def __init__(self, ndim_total, dim_x, dim_y, dim_z, hidden_dim=128):
super(INN, self).__init__()
nodes = [InputNode(ndim_total, name='input')]
self.hidden_dim = hidden_dim
self.ndim_total = ndim_total
self.dim_x = dim_x
self.dim_y = dim_y
self.dim_z = dim_z
for k in range(4):
nodes.append(
Node(nodes[-1],
GLOWCouplingBlock, {
'subnet_constructor': self.subnet_fc,
'clamp': 2.0
},
name=F'coupling_{k}'))
nodes.append(
Node(nodes[-1],
PermuteRandom, {'seed': k},
name=F'permute_{k}'))
nodes.append(OutputNode(nodes[-1], name='output'))
self.model = ReversibleGraphNet(nodes, verbose=False)
self.zeros_noise_scale = 5e-2
self.y_noise_scale = 1e-1
def cINN(flags):
"""
The constructor for INN network
:param flags: input flags from configuration
:return: The INN network
"""
# Set up the conditional node (y)
cond_node = ConditionNode(flags.dim_y)
# Start from input layer
nodes = [InputNode(flags.dim_x, name='input')]
# Recursively add the coupling layers and random permutation layer
for i in range(flags.couple_layer_num):
nodes.append(
Node(nodes[-1],
GLOWCouplingBlock, {
'subnet_constructor': subnet_fc,
'clamp': 2.0
},
conditions=cond_node,
name='coupling_{}'.format(i)))
nodes.append(
Node(nodes[-1],
PermuteRandom, {'seed': i},
name='permute_{}'.format(i)))
# Attach the output Node
nodes.append(OutputNode(nodes[-1], name='output'))
nodes.append(cond_node)
print("The nodes are:", nodes)
# Return the
return ReversibleGraphNet(nodes, verbose=True)
def __init__(self, inputs, outputs, zeroPadding=0, numInvLayers=5, dropout=0.00, minSize=None):
# Determine dimensions and construct DataSchema
inMinLength = schema_min_len(inputs, zeroPadding)
outMinLength = schema_min_len(outputs, zeroPadding)
minLength = max(inMinLength, outMinLength)
if minSize is not None:
minLength = max(minLength, minSize)
self.inSchema = DataSchema1D(inputs, minLength, zeroPadding)
self.outSchema = DataSchema1D(outputs, minLength, zeroPadding)
if len(self.inSchema) != len(self.outSchema):
raise ValueError('Input and output schemas do not have the same dimension.')
# Build net graph
inp = InputNode(len(self.inSchema), name='Input (0-pad extra channels)')
nodes = [inp]
for i in range(numInvLayers):
nodes.append(Node([nodes[-1].out0], rev_multiplicative_layer,
{'F_class': F_fully_connected_leaky, 'clamp': 2.0,
'F_args': {'dropout': 0.0}}, name='Inv%d' % i))
if (i != numInvLayers - 1):
nodes.append(Node([nodes[-1].out0], permute_layer, {'seed': i}, name='Permute%d' % i))
nodes.append(OutputNode([nodes[-1].out0], name='Output'))
# Build net
super().__init__(nodes)
def model(dim_x,
dim_y,
dim_z,
dim_total,
lr,
l2_reg,
meta_epoch,
gamma,
hidden_depth=8):
nodes = []
# 定义输入层节点
nodes.append(InputNode(dim_total, name='input'))
# 定义隐藏层节点
for k in range(hidden_depth):
nodes.append(
Node(nodes[-1],
GLOWCouplingBlock, {
'subnet_constructor': F_fully_connected,
'clamp': 2.0,
},
name='coupling_{k}'))
nodes.append(
Node(nodes[-1], PermuteRandom, {'seed': 1}, name='permute_{k}'))
nodes.append(
Node(nodes[-1],
GLOWCouplingBlock, {
'subnet_constructor': F_fully_connected,
'clamp': 2.0,
},
name='coupling_{k}'))
# 定义输出层节点
nodes.append(OutputNode(nodes[-1], name='output'))
# 构建可逆网络
inn = ReversibleGraphNet(nodes)
# 定义优化器
# TODO:参数调整
optimizer = torch.optim.Adam(inn.parameters(),
lr=lr,
betas=(0.9, 0.999),
eps=1e-04,
weight_decay=l2_reg)
# 学习率调整
# TODO:参数调整
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=meta_epoch,
gamma=gamma)
# 损失函数设置
# x，z无监督：MMD，y有监督：平方误差
loss_backward = MMD_multiscale
loss_latent = MMD_multiscale
loss_fit = fit
return inn, optimizer, scheduler, loss_backward, loss_latent, loss_fit
def main():
# ---------------------------------------生成数据------------------------------------------
t_generate_start = time()
# 设置模拟数据参数
r = 3 # the grid dimension for the output tests
test_split = r * r # number of testing samples to use
optical_model = 'km' # the optical model to use
ydim = 31 # number of data samples
bound = [0.1, 0.9, 0.1, 0.9]
seed = 1 # seed for generating data
# 生成训练数据
# concentrations, reflectance, x, info = data.generate(
# model=optical_model,
# total_dataset_size=2 ** 20 * 20,
# ydim=ydim,
# prior_bound=bound,
# seed=seed
# )
concentrations, reflectance, x, info = data.math_optimized_generate()
print("\n\nGenerating data took %.2f minutes\n" %
((time() - t_generate_start) / 60))
colors = np.arange(0, concentrations.shape[-1], 1)
# 选取几个不参与训练，用作最后的测试样本
c_test = concentrations[-test_split:]
r_test = reflectance[-test_split:]
# 测试样本分光反射率图，用于观察，与模型无关
plt.figure(figsize=(6, 6))
fig, axes = plt.subplots(r, r, figsize=(6, 6))
cnt = 0
for i in range(r):
for j in range(r):
axes[i, j].plot(x, np.array(r_test[cnt, :]), '-')
cnt += 1
axes[i, j].axis([400, 700, 0, 1])
plt.savefig('test_target_reflectance.png', dpi=360)
plt.close()
print("\n\nGenerating data took %.2f minutes\n" %
((time() - t_generate_start) / 60))
# ---------------------------------------构建网络------------------------------------------
# 设置模型参数值
ndim_x = concentrations.shape[-1] # 配方的维度，即待选色浆的种类数
ndim_y = ydim # 反射率的维度 31
ndim_z = 13 # 潜在空间的维度
ndim_tot = max(ndim_x, ndim_y + ndim_z)
# 定义神经网络的不同部分
# 定义输入层节点
inp = InputNode(ndim_tot, name='input')
# 定义隐藏层节点
t1 = Node([inp.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.2
}
})
p1 = Node([t1.out0], permute_layer, {'seed': 1})
t2 = Node([p1.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.2
}
})
p2 = Node([t2.out0], permute_layer, {'seed': 2})
t3 = Node([p2.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.2
}
})
p3 = Node([t3.out0], permute_layer, {'seed': 1})
t4 = Node([p3.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.2
}
})
p4 = Node([t4.out0], permute_layer, {'seed': 2})
t5 = Node([p4.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.2
}
})
# 定义输出层节点
outp = OutputNode([t5.out0], name='output')
# 构建网络
nodes = [inp, t1, p1, t2, p2, t3, p3, t4, p4, t5, outp]
model = ReversibleGraphNet(nodes)
# ---------------------------------------训练网络------------------------------------------
# 超参数
# n_epochs = 3000 # 训练轮数
n_epochs = 0 # 训练轮数
plot_cadence = 100 # 每100步画一次损失函数图
meta_epoch = 12 # 调整学习率的步长
n_its_per_epoch = 12 # 每次训练12批数据
batch_size = 1600 # 每批1600个样本
lr = 1.5e-3 # 初始学习率
gamma = 0.004**(1. / 1333) # 学习率下降的乘数因子
l2_reg = 2e-5 # 权重衰减（L2惩罚）
# 为了让输入和输出维度相同，对维度进行补齐，不使用0，而是使用一些很小的值
y_noise_scale = 3e-2
zeros_noise_scale = 3e-2
# 损失的权重
lambd_predict = 300. # forward pass
lambd_latent = 300. # laten space
lambd_rev = 400. # backwards pass
# 定义优化器
# params：待优化参数，lr：学习率，betas:用于计算梯度以及梯度平方的运行平均值的系数
# eps:为了增加数值计算的稳定性而加到分母里的项
# weight_decay:权重衰减
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
betas=(0.8, 0.8),
eps=1e-04,
weight_decay=l2_reg)
# 学习率调整
# optimizer:优化器
# step_size:调整学习率的步长
# gamma:学习率下降的乘数因子
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=meta_epoch,
gamma=gamma)
# 损失函数设置
# x，z无监督：MMD，y有监督：平方误差
loss_backward = MMD_multiscale
loss_latent = MMD_multiscale
loss_fit = fit
# 训练集数据加载
train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(
concentrations[test_split:], reflectance[test_split:]),
batch_size=batch_size,
shuffle=True,
drop_last=True)
# 初始化网络权重
for mod_list in model.children():
for block in mod_list.children():
for coeff in block.children():
coeff.fc3.weight.data = 0.01 * torch.randn(
coeff.fc3.weight.shape)
model.to(device)
# 初始化测试结果图表
fig, axes = plt.subplots(r, r, figsize=(6, 6))
# 测试用例数量
N_samp = 256
# ---------------------------------------开始训练------------------------------------------
try:
t_start = time() # 训练开始时间
loss_for_list = [] # 记录前向训练的损失
loss_rev_list = [] # 记录反向训练的损失
tsne = TSNE(n_components=2, init='pca')
# 颜色编号
color_names = [
'07H', '08', '08S', '09', '09B', '09S', '10B', '12', '13', '14',
'15', '16', '17A', '18A', '19A', '20A-2', '23A', '2704', '2803',
'2804', '2807'
]
# n_epochs次迭代过程
for i_epoch in tqdm(range(n_epochs), ascii=True, ncols=80):
scheduler.step()
# TODO:这个if并不会进入
# Initially, the l2 reg. on x and z can give huge gradients, set
# the lr lower for this
if i_epoch < 0:
print('inside this iepoch<0 thing')
for param_group in optimizer.param_groups:
param_group['lr'] = lr * 1e-2
# 训练模型
avg_loss, loss_for, loss_rev = train(
model, train_loader, n_its_per_epoch, zeros_noise_scale,
batch_size, ndim_tot, ndim_x, ndim_y, ndim_z, y_noise_scale,
optimizer, lambd_predict, loss_fit, lambd_latent, loss_latent,
lambd_rev, loss_backward, i_epoch)
# 添加正向和逆向的损失
loss_for_list.append(loss_for.item())
loss_rev_list.append(loss_rev.item())
inn_losses = [loss_for_list, loss_rev_list]
if ((i_epoch + 1) % plot_cadence == 0) & (i_epoch > 0):
plot_losses(inn_losses,
legend=['PE-GEN'],
lossNo=int((i_epoch + 1) / plot_cadence))
# TODO
model = torch.load('model_dir/km_impl_model')
# torch.save(model, 'model_dir/km_impl_model')
fig, axes = plt.subplots(1, 1, figsize=(2, 2))
# 真实样本对应的反射率信息
test_samps = np.array([[
0.2673378, 0.3132285, 0.3183329, 0.3234908, 0.3318701, 0.3409707,
0.3604081, 0.4168356, 0.5351773, 0.6202191, 0.6618687, 0.6919741,
0.7136238, 0.7292901, 0.7314631, 0.7131701, 0.6773048, 0.6302681,
0.5738088, 0.5133060, 0.4535525, 0.4108878, 0.3908512, 0.3808001,
0.3752591, 0.3727644, 0.3801365, 0.3976869, 0.4237110, 0.4332685,
0.4433292
]])
# 真实样本对应的配方
test_cons = np.array([[
0, 0.8014, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1491, 0, 0, 0,
0.2241, 0
]])
for cnt in range(test_samps.shape[0]):
print('before:', cnt, test_samps[cnt, :])
test_samp = np.tile(np.array(test_samps[cnt, :]),
N_samp).reshape(N_samp, ydim)
test_samp = torch.tensor(test_samp, dtype=torch.float)
test_samp += y_noise_scale * torch.randn(N_samp, ydim)
test_samp = torch.cat(
[
torch.randn(N_samp, ndim_z), # zeros_noise_scale *
torch.zeros(N_samp, ndim_tot - ndim_y - ndim_z),
test_samp
],
dim=1)
test_samp = test_samp.to(device)
print('after:', cnt, test_samp)
# use the network to predict parameters
test_rev = model(test_samp, rev=True)[:, :colors.size]
test_rev = test_rev.cpu().data.numpy()
# 假设涂料浓度小于一定值，就不需要这种涂料
test_rev = np.where(test_rev < 0.1, 0, test_rev)
# 计算预测配方的反射率信息
# recipe_ref = data.recipe_reflectance(test_rev, optical_model)
# 使用修正后的模型计算配方的反射率信息
recipe_ref = data.correct_recipe_reflectance(test_rev)
print("######## Test Sample %d ########" % cnt)
# 用于记录色差最小的三个配方
top3 = [[100, 0], [100, 0], [100, 0]]
for n in range(test_rev.shape[0]):
# print(test_rev[n, :])
diff = data.color_diff(test_samps[cnt, :], recipe_ref[n, :])
if diff < top3[2][0]:
top3[2][0] = diff
top3[2][1] = n
top3.sort()
# 将色差最小的三个配方打印出来
for n in range(3):
print(test_rev[top3[n][1], :])
print("color diff: %.2f \n" % top3[n][0])
print("\n\n")
# draw
# feature scaling
test_x = test_cons[cnt, :].reshape(1, test_cons[cnt, :].shape[-1])
plot_x = np.concatenate((test_rev, test_x), axis=0)
# use tsne to decrease dimensionality
x_norm = pd.DataFrame(plot_x, columns=color_names)
# 根据需要的涂料种类（需要为1，不需要为0）将配方分类
classes = np.zeros(N_samp).reshape(N_samp, 1)
paint_needed = np.where(test_rev == 0, 0, 1)
for paint_no in colors:
classes[:, 0] += paint_needed[:, paint_no] * 2**paint_no
class_norm = pd.DataFrame(np.concatenate(
(classes, np.zeros(1).reshape(1, 1)), axis=0),
columns=['class'])
data_plot = pd.concat(
[pd.DataFrame(tsne.fit_transform(x_norm)), class_norm], axis=1)
class_data = data_plot['class']
axes.clear()
recipe_classes = np.array(
class_norm[:-1].drop_duplicates()).reshape(1, -1).tolist()[0]
for recipe_class in recipe_classes:
axes.scatter(data_plot[class_data == recipe_class][0],
data_plot[class_data == recipe_class][1],
s=2,
alpha=0.5)
axes.scatter(data_plot[class_data == 0][0],
data_plot[class_data == 0][1],
marker='+',
s=10)
fig.canvas.draw()
plt.savefig('test_result%d.png' % cnt, dpi=360)
# loop over a few cases and plot results in a grid
cnt = 0
for i in range(r):
for j in range(r):
# convert data into correct format
y_samps = np.tile(np.array(r_test[cnt, :]),
N_samp).reshape(N_samp, ydim)
y_samps = torch.tensor(y_samps, dtype=torch.float)
y_samps += y_noise_scale * torch.randn(N_samp, ydim)
y_samps = torch.cat(
[
torch.randn(N_samp, ndim_z), # zeros_noise_scale *
torch.zeros(N_samp, ndim_tot - ndim_y - ndim_z),
y_samps
],
dim=1)
y_samps = y_samps.to(device)
# use the network to predict parameters
rev_x = model(y_samps, rev=True)[:, :colors.size]
rev_x = rev_x.cpu().data.numpy()
# 假设涂料浓度小于一定值，就不需要这种涂料
rev_x = np.where(rev_x < 0.1, 0, rev_x)
# feature scaling
test_x = c_test[cnt, :].reshape(1, c_test[cnt, :].shape[-1])
plot_x = np.concatenate((rev_x, test_x), axis=0)
# use pca to decrease dimensionality
x_norm = pd.DataFrame(plot_x, columns=color_names)
# 根据需要的涂料种类（需要为1，不需要为0）将配方分类
classes = np.zeros(N_samp).reshape(N_samp, 1)
paint_needed = np.where(rev_x == 0, 0, 1)
for paint_no in colors:
classes[:, 0] += paint_needed[:, paint_no] * 2**paint_no
class_norm = pd.DataFrame(np.concatenate(
(classes, np.zeros(1).reshape(1, 1)), axis=0),
columns=['class'])
data_plot = pd.concat(
[pd.DataFrame(tsne.fit_transform(x_norm)), class_norm],
axis=1)
class_data = data_plot['class']
# plot the predicted and the true recipe
axes.clear()
recipe_classes = np.array(
class_norm[:-1].drop_duplicates()).reshape(1,
-1).tolist()[0]
for recipe_class in recipe_classes:
axes.scatter(data_plot[class_data == recipe_class][0],
data_plot[class_data == recipe_class][1],
s=2,
alpha=0.5)
axes.scatter(data_plot[class_data == 0][0],
data_plot[class_data == 0][1],
marker='+',
s=10)
fig.canvas.draw()
plt.savefig('training_result%d.png' % cnt, dpi=360)
# recipe_ref = data.recipe_reflectance(rev_x, optical_model)
# 使用修正后的模型计算配方的反射率信息
recipe_ref = data.correct_recipe_reflectance(rev_x)
print("######## Test %d ########" % cnt)
print(c_test[cnt])
print("################")
# 用于记录色差最小的三个配方
top3 = [[100, 0], [100, 0], [100, 0]]
for n in range(rev_x.shape[0]):
# print(rev_x[n, :])
diff = data.color_diff(r_test[cnt].numpy(),
recipe_ref[n, :])
if diff < top3[2][0]:
top3[2][0] = diff
top3[2][1] = n
top3.sort()
# 将色差最小的三个配方打印出来
for n in range(3):
print(test_rev[top3[n][1], :])
print("color diff: %.2f \n" % top3[n][0])
print("\n\n")
cnt += 1
except KeyboardInterrupt:
pass
finally:
print("\n\nTraining took %.2f minutes\n" % ((time() - t_start) / 60))
def main():
# Set up data
# make training signals
signal_train_pars = []
signal_train_images = []
for i in range(total_temp_num):
signal_train_pars.append(
[np.random.uniform(-1.0, 1.0),
np.random.uniform(0.5, 1.5)])
signal_train_images.append(
np.random.normal(loc=signal_train_pars[i][0],
scale=signal_train_pars[i][1],
size=(1, n_pix)))
signal_train_pars = np.array(signal_train_pars)
signal_train_images = np.array(signal_train_images).reshape(
total_temp_num, n_pix)
# make random 1D gaussian signal
noise_signal = np.random.normal(loc=0.0, scale=1.0, size=(1, n_pix))
#noise_signal = norm.rvs(0,1.0,(1,n_pix))
signal_pars = [0.0, 1.0]
# load in lalinference samples
#with open('gw_data/data/gw150914_mc_q_lalinf_post_srate-1024_python3.sav','rb' ) as f:
# lalinf_post = pickle.load(f)
#lalinf_mc = lalinf_post[0]
#lalinf_q = lalinf_post[1]
# declare gw variants of positions and labels
labels = torch.tensor(signal_train_images, dtype=torch.float)
pos = torch.tensor(signal_train_pars, dtype=torch.float)
# setting up the model
ndim_tot = n_pix + n_neurons # two times the number data dimensions?
ndim_x = 2 # number of parameter dimensions
ndim_y = n_pix # number of data dimensions
ndim_z = 10 # number of latent space dimensions?
# define different parts of the network
# define input node
inp = InputNode(ndim_tot, name='input')
# define hidden layer nodes
t1 = Node([inp.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.0
}
})
t2 = Node([t1.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.0
}
})
"""
t3 = Node([t2.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.2}})
t4 = Node([t3.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}})
"""
# define output layer node
outp = OutputNode([t2.out0], name='output')
nodes = [inp, t1, t2, outp]
model = ReversibleGraphNet(nodes)
# Train model
lr = 1e-2
decayEpochs = (n_epochs * n_its_per_epoch) // meta_epoch
gamma = 0.004**(1.0 / decayEpochs)
l2_reg = 2e-5
#gamma = 0.01**(1./120)
y_noise_scale = 3e-2 # amount of noise to add to y parameter?
zeros_noise_scale = 3e-2 # what is this??
# relative weighting of losses:
lambd_predict = 300. # forward pass
lambd_latent = 300. # laten space
lambd_rev = 400. # backwards pass
# padding both the data and the latent space
# such that they have equal dimension to the parameter space
pad_x = torch.zeros(batch_size, ndim_tot - ndim_x)
pad_yz = torch.zeros(batch_size, ndim_tot - ndim_y - ndim_z)
# define optimizer
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
betas=(0.8, 0.8),
eps=1e-04,
weight_decay=l2_reg)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=meta_epoch,
gamma=gamma)
# define the three loss functions
loss_backward = MMD_multiscale
loss_latent = MMD_multiscale
loss_fit = fit
# set up test set data loader
test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(
pos[:test_split], labels[:test_split]),
batch_size=batch_size,
shuffle=True,
drop_last=True)
# set up training set data loader
train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(
pos[:], labels[:]),
batch_size=batch_size,
shuffle=True,
drop_last=True)
# what is happening here? More set up of network?
for mod_list in model.children():
for block in mod_list.children():
for coeff in block.children():
coeff.fc3.weight.data = 0.01 * torch.randn(
coeff.fc3.weight.shape)
model.to(device)
# number of test samples to use after training
N_samp = 4000
# choose test samples to use after training
# 1000 iterations of test signal burried in noise. Only need to change z parameter.
#x_samps = torch.cat([x for x,y in test_loader], dim=0)[:N_samp]
#y_samps = torch.cat([y for x,y in test_loader], dim=0)[:N_samp]
#y_samps += torch.randn(N_samp, ndim_y) #* y_noise_scale
y_samps_nparray = np.repeat(noise_signal, N_samp, axis=0)
y_samps = torch.tensor(y_samps_nparray, dtype=torch.float)
# make test samples. First element is the latent space dimension
# second element is the extra zeros needed to pad the input.
# the third element is the time series
y_samps = torch.cat(
[
torch.randn(N_samp, ndim_z),
zeros_noise_scale * torch.zeros(
N_samp, ndim_tot - ndim_y - ndim_z), # zeros_noise_scale *
y_samps
],
dim=1)
# what we should have now are 1000 copies of the event burried in noise with zero padding up to 2048
y_samps = y_samps.to(device)
# get control contour values
cont_mu, cont_sig, prob, levels = compute_like(noise_signal.reshape(
n_pix, ),
N=n_pix)
#lalinf_post_blah = np.array([np.random.normal(loc=0,scale=1.0,size=(N_samp)), np.random.normal(loc=1.0,scale=1.0,size=(N_samp))])
# start training loop
lossf_hist = []
lossrev_hist = []
beta_score_hist = []
try:
# print('#Epoch \tIt/s \tl_total')
t_start = time()
# loop over number of epochs
for i_epoch in tqdm(range(n_epochs), ascii=True, ncols=80):
scheduler.step()
# Initially, the l2 reg. on x and z can give huge gradients, set
# the lr lower for this
if i_epoch < 0:
for param_group in optimizer.param_groups:
param_group['lr'] = lr * 1e-2
#print(i_epoch, end='\t ')
_, lossf, lossrev = train(model, train_loader, n_its_per_epoch,
zeros_noise_scale, batch_size, ndim_tot,
ndim_x, ndim_y, ndim_z, y_noise_scale,
optimizer, lambd_predict, loss_fit,
lambd_latent, loss_latent, lambd_rev,
loss_backward, i_epoch)
# append current loss value to loss histories
lossf_hist.append(lossf.item())
lossrev_hist.append(lossrev.item())
pe_losses = [lossf_hist, lossrev_hist]
# predict parameters of signal
rev_x = model(y_samps, rev=True)
rev_x = rev_x.cpu().data.numpy()
# plot pe results and loss
if ((i_epoch % plot_cadence == 0) & (i_epoch > 0)):
#pe_std = [0.005, 0.01] # this will need to be removed
#beta_score_hist.append([plot_pe_samples(rev_x,signal_pars,out_path,i_epoch,lalinf_post,pe_std)])
#plt.plot(np.linspace(plot_cadence,i_epoch,len(beta_score_hist)),beta_score_hist)
#plt.savefig('%s/latest/beta_hist.png' % out_path)
#plt.close()
# plot loss curves - non-log and log
plot_losses(pe_losses,
'%s/latest/pe_losses.png' % out_path,
legend=['PE-GEN'])
plot_losses(pe_losses,
'%s/latest/pe_losses_logscale.png' % out_path,
logscale=True,
legend=['PE-GEN'])
# make PE scatter plots with contours and beta score
mu0 = 0.0
sig0 = 1.0
plt.scatter(rev_x[:, 0],
rev_x[:, 1],
s=1.,
c='red',
label='INN Results')
plt.contour(cont_mu,
cont_sig,
prob,
levels=[0.68, 0.9, 0.95, 0.99])
plt.plot(mu0, sig0, '+', label='Truth')
plt.xlabel('mean')
plt.ylabel('standard deviation')
plt.legend()
plt.savefig('%s/latest/predicted_pe.png' % out_path)
plt.close()
except KeyboardInterrupt:
pass
finally:
print("\n\nTraining took {(time()-t_start)/60:.2f} minutes\n")
def __init__(self, config, *args):
super(INNModel, self).__init__(config)
self.config.update({
'batch_size': int(2**12),
'lr': 0.001,
'n_epochs': 50,
'loss_epochs': 100,
'test_percent': 20,
'data_size': 8_000_000
})
self.config.update(config)
# self.embedder = torch.load(
# './runs/network_Sep19_23-35-50/checkpoints/model_999.pt'
# ).feature_creator
self.ndim_tot = 10
self.ndim_x = 1
self.ndim_y = 9
self.ndim_z = 1
inp = InputNode(self.ndim_tot, name='input')
t1 = Node([inp.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected,
'F_args': {
'batch_norm': True, 'internal_size': 2,
# 'dropout': 0.3
}})
# t2 = Node([t1.out0], rev_multiplicative_layer,
# {'F_class': F_fully_connected, 'clamp': 2.0,
# 'F_args': {'dropout': 0.5}})
# t3 = Node([t2.out0], rev_multiplicative_layer,
# {'F_class': F_fully_connected, 'clamp': 2.0,
# 'F_args': {'dropout': 0.5}})
outp = OutputNode([t1.out0], name='output')
nodes = [inp, t1, outp]
self.model = ReversibleGraphNet(nodes)
self.model.to(self.device)
self.loss = F.mse_loss
self.x_noise_scale = 3e-2
self.y_noise_scale = 3e-2
self.zeros_noise_scale = 3e-2
# relative weighting of losses:
self.lambd_predict = 3.
self.lambd_latent = 2.
self.lambd_rev = 10.
self.pad_x = torch.zeros(self.config['batch_size'], self.ndim_tot -
self.ndim_x)
# self.pad_yz = torch.zeros(self.config['batch_size'], self.ndim_tot -
# self.ndim_y - self.ndim_z)
def MMD_multiscale(x, y):
xx, yy, zz = torch.mm(x,x.t()), torch.mm(y,y.t()), torch.mm(x,y.t())
rx = (xx.diag().unsqueeze(0).expand_as(xx))
ry = (yy.diag().unsqueeze(0).expand_as(yy))
dxx = rx.t() + rx - 2.*xx
dyy = ry.t() + ry - 2.*yy
dxy = rx.t() + ry - 2.*zz
XX, YY, XY = (torch.zeros(xx.shape).to(self.device),
torch.zeros(xx.shape).to(self.device),
torch.zeros(xx.shape).to(self.device))
for a in [0.2, 0.5, 0.9, 1.3]:
XX += a**2 * (a**2 + dxx)**-1
YY += a**2 * (a**2 + dyy)**-1
XY += a**2 * (a**2 + dxy)**-1
return torch.mean(XX + YY - 2.*XY)
def fit(input, target):
return torch.mean((input - target)**2)
self.loss_backward = MMD_multiscale
self.loss_latent = MMD_multiscale
self.loss_fit = F.l1_loss
self.optimizer = torch.optim.Adam(
self.model.parameters(),
lr=self.config['lr'],
weight_decay=1e2,
# momentum=0.9
)
self.scheduler = torch.optim.lr_scheduler.StepLR(
self.optimizer, 1758 * 5,
0.1
)
def main():
# Set up data
batch_size = 1600 # set batch size
test_split = 10000 # number of testing samples to use
# generate data
# makes a torch.tensor() with arrays of (n_samples X parameters) and (n_samples X data)
# labels are the colours and pos are the x,y coords
# however, labels are 1-hot encoded
pos, labels = data.generate(labels='all', tot_dataset_size=2**20)
# just simply renaming the colors properly.
#c = np.where(labels[:test_split])[1]
c = labels[:test_split, :]
plt.figure(figsize=(6, 6))
plt.scatter(pos[:test_split, 0],
pos[:test_split, 1],
c=c,
cmap='Set1',
s=0.25)
plt.xticks([])
plt.yticks([])
plt.savefig('/data/public_html/chrism/FrEIA/test_distribution.png')
plt.close()
# setting up the model
ndim_tot = 16 # ?
ndim_x = 2 # number of parameter dimensions (x,y)
ndim_y = 3 # number of label dimensions (colours for 1-hot encoding)
ndim_z = 2 # number of latent space dimensions?
# define different parts of the network
# define input node
inp = InputNode(ndim_tot, name='input')
# define hidden layer nodes
t1 = Node([inp.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.0
}
})
t2 = Node([t1.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.0
}
})
t3 = Node([t2.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.0
}
})
# define output layer node
outp = OutputNode([t3.out0], name='output')
nodes = [inp, t1, t2, t3, outp]
model = ReversibleGraphNet(nodes)
# Train model
# Training parameters
n_epochs = 3000
meta_epoch = 12 # what is this???
n_its_per_epoch = 4
batch_size = 1600
lr = 1e-2
gamma = 0.01**(1. / 120)
l2_reg = 2e-5
y_noise_scale = 3e-2
zeros_noise_scale = 3e-2
# relative weighting of losses:
lambd_predict = 300. # forward pass
lambd_latent = 300. # laten space
lambd_rev = 400. # backwards pass
# padding both the data and the latent space
# such that they have equal dimension to the parameter space
pad_x = torch.zeros(batch_size, ndim_tot - ndim_x)
pad_yz = torch.zeros(batch_size, ndim_tot - ndim_y - ndim_z)
print(pad_x.shape, pad_yz.shape)
# define optimizer
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
betas=(0.8, 0.8),
eps=1e-04,
weight_decay=l2_reg)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=meta_epoch,
gamma=gamma)
# define the three loss functions
loss_backward = MMD_multiscale
loss_latent = MMD_multiscale
loss_fit = fit
# set up test set data loader
test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(
pos[:test_split], labels[:test_split]),
batch_size=batch_size,
shuffle=True,
drop_last=True)
# set up training set data loader
train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(
pos[test_split:], labels[test_split:]),
batch_size=batch_size,
shuffle=True,
drop_last=True)
# initialisation of network weights
for mod_list in model.children():
for block in mod_list.children():
for coeff in block.children():
coeff.fc3.weight.data = 0.01 * torch.randn(
coeff.fc3.weight.shape)
model.to(device)
# initialize gif for showing training procedure
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
axes[0].set_xticks([])
axes[0].set_yticks([])
axes[0].set_title('Predicted labels (Forwards Process)')
axes[1].set_xticks([])
axes[1].set_yticks([])
axes[1].set_title('Generated Samples (Backwards Process)')
#fig.show()
#fig.canvas.draw()
# number of test samples to use after training
N_samp = 4096
# choose test samples to use after training
x_samps = torch.cat([x for x, y in test_loader], dim=0)[:N_samp]
y_samps = torch.cat([y for x, y in test_loader], dim=0)[:N_samp]
#c = np.where(y_samps)[1]
#c = y_samps[:,0]
c = np.array(y_samps).reshape(N_samp, ndim_y)
y_samps += y_noise_scale * torch.randn(N_samp, ndim_y)
y_samps = torch.cat([
torch.randn(N_samp, ndim_z), zeros_noise_scale *
torch.zeros(N_samp, ndim_tot - ndim_y - ndim_z), y_samps
],
dim=1)
y_samps = y_samps.to(device)
# start training loop
try:
# print('#Epoch \tIt/s \tl_total')
t_start = time()
# loop over number of epochs
for i_epoch in tqdm(range(n_epochs), ascii=True, ncols=80):
scheduler.step()
# Initially, the l2 reg. on x and z can give huge gradients, set
# the lr lower for this
if i_epoch < 0:
for param_group in optimizer.param_groups:
param_group['lr'] = lr * 1e-2
# print(i_epoch, end='\t ')
train(model, train_loader, n_its_per_epoch, zeros_noise_scale,
batch_size, ndim_tot, ndim_x, ndim_y, ndim_z, y_noise_scale,
optimizer, lambd_predict, loss_fit, lambd_latent,
loss_latent, lambd_rev, loss_backward, i_epoch)
# predict the locations of test labels
rev_x = model(y_samps, rev=True)
rev_x = rev_x.cpu().data.numpy()
# predict the label given a location
#pred_c = model(torch.cat((x_samps, torch.zeros(N_samp, ndim_tot - ndim_x)),
# dim=1).to(device)).data[:, -8:].argmax(dim=1)
pred_c = model(
torch.cat((x_samps, torch.zeros(N_samp, ndim_tot - ndim_x)),
dim=1).to(device)).data[:, -1:].argmax(dim=1)
axes[0].clear()
#axes[0].scatter(tmp_x_samps[:,0], tmp_x_samps[:,1], c=pred_c, cmap='Set1', s=1., vmin=0, vmax=9)
axes[0].axis('equal')
axes[0].axis([-3, 3, -3, 3])
axes[0].set_xticks([])
axes[0].set_yticks([])
axes[1].clear()
axes[1].scatter(rev_x[:, 0],
rev_x[:, 1],
c=c,
cmap='Set1',
s=1.,
vmin=0,
vmax=9)
axes[1].axis('equal')
axes[1].axis([-3, 3, -3, 3])
axes[1].set_xticks([])
axes[1].set_yticks([])
fig.canvas.draw()
plt.savefig('/data/public_html/chrism/FrEIA/training_pred.png')
except KeyboardInterrupt:
pass
finally:
print("\n\nTraining took {(time()-t_start)/60:.2f} minutes\n")
def main():
# Set up simulation parameters
batch_size = 1600 # set batch size
r = 4 # the grid dimension for the output tests
test_split = r*r # number of testing samples to use
sigma = 0.2 # the noise std
ndata = 64 # number of data samples
usepars = [0,1,2,3] # parameter indices to use
seed = 1 # seed for generating data
run_label='gpu0'
out_dir = "/home/hunter.gabbard/public_html/CBC/cINNamon/gausian_results/multipar/%s/" % run_label
# generate data
pos, labels, x, sig, parnames = data.generate(
tot_dataset_size=2**20,
ndata=ndata,
usepars=usepars,
sigma=sigma,
seed=seed
)
print('generated data')
# seperate the test data for plotting
pos_test = pos[-test_split:]
labels_test = labels[-test_split:]
sig_test = sig[-test_split:]
# plot the test data examples
plt.figure(figsize=(6,6))
fig, axes = plt.subplots(r,r,figsize=(6,6),sharex='col',sharey='row')
cnt = 0
for i in range(r):
for j in range(r):
axes[i,j].plot(x,np.array(labels_test[cnt,:]),'.')
axes[i,j].plot(x,np.array(sig_test[cnt,:]),'-')
cnt += 1
axes[i,j].axis([0,1,-1.5,1.5])
axes[i,j].set_xlabel('time') if i==r-1 else axes[i,j].set_xlabel('')
axes[i,j].set_ylabel('h(t)') if j==0 else axes[i,j].set_ylabel('')
plt.savefig('%stest_distribution.png' % out_dir,dpi=360)
plt.close()
# precompute true posterior samples on the test data
cnt = 0
N_samp = 1000
ndim_x = len(usepars)
samples = np.zeros((r*r,N_samp,ndim_x))
for i in range(r):
for j in range(r):
samples[cnt,:,:] = data.get_lik(np.array(labels_test[cnt,:]).flatten(),sigma=sigma,usepars=usepars,Nsamp=N_samp)
print(samples[cnt,:10,:])
cnt += 1
# initialize plot for showing testing results
fig, axes = plt.subplots(r,r,figsize=(6,6),sharex='col',sharey='row')
for k in range(ndim_x):
parname1 = parnames[k]
for nextk in range(ndim_x):
parname2 = parnames[nextk]
if nextk>k:
cnt = 0
for i in range(r):
for j in range(r):
# plot the samples and the true contours
axes[i,j].clear()
axes[i,j].scatter(samples[cnt,:,k], samples[cnt,:,nextk],c='b',s=0.5,alpha=0.5)
axes[i,j].plot(pos_test[cnt,k],pos_test[cnt,nextk],'+c',markersize=8)
axes[i,j].set_xlim([0,1])
axes[i,j].set_ylim([0,1])
axes[i,j].set_xlabel(parname1) if i==r-1 else axes[i,j].set_xlabel('')
axes[i,j].set_ylabel(parname2) if j==0 else axes[i,j].set_ylabel('')
cnt += 1
# save the results to file
fig.canvas.draw()
plt.savefig('%strue_samples_%d%d.png' % (out_dir,k,nextk),dpi=360)
# setting up the model
ndim_x = len(usepars) # number of posterior parameter dimensions (x,y)
ndim_y = ndata # number of label dimensions (noisy data samples)
ndim_z = 4 # number of latent space dimensions?
ndim_tot = max(ndim_x,ndim_y+ndim_z) # must be > ndim_x and > ndim_y + ndim_z
# define different parts of the network
# define input node
inp = InputNode(ndim_tot, name='input')
# define hidden layer nodes
t1 = Node([inp.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.2}})
#t1 = Node([inp.out0], rev_multiplicative_layer,
# {'F_class': F_conv, 'clamp': 2.0,
# 'F_args': {'kernel_size': 3,'leaky_slope': 0.1}})
#def __init__(self, dims_in, F_class=F_fully_connected, F_args={},
# clamp=5.):
# super(rev_multiplicative_layer, self).__init__()
# channels = dims_in[0][0]
#
# self.split_len1 = channels // 2
# self.split_len2 = channels - channels // 2
# self.ndims = len(dims_in[0])
#
# self.clamp = clamp
# self.max_s = exp(clamp)
# self.min_s = exp(-clamp)
#
# self.s1 = F_class(self.split_len1, self.split_len2, **F_args)
# self.t1 = F_class(self.split_len1, self.split_len2, **F_args)
# self.s2 = F_class(self.split_len2, self.split_len1, **F_args)
# self.t2 = F_class(self.split_len2, self.split_len1, **F_args)
t2 = Node([t1.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.2}})
t3 = Node([t2.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.2}})
t4 = Node([t3.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}})
# define output layer node
outp = OutputNode([t4.out0], name='output')
nodes = [inp, t1, t2, t3, t4, outp]
model = ReversibleGraphNet(nodes)
# Train model
# Training parameters
n_epochs = 10000
meta_epoch = 12 # what is this???
n_its_per_epoch = 12
batch_size = 1600
lr = 1e-2
gamma = 0.01**(1./120)
l2_reg = 2e-5
y_noise_scale = 3e-2
zeros_noise_scale = 3e-2
# relative weighting of losses:
lambd_predict = 300. # forward pass
lambd_latent = 300. # laten space
lambd_rev = 400. # backwards pass
# padding both the data and the latent space
# such that they have equal dimension to the parameter space
#pad_x = torch.zeros(batch_size, ndim_tot - ndim_x)
#pad_yz = torch.zeros(batch_size, ndim_tot - ndim_y - ndim_z)
# define optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=lr, betas=(0.8, 0.8),
eps=1e-04, weight_decay=l2_reg)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=meta_epoch,
gamma=gamma)
# define the three loss functions
loss_backward = MMD_multiscale
loss_latent = MMD_multiscale
loss_fit = fit
# set up training set data loader
train_loader = torch.utils.data.DataLoader(
torch.utils.data.TensorDataset(pos[test_split:], labels[test_split:]),
batch_size=batch_size, shuffle=True, drop_last=True)
# initialisation of network weights
#for mod_list in model.children():
# for block in mod_list.children():
# for coeff in block.children():
# coeff.fc3.weight.data = 0.01*torch.randn(coeff.fc3.weight.shape)
#model.to(device)
# start training loop
try:
t_start = time()
olvec = np.zeros((r,r,int(n_epochs/10)))
s = 0
# loop over number of epochs
for i_epoch in tqdm(range(n_epochs), ascii=True, ncols=80):
scheduler.step()
# Initially, the l2 reg. on x and z can give huge gradients, set
# the lr lower for this
if i_epoch < 0:
print('inside this iepoch<0 thing')
for param_group in optimizer.param_groups:
param_group['lr'] = lr * 1e-2
# train the model
train(model,train_loader,n_its_per_epoch,zeros_noise_scale,batch_size,
ndim_tot,ndim_x,ndim_y,ndim_z,y_noise_scale,optimizer,lambd_predict,
loss_fit,lambd_latent,loss_latent,lambd_rev,loss_backward,i_epoch)
# loop over a few cases and plot results in a grid
if np.remainder(i_epoch,10)==0:
for k in range(ndim_x):
parname1 = parnames[k]
for nextk in range(ndim_x):
parname2 = parnames[nextk]
if nextk>k:
cnt = 0
# initialize plot for showing testing results
fig, axes = plt.subplots(r,r,figsize=(6,6),sharex='col',sharey='row')
for i in range(r):
for j in range(r):
# convert data into correct format
y_samps = np.tile(np.array(labels_test[cnt,:]),N_samp).reshape(N_samp,ndim_y)
y_samps = torch.tensor(y_samps, dtype=torch.float)
y_samps += y_noise_scale * torch.randn(N_samp, ndim_y)
y_samps = torch.cat([torch.randn(N_samp, ndim_z), zeros_noise_scale *
torch.zeros(N_samp, ndim_tot - ndim_y - ndim_z),
y_samps], dim=1)
y_samps = y_samps.to(device)
# use the network to predict parameters
rev_x = model(y_samps, rev=True)
rev_x = rev_x.cpu().data.numpy()
# compute the n-d overlap
if k==0 and nextk==1:
ol = data.overlap(samples[cnt,:,:ndim_x],rev_x[:,:ndim_x])
olvec[i,j,s] = ol
# plot the samples and the true contours
axes[i,j].clear()
axes[i,j].scatter(samples[cnt,:,k], samples[cnt,:,nextk],c='b',s=0.2,alpha=0.5)
axes[i,j].scatter(rev_x[:,k], rev_x[:,nextk],c='r',s=0.2,alpha=0.5)
axes[i,j].plot(pos_test[cnt,k],pos_test[cnt,nextk],'+c',markersize=8)
axes[i,j].set_xlim([0,1])
axes[i,j].set_ylim([0,1])
oltxt = '%.2f' % olvec[i,j,s]
axes[i,j].text(0.90, 0.95, oltxt,
horizontalalignment='right',
verticalalignment='top',
transform=axes[i,j].transAxes)
matplotlib.rc('xtick', labelsize=8)
matplotlib.rc('ytick', labelsize=8)
axes[i,j].set_xlabel(parname1) if i==r-1 else axes[i,j].set_xlabel('')
axes[i,j].set_ylabel(parname2) if j==0 else axes[i,j].set_ylabel('')
cnt += 1
# save the results to file
fig.canvas.draw()
plt.savefig('%sposteriors_%d%d_%04d.png' % (out_dir,k,nextk,i_epoch),dpi=360)
plt.savefig('%slatest_%d%d.png' % (out_dir,k,nextk),dpi=360)
plt.close()
s += 1
# plot overlap results
if np.remainder(i_epoch,10)==0:
fig, axes = plt.subplots(1,figsize=(6,6))
for i in range(r):
for j in range(r):
axes.semilogx(10*np.arange(olvec.shape[2]),olvec[i,j,:],alpha=0.5)
axes.grid()
axes.set_ylabel('overlap')
axes.set_xlabel('epoch')
axes.set_ylim([0,1])
plt.savefig('%soverlap.png' % out_dir,dpi=360)
plt.close()
except KeyboardInterrupt:
pass
finally:
print("\n\nTraining took {(time()-t_start)/60:.2f} minutes\n")
def main():
# Set up simulation parameters
batch_size = 128 # set batch size
r = 3 # the grid dimension for the output tests
test_split = r * r # number of testing samples to use
sig_model = 'sg' # the signal model to use
sigma = 0.2 # the noise std
ndata = 128 #32 number of data samples in time series
bound = [0.0, 1.0, 0.0, 1.0] # effective bound for likelihood
seed = 1 # seed for generating data
out_dir = "/home/hunter.gabbard/public_html/CBC/cINNamon/gausian_results/"
n_neurons = 0
do_contours = True # if True, plot contours of predictions by INN
plot_cadence = 50
do_latent_struc = False # if True, plot latent space 2D structure
conv_nn = False # if True, use convolutional nn structure
# setup output directory - if it does not exist
os.system('mkdir -p %s' % out_dir)
# generate data
pos, labels, x, sig = data.generate(
model=sig_model,
tot_dataset_size=int(1e6), # 1e6
ndata=ndata,
sigma=sigma,
prior_bound=bound,
seed=seed)
if do_latent_struc:
# calculate mode of x-space for both pars
mode_1 = stats.mode(np.array(pos[:, 0]))
mode_2 = stats.mode(np.array(pos[:, 1]))
# seperate the test data for plotting
pos_test = pos[-test_split:]
labels_test = labels[-test_split:]
sig_test = sig[-test_split:]
# plot the test data examples
plt.figure(figsize=(6, 6))
fig_post, axes = plt.subplots(r, r, figsize=(6, 6))
cnt = 0
for i in range(r):
for j in range(r):
axes[i, j].plot(x, np.array(labels_test[cnt, :]), '.')
axes[i, j].plot(x, np.array(sig_test[cnt, :]), '-')
cnt += 1
axes[i, j].axis([0, 1, -1.5, 1.5])
plt.savefig("%stest_distribution.png" % out_dir, dpi=360)
plt.close()
# setting up the model
ndim_x = 2 # number of posterior parameter dimensions (x,y)
ndim_y = ndata # number of label dimensions (noisy data samples)
ndim_z = 200 # number of latent space dimensions?
ndim_tot = max(
ndim_x,
ndim_y + ndim_z) + n_neurons # must be > ndim_x and > ndim_y + ndim_z
# define different parts of the network
# define input node
inp = InputNode(ndim_tot, name='input')
# define hidden layer nodes
filtsize = 3
dropout = 0.0
clamp = 1.0
if conv_nn == True:
t1 = Node(
[inp.out0], rev_multiplicative_layer, {
'F_class': F_conv,
'clamp': clamp,
'F_args': {
'kernel_size': filtsize,
'leaky_slope': 0.1,
'batch_norm': False
}
})
t2 = Node(
[t1.out0], rev_multiplicative_layer, {
'F_class': F_conv,
'clamp': clamp,
'F_args': {
'kernel_size': filtsize,
'leaky_slope': 0.1,
'batch_norm': False
}
})
t3 = Node(
[t2.out0], rev_multiplicative_layer, {
'F_class': F_conv,
'clamp': clamp,
'F_args': {
'kernel_size': filtsize,
'leaky_slope': 0.1,
'batch_norm': False
}
})
#t4 = Node([t1.out0], rev_multiplicative_layer,
# {'F_class': F_conv, 'clamp': 2.0,
# 'F_args':{'kernel_size': filtsize,'leaky_slope':0.1,
# 'batch_norm':False}})
#t5 = Node([t2.out0], rev_multiplicative_layer,
# {'F_class': F_conv, 'clamp': 2.0,
# 'F_args':{'kernel_size': filtsize,'leaky_slope':0.1,
# 'batch_norm':False}})
else:
t1 = Node(
[inp.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': clamp,
'F_args': {
'dropout': dropout
}
})
t2 = Node(
[t1.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': clamp,
'F_args': {
'dropout': dropout
}
})
t3 = Node(
[t2.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': clamp,
'F_args': {
'dropout': dropout
}
})
# define output layer node
outp = OutputNode([t3.out0], name='output')
nodes = [inp, t1, t2, t3, outp]
model = ReversibleGraphNet(nodes)
# Train model
# Training parameters
n_epochs = 12000
meta_epoch = 12 # what is this???
n_its_per_epoch = 12
lr = 1e-2
gamma = 0.01**(1. / 120)
l2_reg = 2e-5
y_noise_scale = 3e-2
zeros_noise_scale = 3e-2
# relative weighting of losses:
lambd_predict = 4000. #300 forward pass
lambd_latent = 900. #300 laten space
lambd_rev = 1000. #400 backwards pass
# padding both the data and the latent space
# such that they have equal dimension to the parameter space
#pad_x = torch.zeros(batch_size, ndim_tot - ndim_x)
#pad_yz = torch.zeros(batch_size, ndim_tot - ndim_y - ndim_z)
# define optimizer
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
betas=(0.8, 0.8),
eps=1e-04,
weight_decay=l2_reg)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=meta_epoch,
gamma=gamma)
# define the three loss functions
loss_backward = MMD_multiscale
loss_latent = MMD_multiscale
loss_fit = fit
# set up training set data loader
train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(
pos[test_split:], labels[test_split:]),
batch_size=batch_size,
shuffle=True,
drop_last=True)
# initialisation of network weights
for mod_list in model.children():
for block in mod_list.children():
for coeff in block.children():
if conv_nn == True:
coeff.conv3.weight.data = 0.01 * torch.randn(
coeff.conv3.weight.shape)
model.to(device)
# number of test samples to use after training
N_samp = 2500
# precompute true likelihood on the test data
Ngrid = 64
cnt = 0
lik = np.zeros((r, r, Ngrid * Ngrid))
true_post = np.zeros((r, r, N_samp, 2))
lossf_hist = []
lossrev_hist = []
losstot_hist = []
losslatent_hist = []
beta_score_hist = []
for i in range(r):
for j in range(r):
mvec, cvec, temp, post_points = data.get_lik(np.array(
labels_test[cnt, :]).flatten(),
n_grid=Ngrid,
sig_model=sig_model,
sigma=sigma,
xvec=x,
bound=bound)
lik[i, j, :] = temp.flatten()
true_post[i, j, :] = post_points[:N_samp]
cnt += 1
# start training loop
try:
t_start = time()
# loop over number of epochs
for i_epoch in tqdm(range(n_epochs), ascii=True, ncols=80):
scheduler.step()
# Initially, the l2 reg. on x and z can give huge gradients, set
# the lr lower for this
if i_epoch < 0:
print('inside this iepoch<0 thing')
for param_group in optimizer.param_groups:
param_group['lr'] = lr * 1e-2
# train the model
losstot, losslatent, lossrev, lossf, lambd_latent = train(
model, train_loader, n_its_per_epoch, zeros_noise_scale,
batch_size, ndim_tot, ndim_x, ndim_y, ndim_z, y_noise_scale,
optimizer, lambd_predict, loss_fit, lambd_latent, loss_latent,
lambd_rev, loss_backward, conv_nn, i_epoch)
# append current loss value to loss histories
lossf_hist.append(lossf.data.item())
lossrev_hist.append(lossrev.data.item())
losstot_hist.append(losstot)
losslatent_hist.append(losslatent.data.item())
pe_losses = [
losstot_hist, losslatent_hist, lossrev_hist, lossf_hist
]
# loop over a few cases and plot results in a grid
cnt = 0
beta_max = 0
if ((i_epoch % plot_cadence == 0) & (i_epoch > 0)):
# use the network to predict parameters\
if do_latent_struc:
# do latent space structure plotting
y_samps_latent = np.tile(np.array(labels_test[0, :]),
1).reshape(1, ndim_y)
y_samps_latent = torch.tensor(y_samps_latent,
dtype=torch.float)
x1_i_dist = []
x2_i_dist = []
x1_i_par = np.array([])
x2_i_par = np.array([])
# define latent space mesh grid
z_mesh = np.mgrid[-0.99:-0.01:100j, -0.99:-0.01:100j]
z_mesh = np.vstack([z_mesh, np.zeros((2, 100, 100))])
#for z_i in range(10000):
for i in range(z_mesh.shape[1]):
for j in range(z_mesh.shape[2]):
a = torch.randn(1, ndim_z)
a[0, 0] = z_mesh[0, i, j]
a[0, 1] = z_mesh[1, i, j]
x_i = model(torch.cat([
a,
torch.zeros(1, ndim_tot - ndim_y - ndim_z),
y_samps_latent
],
dim=1).to(device),
rev=True)
x_i = x_i.cpu().data.numpy()
# calculate hue and intensity
if np.abs(mode_1[0][0] -
x_i[0][0]) < np.abs(mode_2[0][0] -
x_i[0][1]):
z_mesh[2, i,
j] = np.abs(mode_1[0][0] - x_i[0][0])
z_mesh[3, i, j] = 0
else:
z_mesh[2, i,
j] = np.abs(mode_2[0][0] - x_i[0][1])
z_mesh[3, i, j] = 1
z_mesh[2, :, :][z_mesh[3, :, :] == 0] = z_mesh[2, :, :][
z_mesh[3, :, :] == 0] / np.max(
z_mesh[2, :, :][z_mesh[3, :, :] == 0])
z_mesh[2, :, :][z_mesh[3, :, :] == 1] = z_mesh[2, :, :][
z_mesh[3, :, :] == 1] / np.max(
z_mesh[2, :, :][z_mesh[3, :, :] == 1])
bg_color = 'black'
fg_color = 'red'
fig = plt.figure(facecolor=bg_color, edgecolor=fg_color)
axes = fig.add_subplot(111)
axes.patch.set_facecolor(bg_color)
axes.xaxis.set_tick_params(color=fg_color,
labelcolor=fg_color)
axes.yaxis.set_tick_params(color=fg_color,
labelcolor=fg_color)
for spine in axes.spines.values():
spine.set_color(fg_color)
plt.scatter(z_mesh[0, :, :][z_mesh[3, :, :] == 0],
z_mesh[1, :, :][z_mesh[3, :, :] == 0],
s=1,
c=z_mesh[2, :, :][z_mesh[3, :, :] == 0],
cmap='Greens',
axes=axes)
plt.scatter(z_mesh[0, :, :][z_mesh[3, :, :] == 1],
z_mesh[1, :, :][z_mesh[3, :, :] == 1],
s=1,
c=z_mesh[2, :, :][z_mesh[3, :, :] == 1],
cmap='Purples',
axes=axes)
plt.xlabel('z-space', color=fg_color)
plt.ylabel('z-space', color=fg_color)
plt.savefig('%sstruct_z.png' % out_dir, dpi=360)
plt.close()
# end of latent space structure plotting
# initialize plot for showing testing results
fig, axes = plt.subplots(r, r, figsize=(6, 6))
for i in range(r):
for j in range(r):
# convert data into correct format
y_samps = np.tile(np.array(labels_test[cnt, :]),
N_samp).reshape(N_samp, ndim_y)
y_samps = torch.tensor(y_samps, dtype=torch.float)
#y_samps += y_noise_scale * torch.randn(N_samp, ndim_y)
y_samps = torch.cat(
[
torch.randn(N_samp,
ndim_z), #zeros_noise_scale *
torch.zeros(N_samp,
ndim_tot - ndim_y - ndim_z),
y_samps
],
dim=1)
y_samps = y_samps.to(device)
if conv_nn == True:
y_samps = y_samps.reshape(y_samps.shape[0],
y_samps.shape[1], 1, 1)
rev_x = model(y_samps, rev=True)
rev_x = rev_x.cpu().data.numpy()
if conv_nn == True:
rev_x = rev_x.reshape(rev_x.shape[0],
rev_x.shape[1])
# plot the samples and the true contours
axes[i, j].clear()
axes[i, j].contour(mvec,
cvec,
lik[i, j, :].reshape(Ngrid, Ngrid),
levels=[0.68, 0.9, 0.99])
axes[i, j].scatter(rev_x[:, 0],
rev_x[:, 1],
s=0.5,
alpha=0.5,
color='red')
axes[i, j].scatter(true_post[i, j, :, 1],
true_post[i, j, :, 0],
s=0.5,
alpha=0.5,
color='blue')
axes[i, j].plot(pos_test[cnt, 0],
pos_test[cnt, 1],
'+r',
markersize=8)
axes[i, j].axis(bound)
# add contours to results
try:
if do_contours:
contour_y = np.reshape(rev_x[:, 1],
(rev_x[:, 1].shape[0]))
contour_x = np.reshape(rev_x[:, 0],
(rev_x[:, 0].shape[0]))
contour_dataset = np.array(
[contour_x, contour_y])
kernel_cnn = make_contour_plot(
axes[i, j],
contour_x,
contour_y,
contour_dataset,
'red',
flip=False,
kernel_cnn=False)
# run overlap tests on results
contour_x = np.reshape(
true_post[i, j][:, 1],
(true_post[i, j][:, 1].shape[0]))
contour_y = np.reshape(
true_post[i, j][:, 0],
(true_post[i, j][:, 0].shape[0]))
contour_dataset = np.array(
[contour_x, contour_y])
ks_score, ad_score, beta_score = overlap_tests(
rev_x, true_post[i, j], pos_test[cnt],
kernel_cnn, gaussian_kde(contour_dataset))
axes[i, j].legend([
'Overlap: %s' %
str(np.round(beta_score, 3))
])
beta_score_hist.append([beta_score])
except ValueError as e:
pass
cnt += 1
# sve the results to file
fig_post.canvas.draw()
plt.savefig('%sposteriors_%s.png' % (out_dir, i_epoch),
dpi=360)
plt.savefig('%slatest.png' % out_dir, dpi=360)
plot_losses(pe_losses,
'%spe_losses.png' % out_dir,
legend=['PE-GEN'])
plot_losses(pe_losses,
'%spe_losses_logscale.png' % out_dir,
logscale=True,
legend=['PE-GEN'])
except KeyboardInterrupt:
pass
finally:
print("\n\nTraining took {(time()-t_start)/60:.2f} minutes\n")
def main():
# Set up data
test_split = 1 # number of testing samples to use
# load in gw templates and signals
signal_train_images, signal_train_pars, signal_image, noise_signal, signal_pars = load_gw_data()
if add_noise_real:
train_array = []
train_pe_array = []
for i in range(len(signal_train_images)):
for j in range(n_real):
train_array.append([signal_train_images[i] + np.random.normal(loc=0.0, scale=n_sig) / 817.98 * 1079.23])
train_pe_array.append([signal_train_pars[i]])
train_array = np.array(train_array)
train_pe_array = np.array(train_pe_array)
train_array = train_array.reshape(train_array.shape[0],train_array.shape[2])
train_pe_array = train_pe_array.reshape(train_pe_array.shape[0],train_pe_array.shape[2])
else:
for i in range(len(signal_train_images)):
signal_train_images[i] += np.random.normal(loc=0.0, scale=n_sig) / 817.98 * 1079.23
# load in lalinference noise signal
noise_signal = h5py.File("gw_data/data/%s0%s.hdf5" % (event_name,tag),"r")
noise_signal = np.reshape(noise_signal['wht_wvf'][:] * 1079.23,(n_pix,1)) # 817.98 need to not have this hardcoded
#noise_signal *= 1079.23 / 817.98
#noise_signal = noise_signal.reshape(noise_signal.shape[0],1)
plt.plot(noise_signal)
plt.savefig('%s/test.png' % out_path)
plt.close()
# load in lalinference samples
with open('gw_data/data/gw150914_mc_q_lalinf_post_srate-1024_python3.sav','rb' ) as f:
lalinf_post = pickle.load(f)
lalinf_mc = lalinf_post[0]
lalinf_q = lalinf_post[1]
kernel_lalinf = gaussian_kde(lalinf_post)
# declare gw variants of positions and labels
mc_max = np.max(signal_train_pars[:,0])
#signal_train_pars /= mc_max
labels = torch.tensor(signal_train_images, dtype=torch.float)
pos = torch.tensor(signal_train_pars, dtype=torch.float)
# setting up the model
ndim_x = 2 # number of parameter dimensions
ndim_y = n_pix # number of data dimensions
ndim_z = 100 # number of latent space dimensions?
ndim_tot = n_pix+ndim_z+ndim_x+n_neurons # two times the number data dimensions?
# define different parts of the network
# define input node
inp = InputNode(ndim_tot, name='input')
# define hidden layer nodes
# number of nodes equal to number of parameters?
t1 = Node([inp.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}, 'F_args': {'batch_norm': False}})
t2 = Node([t1.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}, 'F_args': {'batch_norm': False}})
"""
t3 = Node([t2.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}, 'F_args': {'batch_norm': False}})
t4 = Node([t3.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}})
t5 = Node([t4.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}})
t6 = Node([t5.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}})
t7 = Node([t6.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}})
t8 = Node([t7.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}})
t9 = Node([t8.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}})
t10 = Node([t9.out0], rev_multiplicative_layer,
{'F_class': F_fully_connected, 'clamp': 2.0,
'F_args': {'dropout': 0.0}})
"""
# define output layer node
outp = OutputNode([t2.out0], name='output')
nodes = [inp, t1, t2, outp]
model = ReversibleGraphNet(nodes)
# Train model
lr = 1e-4
gamma = 0.01**(1./120)
l2_reg = 2e-5
y_noise_scale = 1 # amount of noise to add to y parameter?
zeros_noise_scale = 3e-2 # what is this??
# relative weighting of losses:
lambd_predict = 300. # forward pass
lambd_latent = 300. # laten space
lambd_rev = 400. # backwards pass
# padding both the data and the latent space
# such that they have equal dimension to the parameter space
pad_x = torch.zeros(batch_size, ndim_tot - ndim_x)
pad_yz = torch.zeros(batch_size, ndim_tot - ndim_y - ndim_z)
# define optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=lr, betas=(0.8, 0.8),
eps=1e-04, weight_decay=l2_reg, amsgrad=True)
#optimizer = torch.optim.SGD(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=meta_epoch,
gamma=gamma)
# define the three loss functions
loss_backward = MMD_multiscale
loss_latent = MMD_multiscale
loss_fit = fit
# set up test set data loader
test_loader = torch.utils.data.DataLoader(
torch.utils.data.TensorDataset(pos[:test_split], labels[:test_split]),
batch_size=batch_size, shuffle=True, drop_last=True)
# set up training set data loader
train_loader = torch.utils.data.DataLoader(
torch.utils.data.TensorDataset(pos[:], labels[:]),
batch_size=batch_size, shuffle=True, drop_last=True)
# what is happening here? More set up of network?
for mod_list in model.children():
for block in mod_list.children():
for coeff in block.children():
coeff.fc3.weight.data = 0.01*torch.randn(coeff.fc3.weight.shape)
model.to(device)
# number of test samples to use after training
N_samp = 4000
# choose test samples to use after training
# 1000 iterations of test signal burried in noise. Only need to change z parameter.
#x_samps = torch.cat([x for x,y in test_loader], dim=0)[:N_samp]
#y_samps = torch.cat([y for x,y in test_loader], dim=0)[:N_samp]
#y_samps += torch.randn(N_samp, ndim_y) #* y_noise_scale
y_samps = y_noise_scale * np.transpose(torch.tensor(np.repeat(noise_signal, N_samp, axis=1), dtype=torch.float))
# make test samples. First element is the latent space dimension
# second element is the extra zeros needed to pad the input.
# the third element is the time series
y_samps = torch.cat([torch.randn(N_samp, ndim_z),
zeros_noise_scale * torch.zeros(N_samp, ndim_tot - ndim_y - ndim_z), # zeros_noise_scale *
y_samps], dim=1)
# what we should have now are 1000 copies of the event burried in noise with zero padding up to 2048
y_samps = y_samps.to(device)
# start training loop
lossf_hist = []
lossrev_hist = []
beta_score_hist = []
kernel_cnn = False
try:
# print('#Epoch \tIt/s \tl_total')
t_start = time()
# loop over number of epochs
for i_epoch in tqdm(range(n_epochs), ascii=True, ncols=80):
scheduler.step()
# Initially, the l2 reg. on x and z can give huge gradients, set
# the lr lower for this
if i_epoch < 0:
for param_group in optimizer.param_groups:
param_group['lr'] = lr * 1e-2
#print(i_epoch, end='\t ')
_,lossf, lossrev = train(model,train_loader,n_its_per_epoch,zeros_noise_scale,batch_size,ndim_tot,ndim_x,ndim_y,ndim_z,y_noise_scale,optimizer,lambd_predict,loss_fit,lambd_latent,loss_latent,lambd_rev,loss_backward,i_epoch)
# append current loss value to loss histories
lossf_hist.append(lossf.item())
lossrev_hist.append(lossrev.item())
pe_losses = [lossf_hist,lossrev_hist]
# predict parameters of signal
rev_x = model(y_samps, rev=True)
rev_x = rev_x.cpu().data.numpy()
#rev_x[:,0] = mc_max * rev_x[:,0]
# plot pe results and loss
beta_max = 0
"""
if i_epoch>0:
kernel_cnn = gaussian_kde(rev_x)
#overlap_y = np.reshape(rev_x[:,1], (rev_x[:,1].shape[0]))
#overlap_x = np.reshape(rev_x[:,0], (rev_x[:,0].shape[0]))
#overlap_dataset = np.array([overlap_x,overlap_y]).transpose()
ks_score, ad_score, beta_score = overlap_tests(rev_x,lalinf_post,signal_pars,kernel_cnn,kernel_lalinf)
beta_score_hist.append([beta_score])
plt.plot(np.linspace(1,i_epoch,len(beta_score_hist)),beta_score_hist)
plt.savefig('%s/latest/beta_hist.png' % out_path)
plt.close()
"""
if ((i_epoch % plot_cadence == 0) & (i_epoch>0)):
pe_std = [0.02185649964844209, 0.005701401364171313] # this will need to be removed
beta_score_hist.append([plot_pe_samples(rev_x,signal_pars,out_path,i_epoch,lalinf_post,pe_std,kernel_lalinf=kernel_lalinf,kernel_cnn=kernel_cnn)])
plt.plot(np.linspace(plot_cadence,i_epoch,len(beta_score_hist)),beta_score_hist)
plt.savefig('%s/latest/beta_hist.png' % out_path)
plt.close()
# plot loss curves - non-log and log
plot_losses(pe_losses,'%s/latest/pe_losses.png' % out_path,legend=['PE-GEN'])
plot_losses(pe_losses,'%s/latest/pe_losses_logscale.png' % out_path,logscale=True,legend=['PE-GEN'])
# save model
#if beta_score_hist[:-1] > beta_max: beta_max = beta_score_hist[:-1]
#if beta_score_hist[:-1] > beta_max or i_epoch==plot_cadence: model.save_state_dict('mytraining.pt')
# make PE scatter plots with contours and beta score
#plt.scatter(rev_x[:,0], rev_x[:,1], s=1., c='red')
#plt.scatter(lalinf_mc, lalinf_q, s=1., c='blue')
except KeyboardInterrupt:
pass
finally:
print("\n\nTraining took {(time()-t_start)/60:.2f} minutes\n")
def main():
# Set up simulation parameters
batch_size = 1600 # set batch size
r = 3 # the grid dimension for the output tests
test_split = r * r # number of testing samples to use
sig_model = 'sg' # the signal model to use
sigma = 0.2 # the noise std
ndata = 32 # number of data samples
bound = [0.0, 1.0, 0.0, 1.0] # effective bound for likelihood
seed = 1 # seed for generating data
# generate data
pos, labels, x, sig = data.generate(model=sig_model,
tot_dataset_size=2**20,
ndata=ndata,
sigma=sigma,
prior_bound=bound,
seed=seed)
# seperate the test data for plotting
pos_test = pos[-test_split:]
labels_test = labels[-test_split:]
sig_test = sig[-test_split:]
# plot the test data examples
plt.figure(figsize=(6, 6))
fig, axes = plt.subplots(r, r, figsize=(6, 6))
cnt = 0
for i in range(r):
for j in range(r):
axes[i, j].plot(x, np.array(labels_test[cnt, :]), '.')
axes[i, j].plot(x, np.array(sig_test[cnt, :]), '-')
cnt += 1
axes[i, j].axis([0, 1, -1.5, 1.5])
plt.savefig('/data/public_html/chrism/FrEIA/test_distribution.png',
dpi=360)
plt.close()
# setting up the model
ndim_x = 2 # number of posterior parameter dimensions (x,y)
ndim_y = ndata # number of label dimensions (noisy data samples)
ndim_z = 8 # number of latent space dimensions?
ndim_tot = max(ndim_x,
ndim_y + ndim_z) # must be > ndim_x and > ndim_y + ndim_z
# define different parts of the network
# define input node
inp = InputNode(ndim_tot, name='input')
# define hidden layer nodes
t1 = Node([inp.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.2
}
})
t2 = Node([t1.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.2
}
})
t3 = Node([t2.out0], rev_multiplicative_layer, {
'F_class': F_fully_connected,
'clamp': 2.0,
'F_args': {
'dropout': 0.2
}
})
# define output layer node
outp = OutputNode([t3.out0], name='output')
nodes = [inp, t1, t2, t3, outp]
model = ReversibleGraphNet(nodes)
# Train model
# Training parameters
n_epochs = 1000
meta_epoch = 12 # what is this???
n_its_per_epoch = 12
batch_size = 1600
lr = 1e-2
gamma = 0.01**(1. / 120)
l2_reg = 2e-5
y_noise_scale = 3e-2
zeros_noise_scale = 3e-2
# relative weighting of losses:
lambd_predict = 300. # forward pass
lambd_latent = 300. # laten space
lambd_rev = 400. # backwards pass
# padding both the data and the latent space
# such that they have equal dimension to the parameter space
#pad_x = torch.zeros(batch_size, ndim_tot - ndim_x)
#pad_yz = torch.zeros(batch_size, ndim_tot - ndim_y - ndim_z)
# define optimizer
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
betas=(0.8, 0.8),
eps=1e-04,
weight_decay=l2_reg)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=meta_epoch,
gamma=gamma)
# define the three loss functions
loss_backward = MMD_multiscale
loss_latent = MMD_multiscale
loss_fit = fit
# set up training set data loader
train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(
pos[test_split:], labels[test_split:]),
batch_size=batch_size,
shuffle=True,
drop_last=True)
# initialisation of network weights
for mod_list in model.children():
for block in mod_list.children():
for coeff in block.children():
coeff.fc3.weight.data = 0.01 * torch.randn(
coeff.fc3.weight.shape)
model.to(device)
# initialize plot for showing testing results
fig, axes = plt.subplots(r, r, figsize=(6, 6))
# number of test samples to use after training
N_samp = 256
# precompute true likelihood on the test data
Ngrid = 64
cnt = 0
lik = np.zeros((r, r, Ngrid * Ngrid))
for i in range(r):
for j in range(r):
mvec, cvec, temp = data.get_lik(np.array(
labels_test[cnt, :]).flatten(),
n_grid=Ngrid,
sig_model=sig_model,
sigma=sigma,
xvec=x,
bound=bound)
lik[i, j, :] = temp.flatten()
cnt += 1
# start training loop
try:
t_start = time()
# loop over number of epochs
for i_epoch in tqdm(range(n_epochs), ascii=True, ncols=80):
scheduler.step()
# Initially, the l2 reg. on x and z can give huge gradients, set
# the lr lower for this
if i_epoch < 0:
print('inside this iepoch<0 thing')
for param_group in optimizer.param_groups:
param_group['lr'] = lr * 1e-2
# train the model
train(model, train_loader, n_its_per_epoch, zeros_noise_scale,
batch_size, ndim_tot, ndim_x, ndim_y, ndim_z, y_noise_scale,
optimizer, lambd_predict, loss_fit, lambd_latent,
loss_latent, lambd_rev, loss_backward, i_epoch)
# loop over a few cases and plot results in a grid
cnt = 0
for i in range(r):
for j in range(r):
# convert data into correct format
y_samps = np.tile(np.array(labels_test[cnt, :]),
N_samp).reshape(N_samp, ndim_y)
y_samps = torch.tensor(y_samps, dtype=torch.float)
#y_samps += y_noise_scale * torch.randn(N_samp, ndim_y)
y_samps = torch.cat(
[
torch.randn(N_samp, ndim_z), #zeros_noise_scale *
torch.zeros(N_samp, ndim_tot - ndim_y - ndim_z),
y_samps
],
dim=1)
y_samps = y_samps.to(device)
# use the network to predict parameters
rev_x = model(y_samps, rev=True)
rev_x = rev_x.cpu().data.numpy()
# plot the samples and the true contours
axes[i, j].clear()
axes[i, j].contour(mvec,
cvec,
lik[i, j, :].reshape(Ngrid, Ngrid),
levels=[0.68, 0.9, 0.99])
axes[i, j].scatter(rev_x[:, 0],
rev_x[:, 1],
s=0.5,
alpha=0.5)
axes[i, j].plot(pos_test[cnt, 0],
pos_test[cnt, 1],
'+r',
markersize=8)
axes[i, j].axis(bound)
cnt += 1
# sve the results to file
fig.canvas.draw()
plt.savefig('/data/public_html/chrism/FrEIA/posteriors_%s.png' %
i_epoch,
dpi=360)
plt.savefig('/data/public_html/chrism/FrEIA/latest.png', dpi=360)
except KeyboardInterrupt:
pass
finally:
print("\n\nTraining took {(time()-t_start)/60:.2f} minutes\n")