_string('optimizer:%s with momentum=%f\n' % (R_dic['optimizer_name'], R_dic['momentum']), log_fileout)
if R_dic['train_group'] == 0:
DNN_tools.log_string('Training total loss \n', log_fileout)
elif R_dic['train_group'] == 1:
DNN_tools.log_string('Training total loss + parts loss \n', log_fileout)
elif R_dic['train_group'] == 2:
DNN_tools.log_string('Training parts loss \n', log_fileout)
DNN_tools.log_string('Init learning rate: %s\n' % str(R_dic['learning_rate']), log_fileout)
DNN_tools.log_string('Decay to learning rate: %s\n' % str(R_dic['learning_rate_decay']), log_fileout)
DNN_tools.log_string('hidden layer:%s\n' % str(R_dic['hidden_layers']), log_fileout)
DNN_tools.log_string('Batch-size 2 integral: %s\n' % str(R_dic['batch_size2integral']), log_fileout)
DNN_tools.log_string('Batch-size 2 auxiliary: %s\n' % str(R_dic['batch_size2auxiliary']), log_fileout)
def dictionary_out2file(R_dic, log_fileout):
# -----------------------------------------------------------------------------------------------------------------
DNN_tools.log_string('PDE type for problem: %s\n' % (R_dic['PDE_type']),
log_fileout)
DNN_tools.log_string(
'Equation name for problem: %s\n' % (R_dic['equa_name']), log_fileout)
if R_dic['input_dim'] == 1:
if R_dic['PDE_type'] == 'pLaplace':
DNN_tools.log_string(
'The order of pLaplace operator: %s\n' %
(R_dic['order2pLaplace_operator']), log_fileout)
DNN_tools.log_string(
'The epsilon to pLaplace operator: %f\n' % (R_dic['epsilon']),
log_fileout)
if R_dic['PDE_type'] == 'Possion_Boltzmann':
DNN_tools.log_string(
'The order of pLaplace operator: %s\n' %
(R_dic['order2pLaplace_operator']), log_fileout)
DNN_tools.log_string(
'The epsilon to pLaplace operator: %f\n' % (R_dic['epsilon']),
log_fileout)
elif R_dic['input_dim'] == 2:
if R_dic['PDE_type'] == 'pLaplace_implicit' or R_dic[
'PDE_type'] == 'pLaplace_explicit':
DNN_tools.log_string(
'The order of pLaplace operator: %s\n' %
(R_dic['order2pLaplace_operator']), log_fileout)
DNN_tools.log_string(
'The mesh_number: %f\n' % (R_dic['mesh_number']), log_fileout)
elif R_dic['PDE_type'] == 'Possion_Boltzmann':
DNN_tools.log_string(
'The mesh_number: %f\n' % (R_dic['mesh_number']), log_fileout)
else:
DNN_tools.log_string(
'The order of pLaplace operator: %s\n' %
(R_dic['order2pLaplace_operator']), log_fileout)
DNN_tools.log_string(
'The epsilon to pLaplace operator: %f\n' % (R_dic['epsilon']),
log_fileout)
# -----------------------------------------------------------------------------------------------------------------
DNN_tools.log_string(
'Network model of solving problem: %s\n' % str(R_dic['model2NN']),
log_fileout)
if R_dic['model2NN'] == 'DNN_FourierBase' or R_dic[
'model2NN'] == 'Fourier_DNN':
DNN_tools.log_string(
'Activate function for NN-input: %s\n' % '[Sin;Cos]', log_fileout)
else:
DNN_tools.log_string(
'Activate function for NN-input: %s\n' % str(R_dic['name2act_in']),
log_fileout)
DNN_tools.log_string(
'Activate function for NN-hidden: %s\n' %
str(R_dic['name2act_hidden']), log_fileout)
DNN_tools.log_string(
'Activate function for NN-output: %s\n' % str(R_dic['name2act_out']),
log_fileout)
DNN_tools.log_string('hidden layer:%s\n' % str(R_dic['hidden_layers']),
log_fileout)
if R_dic['model2NN'] != 'DNN':
DNN_tools.log_string(
'The frequency to neural network: %s\n' % (R_dic['freq']),
log_fileout)
if R_dic['model2NN'] == 'DNN_FourierBase' or R_dic[
'model2NN'] == 'Fourier_DNN':
DNN_tools.log_string(
'The scale-factor to fourier basis: %s\n' % (R_dic['sfourier']),
log_fileout)
if R_dic['loss_type'] == 'variational_loss':
DNN_tools.log_string('Loss function: variational loss\n', log_fileout)
else:
DNN_tools.log_string('Loss function: L2 loss\n', log_fileout)
if (R_dic['train_model']) == 'union_training':
DNN_tools.log_string(
'The model for training loss: %s\n' % 'total loss', log_fileout)
elif (R_dic['train_model']) == 'group3_training':
DNN_tools.log_string(
'The model for training loss: %s\n' %
'total loss + loss_it + loss_bd', log_fileout)
elif (R_dic['train_model']) == 'group2_training':
DNN_tools.log_string(
'The model for training loss: %s\n' % 'total loss + loss_bd',
log_fileout)
if (R_dic['optimizer_name']).title() == 'Adam':
DNN_tools.log_string('optimizer:%s\n' % str(R_dic['optimizer_name']),
log_fileout)
else:
DNN_tools.log_string(
'optimizer:%s with momentum=%f\n' %
(R_dic['optimizer_name'], R_dic['momentum']), log_fileout)
DNN_tools.log_string(
'Init learning rate: %s\n' % str(R_dic['learning_rate']), log_fileout)
DNN_tools.log_string(
'Decay to learning rate: %s\n' % str(R_dic['learning_rate_decay']),
log_fileout)
# -----------------------------------------------------------------------------------------------------------------
DNN_tools.log_string(
'Batch-size 2 interior: %s\n' % str(R_dic['batch_size2interior']),
log_fileout)
DNN_tools.log_string(
'Batch-size 2 boundary: %s\n' % str(R_dic['batch_size2boundary']),
log_fileout)
DNN_tools.log_string(
'Initial boundary penalty: %s\n' % str(R_dic['init_boundary_penalty']),
log_fileout)
if R_dic['activate_penalty2bd_increase'] == 1:
DNN_tools.log_string(
'The penalty of boundary will increase with training going on.\n',
log_fileout)
elif R_dic['activate_penalty2bd_increase'] == 2:
DNN_tools.log_string(
'The penalty of boundary will decrease with training going on.\n',
log_fileout)
else:
DNN_tools.log_string(
'The penalty of boundary will keep unchanged with training going on.\n',
log_fileout)
if R_dic['activate_stop'] != 0:
DNN_tools.log_string(
'activate the stop_step and given_step= %s\n' %
str(R_dic['max_epoch']), log_fileout)
else:
DNN_tools.log_string(
'no activate the stop_step and given_step = default: %s\n' %
str(R_dic['max_epoch']), log_fileout)
def print_and_log_train_one_epoch(i_epoch,
run_time,
tmp_lr,
temp_penalty_bd,
pwb,
loss_it_tmp,
loss_bd_tmp,
loss_tmp,
train_mse_tmp,
train_rel_tmp,
log_out=None):
# 将运行结果打印出来
print('train epoch: %d, time: %.3f' % (i_epoch, run_time))
print('learning rate: %f' % tmp_lr)
print('boundary penalty: %f' % temp_penalty_bd)
print('weights and biases with penalty: %f' % pwb)
print('loss_it for training: %.10f' % loss_it_tmp)
print('loss_bd for training: %.10f' % loss_bd_tmp)
print('loss for training: %.10f' % loss_tmp)
print('solution mean square error for training: %.10f' % train_mse_tmp)
print('solution residual error for training: %.10f\n' % train_rel_tmp)
DNN_tools.log_string('train epoch: %d,time: %.3f' % (i_epoch, run_time),
log_out)
DNN_tools.log_string('learning rate: %f' % tmp_lr, log_out)
DNN_tools.log_string('boundary penalty: %f' % temp_penalty_bd, log_out)
DNN_tools.log_string('weights and biases with penalty: %f' % pwb, log_out)
DNN_tools.log_string('loss_it for training: %.10f' % loss_it_tmp, log_out)
DNN_tools.log_string('loss_bd for training: %.10f' % loss_bd_tmp, log_out)
DNN_tools.log_string('loss for training: %.10f' % loss_tmp, log_out)
DNN_tools.log_string(
'solution mean square error for training: %.10f' % train_mse_tmp,
log_out)
DNN_tools.log_string(
'solution residual error for training: %.10f\n' % train_rel_tmp,
log_out)
