def __init__(self, args):
super(DEEP_CNN_MUI, self).__init__()
self.args = args
V = args.embed_num
V_mui = args.embed_num_mui
D = args.embed_dim
C = args.class_num
Ci = 2
Co = args.kernel_num
Ks = args.kernel_sizes
if args.max_norm is not None:
print("max_norm = {} ".format(args.max_norm))
self.embed_no_static = nn.Embedding(V, D, max_norm=args.max_norm, scale_grad_by_freq=True)
self.embed_static = nn.Embedding(V_mui, D, max_norm=args.max_norm, scale_grad_by_freq=True)
else:
print("max_norm = {} ".format(args.max_norm))
self.embed_no_static = nn.Embedding(V, D, scale_grad_by_freq=True)
self.embed_static = nn.Embedding(V_mui, D, scale_grad_by_freq=True)
if args.word_Embedding:
pretrained_weight = np.array(args.pretrained_weight)
self.embed_no_static.weight.data.copy_(torch.from_numpy(pretrained_weight))
pretrained_weight_static = np.array(args.pretrained_weight_static)
self.embed_static.weight.data.copy_(torch.from_numpy(pretrained_weight_static))
# whether to fixed the word embedding
self.embed_no_static.weight.requires_grad = True
# cons layer
self.convs1 = [nn.Conv2d(Ci, D, (K, D), stride=1, padding=(K//2, 0), bias=True) for K in Ks]
self.convs2 = [nn.Conv2d(1, Co, (K, D), stride=1, padding=(K//2, 0), bias=True) for K in Ks]
print(self.convs1)
print(self.convs2)
if args.init_weight:
print("Initing W .......")
for (conv1, conv2) in zip(self.convs1, self.convs2):
init.xavier_normal(conv1.weight.data, gain=np.sqrt(args.init_weight_value))
init.uniform(conv1.bias, 0, 0)
init.xavier_normal(conv2.weight.data, gain=np.sqrt(args.init_weight_value))
init.uniform(conv2.bias, 0, 0)
# dropout
self.dropout = nn.Dropout(args.dropout)
# linear
in_fea = len(Ks) * Co
self.fc1 = nn.Linear(in_features=in_fea, out_features=in_fea // 2, bias=True)
self.fc2 = nn.Linear(in_features=in_fea // 2, out_features=C, bias=True)
def fwd_split(self, input, batch, depth,
random_split=False, mode='train', epoch=0):
length = self.split.n
var = 0.0
# Iterate over scales
e = Variable(torch.zeros(self.batch_size, length)).type(dtype)
mask = (input[:, :, 0] >= 0).type(dtype).squeeze()
Phis, Bs, Inputs_N, Samples = ([] for ii in range(4))
for scale in range(depth):
logits, probs, input_n, Phi = self.split(e, input,
mask, scale=scale)
# Sample from probabilities and update embeddings
if random_split:
rand = (Variable(torch.zeros(self.batch_size, length))
.type(dtype))
init.uniform(rand)
sample = (rand > 0.5).type(dtype)
else:
rand = (Variable(torch.zeros(self.batch_size, length))
.type(dtype))
init.uniform(rand)
sample = (probs > rand).type(dtype)
e = 2 * e + sample
# Appends
Samples.append(sample)
Phis.append(Phi)
Bs.append(probs)
Inputs_N.append(input_n)
# variance of bernouilli probabilities
var += self.compute_variance(probs, mask)
# computes log probabilities of binary actions for the policy gradient
Log_Probs = self.log_probabilities(Bs, Samples, mask, depth)
# pad embeddings with infinity to not affect embeddings argsort
infty = 1e6
e = e * mask + (1 - mask) * infty
return var, Phis, Bs, Inputs_N, e, Log_Probs
def __init__(self, batchNorm=True, div_flow=20):
super(FlowNetCImg, self).__init__()
self.batchNorm = batchNorm
self.div_flow = div_flow
self.conv1 = conv(self.batchNorm, 3, 64, kernel_size=7, stride=2)
self.conv2 = conv(self.batchNorm, 64, 128, kernel_size=5, stride=2)
self.conv3 = conv(self.batchNorm, 128, 256, kernel_size=5, stride=2)
self.conv_redir = conv(self.batchNorm,
256,
32,
kernel_size=1,
stride=1)
#if args.fp16:
# self.corr = nn.Sequential(
# tofp32(),
# Correlation(pad_size=20, kernel_size=1, max_displacement=20, stride1=1, stride2=2, corr_multiply=1),
# tofp16())
#else:
self.corr = Correlation(pad_size=20,
kernel_size=1,
max_displacement=20,
stride1=1,
stride2=2,
corr_multiply=1)
self.corr_activation = nn.LeakyReLU(0.1, inplace=True)
self.conv3_1 = conv(self.batchNorm, 473, 256)
self.conv4 = conv(self.batchNorm, 256, 512, stride=2)
self.conv4_1 = conv(self.batchNorm, 512, 512)
self.conv5 = conv(self.batchNorm, 512, 512, stride=2)
self.conv5_1 = conv(self.batchNorm, 512, 512)
self.conv6 = conv(self.batchNorm, 512, 1024, stride=2)
self.conv6_1 = conv(self.batchNorm, 1024, 1024)
self.deconv5 = deconv(1024, 512)
self.deconv4 = deconv(1026, 256)
self.deconv3 = deconv(770, 128)
self.deconv2 = deconv(386, 64)
self.predict_flow6 = predict_flow(1024)
self.predict_flow5 = predict_flow(1026)
self.predict_flow4 = predict_flow(770)
self.predict_flow3 = predict_flow(386)
self.predict_flow2 = predict_flow(194)
self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
# init_deconv_bilinear(m.weight)
self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
def __init__(self, with_bn=True, fp16=False):
super(FlowNetC, self).__init__()
self.with_bn = with_bn
self.fp16 = fp16
self.conv1 = conv(3, 64, kernel_size=7, stride=2, with_bn=with_bn)
self.conv2 = conv(64, 128, kernel_size=5, stride=2, with_bn=with_bn)
self.conv3 = conv(128, 256, kernel_size=5, stride=2, with_bn=with_bn)
self.conv_redir = conv(256,
32,
kernel_size=1,
stride=1,
with_bn=with_bn)
corr = Correlation(pad_size=20,
kernel_size=1,
max_displacement=20,
stride1=1,
stride2=2,
corr_multiply=1)
self.corr = nn.Sequential(tofp32(), corr, tofp16()) if fp16 else corr
self.corr_activation = nn.LeakyReLU(0.1, inplace=True)
self.conv3_1 = conv(473, 256, with_bn=with_bn)
self.conv4 = conv(256, 512, stride=2, with_bn=with_bn)
self.conv4_1 = conv(512, 512, with_bn=with_bn)
self.conv5 = conv(512, 512, stride=2, with_bn=with_bn)
self.conv5_1 = conv(512, 512, with_bn=with_bn)
self.conv6 = conv(512, 1024, stride=2, with_bn=with_bn)
self.conv6_1 = conv(1024, 1024, with_bn=with_bn)
self.deconv5 = deconv(1024, 512)
self.deconv4 = deconv(1026, 256)
self.deconv3 = deconv(770, 128)
self.deconv2 = deconv(386, 64)
self.predict_flow6 = predict_flow(1024)
self.predict_flow5 = predict_flow(1026)
self.predict_flow4 = predict_flow(770)
self.predict_flow3 = predict_flow(386)
self.predict_flow2 = predict_flow(194)
self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
nn_init.uniform(m.bias)
nn_init.xavier_uniform(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
nn_init.uniform(m.bias)
nn_init.xavier_uniform(m.weight)
def init_param(self, param):
if len(param.size()) < 2:
init.uniform(param)
else:
init.xavier_uniform(param)
def __init__(self,
input_size,
output_size,
hidden_size,
dtype,
n_layers=1,
batch_size=1,
scale=1.0,
final_layer_flag=0,
policy_flag=0):
super(PMLP, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.hidden_size = hidden_size
self.n_layers = n_layers
self.batch_size = batch_size
self.scale = scale # scale output of actor from [-1,1] to range of action space [-scale,scale]. set to 1 for critic
self.final_layer_flag = final_layer_flag # 1 for actor, 0 for critic (since critic range need not be restricted to [-1,1])
self.dtype = dtype
self.policy_flag = policy_flag
if policy_flag:
self.action_log_std = nn.Parameter(torch.zeros(1, output_size))
#self.control_gru_list = []
self.control_hidden_list = []
self.control_h2o_list = []
#self.gru_00 = nn.GRUCell(self.input_size, self.hidden_size)
self.l_00 = nn.Linear(self.input_size, self.hidden_size).type(dtype)
self.h2o_0 = nn.Linear(self.hidden_size, self.output_size).type(dtype)
#self.gru_10 = nn.GRUCell(self.input_size, self.hidden_size)
self.l_10 = nn.Linear(self.input_size, self.hidden_size).type(dtype)
self.h2o_1 = nn.Linear(self.hidden_size, self.output_size).type(dtype)
#self.gru_20 = nn.GRUCell(self.input_size, self.hidden_size)
self.l_20 = nn.Linear(self.input_size, self.hidden_size).type(dtype)
self.h2o_2 = nn.Linear(self.hidden_size, self.output_size).type(dtype)
#self.gru_30 = nn.GRUCell(self.input_size, self.hidden_size)
self.l_30 = nn.Linear(self.input_size, self.hidden_size).type(dtype)
self.h2o_3 = nn.Linear(self.hidden_size, self.output_size).type(dtype)
init_fanin(self.l_00.weight)
init_fanin(self.l_10.weight)
init_fanin(self.l_20.weight)
init_fanin(self.l_30.weight)
init.uniform(self.h2o_0.weight, -3e-3, 3e-3)
init.uniform(self.h2o_0.bias, -3e-3, 3e-3)
init.uniform(self.h2o_1.weight, -3e-3, 3e-3)
init.uniform(self.h2o_1.bias, -3e-3, 3e-3)
init.uniform(self.h2o_2.weight, -3e-3, 3e-3)
init.uniform(self.h2o_2.bias, -3e-3, 3e-3)
init.uniform(self.h2o_3.weight, -3e-3, 3e-3)
init.uniform(self.h2o_3.bias, -3e-3, 3e-3)
if n_layers == 2:
#self.gru_01 = nn.GRUCell(self.hidden_size, self.hidden_size)
#self.gru_11 = nn.GRUCell(self.hidden_size, self.hidden_size)
#self.gru_21 = nn.GRUCell(self.hidden_size, self.hidden_size)
#self.gru_31 = nn.GRUCell(self.hidden_size, self.hidden_size)
self.l_01 = nn.Linear(self.hidden_size,
self.hidden_size).type(dtype)
self.l_11 = nn.Linear(self.hidden_size,
self.hidden_size).type(dtype)
self.l_21 = nn.Linear(self.hidden_size,
self.hidden_size).type(dtype)
self.l_31 = nn.Linear(self.hidden_size,
self.hidden_size).type(dtype)
init_fanin(self.l_01.weight)
init_fanin(self.l_11.weight)
init_fanin(self.l_21.weight)
init_fanin(self.l_31.weight)
self.control_hidden_list.append(
[self.l_00, self.l_10, self.l_20, self.l_30])
if n_layers == 2:
self.control_hidden_list.append(
[self.l_01, self.l_11, self.l_21, self.l_31])
self.control_h2o_list = [
self.h2o_0, self.h2o_1, self.h2o_2, self.h2o_3
]
#self.alpha = []
#for i in range(4):
# self.alpha.append(Alpha(n_layers))
#self.init_controls(self.control_hidden_list, self.control_h2o_list, self.alpha)
#self.h_0 = Variable(torch.zeros(batch_size, hidden_size), requires_grad=True)
#if n_layers == 2:
# self.h_1 = Variable(torch.zeros(batch_size, hidden_size), requires_grad=True)
self.hidden_list = []
self.h2o_list = []
self.phase_list = []
# to initialize grad of control hidden and h2o ... I need to do this stupid thing ...
dummy_x = Variable(torch.zeros(batch_size, input_size),
requires_grad=False).type(dtype)
dummy_y = Variable(torch.zeros(batch_size, output_size),
requires_grad=False).type(dtype)
dummy_criterion = nn.MSELoss()
if n_layers == 1:
for l, h2o in zip(self.control_hidden_list[0],
self.control_h2o_list):
dummy_h = F.relu(l(dummy_x))
dummy_o = h2o(dummy_h)
dummy_loss = dummy_criterion(dummy_o, dummy_y)
dummy_loss.backward()
if n_layers == 2:
for l0, l1, h2o in zip(self.control_hidden_list[0],
self.control_hidden_list[1],
self.control_h2o_list):
dummy_h0 = F.relu(l0(dummy_x))
dummy_h1 = l1(dummy_h0)
dummy_o = h2o(dummy_h1)
dummy_loss = dummy_criterion(dummy_o, dummy_y)
dummy_loss.backward()
def init_weights(self):
initrange = 0.01
init.uniform(self.embeddings_pri.weight, -1 * initrange, initrange)
init.uniform(self.embeddings_sec.weight, -1 * initrange, initrange)
import matplotlib import matplotlib.pyplot as plt from NoiseNet import NoiseNet from learn.load import load_noise as load from learn.train import train from torch.nn.init import uniform train_loader = load() # Initialize Model model = NoiseNet() uniform(model.fc1.weight.data, a=0.005, b=0.015) criterion = nn.MSELoss() # setup optimization routine learning_rate = 1e-4 optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9) # Training Function log_interval = 2048 # Actual Loop nepochs = 80000 data = next(enumerate(train_loader))[1]
def init_embeddings(embeddings):
init.uniform(embeddings.weight, -0.05, 0.05)
def __init__(self, args):
super(DEEP_CNN_MUI, self).__init__()
self.args = args
V = args.embed_num
V_mui = args.embed_num_mui
D = args.embed_dim
C = args.class_num
Ci = 2
Co = args.kernel_num
Ks = args.kernel_sizes
if args.max_norm is not None:
print("max_norm = {} ".format(args.max_norm))
self.embed_no_static = nn.Embedding(V,
D,
max_norm=args.max_norm,
scale_grad_by_freq=True)
self.embed_static = nn.Embedding(V_mui,
D,
max_norm=args.max_norm,
scale_grad_by_freq=True)
else:
print("max_norm = {} ".format(args.max_norm))
self.embed_no_static = nn.Embedding(V, D, scale_grad_by_freq=True)
self.embed_static = nn.Embedding(V_mui, D, scale_grad_by_freq=True)
if args.word_Embedding:
pretrained_weight = np.array(args.pretrained_weight)
self.embed_no_static.weight.data.copy_(
torch.from_numpy(pretrained_weight))
pretrained_weight_static = np.array(args.pretrained_weight_static)
self.embed_static.weight.data.copy_(
torch.from_numpy(pretrained_weight_static))
# whether to fixed the word embedding
self.embed_no_static.weight.requires_grad = True
# cons layer
self.convs1 = [
nn.Conv2d(Ci, D, (K, D), stride=1, padding=(K // 2, 0), bias=True)
for K in Ks
]
self.convs2 = [
nn.Conv2d(1, Co, (K, D), stride=1, padding=(K // 2, 0), bias=True)
for K in Ks
]
print(self.convs1)
print(self.convs2)
if args.init_weight:
print("Initing W .......")
for (conv1, conv2) in zip(self.convs1, self.convs2):
init.xavier_normal(conv1.weight.data,
gain=np.sqrt(args.init_weight_value))
init.uniform(conv1.bias, 0, 0)
init.xavier_normal(conv2.weight.data,
gain=np.sqrt(args.init_weight_value))
init.uniform(conv2.bias, 0, 0)
# dropout
self.dropout = nn.Dropout(args.dropout)
# linear
in_fea = len(Ks) * Co
self.fc1 = nn.Linear(in_features=in_fea,
out_features=in_fea // 2,
bias=True)
self.fc2 = nn.Linear(in_features=in_fea // 2,
out_features=C,
bias=True)
def reset_parameters(self):
self.mlp.reset_parameters()
# Initialize the last softmax layer with
last_linear = self.mlp.get_linear_layer(self.num_layers)
init.uniform(last_linear.weight.data, -0.005, 0.005)
def XavierFill(tensor):
"""Caffe2 XavierFill Implementation"""
size = reduce(operator.mul, tensor.shape, 1)
fan_in = size / tensor.shape[0]
scale = math.sqrt(3 / fan_in)
return init.uniform(tensor, -scale, scale)
import numpy as np import torch import torch.nn as nn import torch.optim as optim import torch.nn.init as init from torch.autograd import Variable from visdom import Visdom viz = Visdom() num_data=1000 num_epoch=400 x = init.uniform(torch.Tensor(num_data,1),-10,10) y = init.uniform(torch.Tensor(num_data,1),-10,10) z = x**2 + y**2 x_noise = x + init.normal(torch.FloatTensor(num_data,1),std=0.5) y_noise = y + init.normal(torch.FloatTensor(num_data,1),std=0.5) z_noise = x_noise**2 + y_noise**2 data_noise = torch.cat([x_noise,y_noise,z_noise],1) # visualize data win_1=viz.scatter( X=data_noise, opts=dict( markersize=5, markercolor=np.ndarray(shape=[num_data,3],dtype=float,buffer=[51,153,255]*np.ones(shape=[num_data,3])) ) )
def UniInitializer(param):
uniform(param, -0.005, 0.005)
def init_linear(linear):
init.uniform(linear.weight, -0.05, 0.05)
init.constant(linear.bias, 0.)
def __init__(self, input_channels=12, with_bn=True):
super(FlowNetS, self).__init__()
self.with_bn = with_bn
self.conv1 = conv(input_channels,
64,
kernel_size=7,
stride=2,
with_bn=with_bn)
self.conv2 = conv(64, 128, kernel_size=5, stride=2, with_bn=with_bn)
self.conv3 = conv(128, 256, kernel_size=5, stride=2, with_bn=with_bn)
self.conv3_1 = conv(256, 256, with_bn=with_bn)
self.conv4 = conv(256, 512, stride=2, with_bn=with_bn)
self.conv4_1 = conv(512, 512, with_bn=with_bn)
self.conv5 = conv(512, 512, stride=2, with_bn=with_bn)
self.conv5_1 = conv(512, 512, with_bn=with_bn)
self.conv6 = conv(512, 1024, stride=2, with_bn=with_bn)
self.conv6_1 = conv(1024, 1024, with_bn=with_bn)
self.deconv5 = deconv(1024, 512)
self.deconv4 = deconv(1026, 256)
self.deconv3 = deconv(770, 128)
self.deconv2 = deconv(386, 64)
self.predict_flow6 = predict_flow(1024)
self.predict_flow5 = predict_flow(1026)
self.predict_flow4 = predict_flow(770)
self.predict_flow3 = predict_flow(386)
self.predict_flow2 = predict_flow(194)
self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=False)
self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=False)
self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=False)
self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=False)
self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
nn_init.uniform(m.bias)
nn_init.xavier_uniform(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
nn_init.uniform(m.bias)
nn_init.xavier_uniform(m.weight)
tra_type=args.tra_type, rnn_mode=args.rnn_type, cla_dropout=args.cla_dropout)
if args.load is not None:
# Load network
print("### Loading .. ###")
basedir = './models/'
net.load_state_dict(torch.load(basedir + args.load + '/best', map_location=lambda storage, loc: storage))
else:
# Weight initialization --> accelerate training with Xavier
dict = {} # we can store the weights in this dict for convenience
for name, param in net.named_parameters():
if 'weight' in name: # all weights
weight_init.xavier_uniform(param, gain=1.6)
if args.rnn_type == 'SRU':
print('SRU mode')
weight_init.uniform(param, -0.05, 0.05)
dict[name] = param
if 'bias' in name: # all biases
weight_init.constant(param, 0)
if args.rnn_type == 'LSTM': # only LSTM biases
if ('bias_ih' in name) or ('bias_hh' in name):
no4 = int(len(param) / 4)
no2 = int(len(param) / 2)
weight_init.constant(param, 0)
weight_init.constant(param[no4:no2], 1)
if args.cuda == True:
net = net.cuda()
# optimizer = optim.Adam(net.parameters(), weight_decay=args.weight_decay)
optimizer = optim.Adam(net.parameters())
def __init__(self):
""""Constructor of the class"""
super(ExactMatchChannel, self).__init__()
self.alpha = nn.Parameter(torch.FloatTensor(1))
# Initializing the value of alpha
init.uniform(self.alpha)
def __init__(self, args):
print("Decoder model")
super(Decoder_WordLstm, self).__init__()
self.args = args
# self.lstm = nn.LSTM(input_size=self.args.hidden_size, hidden_size=self.args.rnn_hidden_dim, bias=True)
self.lstmcell = nn.LSTMCell(input_size=self.args.hidden_size,
hidden_size=self.args.rnn_hidden_dim,
bias=True)
init.xavier_uniform(self.lstmcell.weight_ih)
init.xavier_uniform(self.lstmcell.weight_hh)
self.lstmcell.bias_hh.data.uniform_(
-np.sqrt(6 / (self.args.rnn_hidden_dim + 1)),
np.sqrt(6 / (self.args.rnn_hidden_dim + 1)))
self.lstmcell.bias_ih.data.uniform_(
-np.sqrt(6 / (self.args.rnn_hidden_dim + 1)),
np.sqrt(6 / (self.args.rnn_hidden_dim + 1)))
self.pos_embed = nn.Embedding(num_embeddings=self.args.pos_size,
embedding_dim=self.args.pos_dim)
init.uniform(self.pos_embed.weight,
a=-np.sqrt(3 / self.args.pos_dim),
b=np.sqrt(3 / self.args.pos_dim))
self.pos_embed.weight.requires_grad = True
self.linear = nn.Linear(in_features=self.args.rnn_hidden_dim * 2 +
self.args.hidden_size,
out_features=self.args.label_size,
bias=False)
# self.non_linear = nn.Linear(in_features=self.args.rnn_hidden_dim * 2, out_features=self.args.hidden_size,
# bias=True)
self.combine_linear = nn.Linear(
in_features=self.args.rnn_hidden_dim * 2 + self.args.pos_dim,
out_features=self.args.hidden_size,
bias=True)
init.xavier_uniform(self.linear.weight)
# init.xavier_uniform(self.non_linear.weight)
init.xavier_uniform(self.combine_linear.weight)
# self.non_linear.bias.data.uniform_(-np.sqrt(6 / (self.args.hidden_size + 1)),
# np.sqrt(6 / (self.args.hidden_size + 1)))
self.combine_linear.bias.data.uniform_(
-np.sqrt(6 / (self.args.hidden_size + 1)),
np.sqrt(6 / (self.args.hidden_size + 1)))
self.dropout = nn.Dropout(self.args.dropout)
self.softmax = nn.LogSoftmax()
self.bucket = Variable(torch.zeros(1, self.args.label_size)).type(
torch.FloatTensor)
self.bucket_rnn = Variable(torch.zeros(
1, self.args.rnn_hidden_dim)).type(torch.FloatTensor)
if self.args.use_cuda is True:
self.bucket = self.bucket.cuda()
self.bucket_rnn = self.bucket_rnn.cuda()
self.z_bucket = Variable(torch.zeros(1, self.args.hidden_size)).type(
torch.FloatTensor)
self.h_bucket = Variable(torch.zeros(
1, self.args.rnn_hidden_dim)).type(torch.FloatTensor)
self.c_bucket = Variable(torch.zeros(
1, self.args.rnn_hidden_dim)).type(torch.FloatTensor)
if self.args.use_cuda is True:
self.z_bucket = self.z_bucket.cuda()
self.h_bucket = self.h_bucket.cuda()
self.c_bucket = self.c_bucket.cuda()
def init_hidden(self, initrange):
for ww in self.parameters():
init.uniform(ww.data, -1 * initrange, initrange)
weight = next(self.parameters()).data
return autograd.Variable(
weight.new(1, self.hidden_size).zero_().cuda())
def _layer_init(self, layer, x):
init.uniform(layer.weight, a=-x, b=x)
init.constant(layer.bias, 0)
def __init__(self, args):
super(CNN_MUI, self).__init__()
self.args = args
V = args.embed_num
V_mui = args.embed_num_mui
D = args.embed_dim
C = args.class_num
Ci = 2
Co = args.kernel_num
Ks = args.kernel_sizes
if args.max_norm is not None:
print("max_norm = {} ".format(args.max_norm))
self.embed_no_static = nn.Embedding(V, D, max_norm=args.max_norm, scale_grad_by_freq=True)
self.embed_static = nn.Embedding(V_mui, D, max_norm=args.max_norm, scale_grad_by_freq=True)
# self.embed_static = nn.Embedding(V, D, max_norm=args.max_norm, scale_grad_by_freq=True)
else:
print("max_norm = {} ".format(args.max_norm))
self.embed_no_static = nn.Embedding(V, D, scale_grad_by_freq=True)
self.embed_static = nn.Embedding(V_mui, D, scale_grad_by_freq=True)
# self.embed_static = nn.Embedding(V, D, scale_grad_by_freq=True)
if args.word_Embedding:
pretrained_weight = np.array(args.pretrained_weight)
self.embed_no_static.weight.data.copy_(torch.from_numpy(pretrained_weight))
pretrained_weight_static = np.array(args.pretrained_weight_static)
self.embed_static.weight.data.copy_(torch.from_numpy(pretrained_weight_static))
# whether to fixed the word embedding
self.embed_no_static.weight.requires_grad = True
# self.embed_static.weight.requires_grad = False
if args.wide_conv is True:
print("using wide convolution")
self.convs1 = [nn.Conv2d(in_channels=Ci, out_channels=Co, kernel_size=(K, D), stride=(1, 1),
padding=(K//2, 0), bias=True) for K in Ks]
else:
print("using narrow convolution")
self.convs1 = [nn.Conv2d(in_channels=Ci, out_channels=Co, kernel_size=(K, D), bias=True) for K in Ks]
# self.convs1 = [nn.Conv2d(Ci, D, (K, D), stride=1, padding=(K // 2, 0)) for K in Ks]
print(self.convs1)
if args.init_weight:
print("Initing W .......")
for conv in self.convs1:
init.xavier_normal(conv.weight.data, gain=np.sqrt(args.init_weight_value))
init.uniform(conv.bias, 0, 0)
'''
self.conv13 = nn.Conv2d(Ci, Co, (3, D))
self.conv14 = nn.Conv2d(Ci, Co, (4, D))
self.conv15 = nn.Conv2d(Ci, Co, (5, D))
'''
self.dropout = nn.Dropout(args.dropout)
in_fea = len(Ks) * Co
self.fc1 = nn.Linear(in_features=in_fea, out_features=in_fea // 2, bias=True)
self.fc2 = nn.Linear(in_features=in_fea // 2, out_features=C, bias=True)
if args.batch_normalizations is True:
print("using batch_normalizations in the model......")
self.convs1_bn = nn.BatchNorm2d(num_features=Co, momentum=args.bath_norm_momentum,
affine=args.batch_norm_affine)
self.fc1_bn = nn.BatchNorm1d(num_features=in_fea//2, momentum=args.bath_norm_momentum,
affine=args.batch_norm_affine)
self.fc2_bn = nn.BatchNorm1d(num_features=C, momentum=args.bath_norm_momentum,
affine=args.batch_norm_affine)
def init_weights(self):
init.uniform(self.lstm.weight_ih_l0, a = -0.01, b = 0.01)
init.orthogonal(self.lstm.weight_hh_l0)
self.lstm.weight_ih_l0.requires_grad = True
self.lstm.weight_hh_l0.requires_grad = True
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.init as init
from torch.autograd import Variable
from visdom import Visdom
viz = Visdom()
# data generation
num_data = 1000
num_epoch = 1000
noise = init.normal(torch.FloatTensor(num_data, 1), std=0.5)
x = init.uniform(torch.Tensor(num_data, 1), -15, 10)
y = -x**3 - 8 * (x**2) + 7 * x + 3
x_noise = x + noise
y_noise = -x_noise**3 - 8 * (x_noise**2) + 7 * x_noise + 3
input_data = torch.cat([x, y_noise], 1)
win = viz.scatter(
X=input_data,
opts=dict(
xtickmin=-15,
xtickmax=10,
xtickstep=1,
ytickmin=-300,
ytickmax=200,
ytickstep=1,
def __init__(self, config):
super(Encoder, self).__init__()
self.config = config
# random
self.char_embed = nn.Embedding(self.config.embed_char_num,
self.config.embed_char_dim,
sparse=False,
padding_idx=self.config.char_paddingId)
self.char_embed.weight.requires_grad = True
self.bichar_embed = nn.Embedding(
self.config.embed_bichar_num,
self.config.embed_bichar_dim,
sparse=False,
padding_idx=self.config.bichar_paddingId)
self.bichar_embed.weight.requires_grad = True
# fix the word embedding
self.static_char_embed = nn.Embedding(
self.config.static_embed_char_num,
self.config.embed_char_dim,
sparse=False,
padding_idx=self.config.static_char_paddingId)
init.uniform(self.static_char_embed.weight,
a=-np.sqrt(3 / self.config.embed_char_dim),
b=np.sqrt(3 / self.config.embed_char_dim))
self.static_bichar_embed = nn.Embedding(
self.config.static_embed_bichar_num,
self.config.embed_bichar_dim,
sparse=False,
padding_idx=self.config.static_bichar_paddingId)
init.uniform(self.static_bichar_embed.weight,
a=-np.sqrt(3 / self.config.embed_bichar_dim),
b=np.sqrt(3 / self.config.embed_bichar_dim))
# load external word embedding
if config.char_pretrained_embed is True:
self.static_char_embed.weight.data.copy_(
self.config.char_pretrain_embed)
for index in range(self.config.embed_char_dim):
self.static_char_embed.weight.data[
self.config.static_char_paddingId][index] = 0
self.static_char_embed.weight.requires_grad = False
if config.bichar_pretrained_embed is True:
self.static_bichar_embed.weight.data.copy_(
self.config.bichar_pretrain_embed)
for index in range(self.config.embed_bichar_dim):
self.static_bichar_embed.weight.data[
self.config.static_bichar_paddingId][index] = 0
self.static_bichar_embed.weight.requires_grad = False
# LSTMCell
self.lstm_left = nn.LSTMCell(input_size=self.config.rnn_dim,
hidden_size=self.config.rnn_hidden_dim,
bias=True)
self.lstm_right = nn.LSTMCell(input_size=self.config.rnn_dim,
hidden_size=self.config.rnn_hidden_dim,
bias=True)
# init lstm weight and bias
init.xavier_uniform(self.lstm_left.weight_ih)
init.xavier_uniform(self.lstm_left.weight_hh)
init.xavier_uniform(self.lstm_right.weight_ih)
init.xavier_uniform(self.lstm_right.weight_hh)
value = np.sqrt(6 / (self.config.rnn_hidden_dim + 1))
self.lstm_left.bias_hh.data.uniform_(-value, value)
self.lstm_left.bias_ih.data.uniform_(-value, value)
self.lstm_right.bias_hh.data.uniform_(-value, value)
self.lstm_right.bias_ih.data.uniform_(-value, value)
self.dropout = nn.Dropout(self.config.dropout)
self.dropout_embed = nn.Dropout(self.config.dropout_embed)
self.input_dim = (self.config.embed_char_dim +
self.config.embed_bichar_dim) * 2
self.liner = nn.Linear(in_features=self.input_dim,
out_features=self.config.rnn_dim,
bias=True)
# init linear
init.xavier_uniform(self.liner.weight)
init_linear_value = np.sqrt(6 / (self.config.rnn_dim + 1))
self.liner.bias.data.uniform_(-init_linear_value, init_linear_value)
def init_fanin(tensor):
fanin = tensor.size(1)
v = 1.0 / np.sqrt(fanin)
init.uniform(tensor, -v, v)
def __init__(self, args):
print("Decoder model")
super(Decoder_WordLstm, self).__init__()
self.args = args
self.pos_paddingKey = self.args.create_alphabet.pos_PaddingID
print("pos_paddingKey", self.pos_paddingKey)
print("appID", self.args.create_alphabet.appID)
# self.lstm = nn.LSTM(input_size=self.args.hidden_size, hidden_size=self.args.rnn_hidden_dim, bias=True)
self.lstmcell = nn.LSTMCell(input_size=self.args.hidden_size,
hidden_size=self.args.rnn_hidden_dim,
bias=True)
init.xavier_uniform(self.lstmcell.weight_ih)
init.xavier_uniform(self.lstmcell.weight_hh)
self.lstmcell.bias_hh.data.uniform_(
-np.sqrt(6 / (self.args.rnn_hidden_dim + 1)),
np.sqrt(6 / (self.args.rnn_hidden_dim + 1)))
self.lstmcell.bias_ih.data.uniform_(
-np.sqrt(6 / (self.args.rnn_hidden_dim + 1)),
np.sqrt(6 / (self.args.rnn_hidden_dim + 1)))
# self.pos_embed = nn.Embedding(num_embeddings=self.args.pos_size, embedding_dim=self.args.pos_dim,
# padding_idx=self.pos_paddingKey)
self.pos_embed = nn.Embedding(num_embeddings=self.args.pos_size,
embedding_dim=self.args.pos_dim)
init.uniform(self.pos_embed.weight,
a=-np.sqrt(3 / self.args.pos_dim),
b=np.sqrt(3 / self.args.pos_dim))
for i in range(self.args.pos_dim):
self.pos_embed.weight.data[self.pos_paddingKey][i] = 0
self.pos_embed.weight.requires_grad = True
self.linear = nn.Linear(in_features=self.args.rnn_hidden_dim * 2 +
self.args.hidden_size,
out_features=self.args.label_size,
bias=False)
# self.non_linear = nn.Linear(in_features=self.args.rnn_hidden_dim * 2, out_features=self.args.hidden_size,
# bias=True)
self.combine_linear = nn.Linear(
in_features=self.args.rnn_hidden_dim * 2 + self.args.pos_dim,
out_features=self.args.hidden_size,
bias=True)
init.xavier_uniform(self.linear.weight)
# init.xavier_uniform(self.non_linear.weight)
init.xavier_uniform(self.combine_linear.weight)
# self.non_linear.bias.data.uniform_(-np.sqrt(6 / (self.args.hidden_size + 1)),
# np.sqrt(6 / (self.args.hidden_size + 1)))
self.combine_linear.bias.data.uniform_(
-np.sqrt(6 / (self.args.hidden_size + 1)),
np.sqrt(6 / (self.args.hidden_size + 1)))
self.dropout = nn.Dropout(self.args.dropout)
self.softmax = nn.LogSoftmax(dim=1)
self.bucket = Variable(torch.zeros(1, self.args.label_size))
self.bucket_rnn = Variable(torch.zeros(1, self.args.rnn_hidden_dim))
if self.args.use_cuda is True:
self.bucket = self.bucket.cuda()
self.bucket_rnn = self.bucket_rnn.cuda()
def __init__(self, kwargs): super(TextCNNNet, self).__init__() self.input_size = kwargs['input_size'] self.hidden_size = kwargs['hidden_size'] self.output_size = kwargs['output_size'] if 'kernel_num' in kwargs: self.kernel_num = kwargs['kernel_num'] else: self.kernel_num = 128 if 'kernel_sizes' in kwargs: self.kernel_sizes = kwargs['kernel_sizes'] else: self.kernel_sizes = [1, 2, 3, 4] if 'embed_size' in kwargs: self.embed_size = kwargs['embed_size'] else: self.embed_size = kwargs['hidden_size'] if 'dropout' in kwargs: self.dropout = kwargs['dropout'] else: self.dropout = 0.1 if 'wide_conv' in kwargs: self.wide_conv = kwargs['wide_conv'] else: self.wide_conv = True if 'init_weight' in kwargs: self.init_weight = kwargs['init_weight'] else: self.init_weight = False if 'init_weight_value' in kwargs: self.init_weight_value = kwargs['init_weight_value'] else: self.init_weight_value = 2.0 if 'batch_normal' in kwargs: self.batch_normal = kwargs['batch_normal'] else: self.batch_normal = False if 'batch_normal_momentum' in kwargs: self.batch_normal_momentum else: self.batch_normal_momentum = 0.1 if 'batch_normal_affine' in kwargs: self.batch_normal_affine = kwargs['batch_normal_affine'] else: self.batch_normal_affine = False Ci = 1 # input channels, 处理文本,一层通道 Co = self.kernel_num # output channel Ks = self.kernel_sizes # list if 'max_norm' in kwargs: self.embed = nn.Embedding(self.input_size, self.embed_size, max_norm=kwargs['max_norm']) else: self.embed = nn.Embedding(self.input_size, self.embed_size, scale_grad_by_freq=True) if 'word_embedding' in kwargs: pretrained_weight = torch.from_numpy(kwargs['word_embedding']) self.embed.weight.data.copy_(pretrained_weight) self.embed.weight.requires_grad = True if self.wide_conv is True: self.convs1 = [nn.Conv2d(in_channels=Ci, out_channels=Co, kernel_size=(K, self.embed_size), stride=(1, 1), padding=(K//2 ,0), dilation=1, bias=True) for K in Ks] else: self.convs1 = [nn.Conv2d(in_channels=Ci, out_channels=Co, kernel_size=(K, self.embed_size), bias=True) for K in Ks] if self.init_weight: for conv in self.convs1: init.xavier_normal(conv.weight.data, gain=np.sqrt(self.init_weight_value)) fanin, fanout = self.cal_fanin_fanout(conv.weight.data) std = np.sqrt(self.init_weight_value) * np.sqrt(2.0 / (fanin+fanout)) init.uniform(conv.bias, 0, 0) self.dropout = nn.Dropout(self.dropout) in_fea = len(Ks) * Co self.f1 = nn.Linear(in_fea, in_fea//2, bias=True) self.f2 = nn.Linear(in_fea//2, self.output_size, bias=True) self.h2o = nn.Linear(in_fea, self.output_size) self.softmax = nn.LogSoftmax() if self.batch_normal: self.convs1_bn = nn.BatchNorm2d(num_features=Co, momentum=self.batch_normal_momentum, affine=self.batch_normal_affine) self.f1_bn = nn.BatchNorm1d(num_features=in_fea//2, momentum=self.batch_normal_momentum, affine=self.batch_normal_affine) self.f2_bn = nn.BatchNorm1d(num_features=self.output_size, momentum=self.batch_normal_momentum, affine=self.batch_normal_affine)
# load the training data, then further partition into training and validation sets, preserving the ratio of
# positives to negative training examples
train_data = imdbTrainDataset()
train_dataloader = DataLoader(train_data, batch_size=batch_size, num_workers=num_workers)
# load the model
model = CNN(vocab_size=20000, embedding_dim=128, hidden_dim=50, label_size=1, batch_size=batch_size, seq_len=250)
model.cuda()
# model._parameters = init.xavier_normal(list(model.parameters()))
# or
for param in model.parameters():
# init.xavier_normal(param)
init.uniform(param)
loss_fn = torch.nn.BCEWithLogitsLoss()
# loss_fn = torch.nn.CrossEntropyLoss()
loss_name = "bce"
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-5)
# optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
start_clock = time.clock()
epoch = 0
print("time_stamp: {}".format(time_stamp))
print()
print("model: {}".format(args.model))
print("data: {}".format(args.data))
print("features: {}".format(args.feats))
print("null features: {}".format(args.null))
def __init__(self, args, batchNorm=True):
super(FlowNetS, self).__init__()
self.batchNorm = batchNorm
self.conv1 = conv(self.batchNorm, 12, 64, kernel_size=7, stride=2)
self.conv2 = conv(self.batchNorm, 64, 128, kernel_size=5, stride=2)
self.conv3 = conv(self.batchNorm, 128, 256, kernel_size=5, stride=2)
self.conv3_1 = conv(self.batchNorm, 256, 256)
self.conv4 = conv(self.batchNorm, 256, 512, stride=2)
self.conv4_1 = conv(self.batchNorm, 512, 512)
self.conv5 = conv(self.batchNorm, 512, 512, stride=2)
self.conv5_1 = conv(self.batchNorm, 512, 512)
self.conv6 = conv(self.batchNorm, 512, 1024, stride=2)
self.conv6_1 = conv(self.batchNorm, 1024, 1024)
self.deconv5 = deconv(1024, 512)
self.deconv4 = deconv(1026, 256)
self.deconv3 = deconv(770, 128)
self.deconv2 = deconv(386, 64)
self.predict_flow6 = predict_flow(1024)
self.predict_flow5 = predict_flow(1026)
self.predict_flow4 = predict_flow(770)
self.predict_flow3 = predict_flow(386)
self.predict_flow2 = predict_flow(194)
self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=False)
self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=False)
self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=False)
self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=False)
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
def __init__(self, batchNorm=False, div_flow=20.):
super(FlowNet2, self).__init__()
self.batchNorm = batchNorm
self.div_flow = div_flow
#self.rgb_max = args.rgb_max
self.rgb_max = 1
#self.args = args
self.channelnorm = ChannelNorm()
# First Block (FlowNetC)
self.flownetc = FlowNetC.FlowNetC(batchNorm=self.batchNorm)
self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
# if args.fp16:
# self.resample1 = nn.Sequential(
# tofp32(),
# Resample2d(),
# tofp16())
# else:
self.resample1 = Resample2d()
# Block (FlowNetS1)
self.flownets_1 = FlowNetS.FlowNetS(batchNorm=self.batchNorm)
self.upsample2 = nn.Upsample(scale_factor=4, mode='bilinear')
# if args.fp16:
# self.resample2 = nn.Sequential(
# tofp32(),
# Resample2d(),
# tofp16())
# else:
self.resample2 = Resample2d()
# Block (FlowNetS2)
self.flownets_2 = FlowNetS.FlowNetS(batchNorm=self.batchNorm)
# Block (FlowNetSD)
self.flownets_d = FlowNetSD.FlowNetSD(batchNorm=self.batchNorm)
self.upsample3 = nn.Upsample(scale_factor=4, mode='nearest')
self.upsample4 = nn.Upsample(scale_factor=4, mode='nearest')
# if args.fp16:
# self.resample3 = nn.Sequential(
# tofp32(),
# Resample2d(),
# tofp16())
# else:
self.resample3 = Resample2d()
# if args.fp16:
# self.resample4 = nn.Sequential(
# tofp32(),
# Resample2d(),
# tofp16())
# else:
self.resample4 = Resample2d()
# Block (FLowNetFusion)
self.flownetfusion = FlowNetFusion.FlowNetFusion(
batchNorm=self.batchNorm)
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
def init_hidden(self, initrange):
for ww in self.parameters():
init.uniform(ww.data, -1 * initrange, initrange)
def trainClassifier(allDataTrain,targetDataTrain,allDataTest,targetDataTest,learning_rate,momentum,maxEpoch,saveModel,results):
classifier = buildClassifierModel(embedding_dim, 1).cuda()
for param in classifier.parameters():
init.uniform(param, -1 * 0.0000001, 0.0000001)
loss_function = nn.BCELoss(size_average=True).cuda()
optimizer = optim.RMSprop(classifier.parameters(), lr=learning_rate, alpha=0.99, eps=1e-08, weight_decay=0,momentum=momentum, centered=False)
lossVal = mres.LossValues()
errors = []
lrStr = mres.floatToStr("%2.15f",learning_rate)
momentumStr = mres.floatToStr("%2.15f",momentum)
epc = 0
numberOfDoc = math.floor((allDataTrain.size()[0]/batch_size)*batch_size)
for fold in range(folds):
for epoch in range(maxEpoch):
print("class : %s fold %d epoch %d" % (classname,fold,epoch))
epc += 1
inds = torch.range(1, numberOfDoc, batch_size).long()
shuffle = torch.randperm(inds.size()[0])
lr=optimizer.param_groups[0]['lr']
lrStr = mres.floatToStr("%2.15f",lr)
for i in range(int(numberOfDoc/batch_size)):
start = inds[shuffle[i]] - 1
endd = inds[shuffle[i]] + batch_size - 1
inp = autograd.Variable(allDataTrain[start:endd].data.cuda(), requires_grad=False)
target = autograd.Variable(torch.Tensor(batch_size).copy_(targetDataTrain[start:endd]).cuda(), requires_grad=False)
classifier.zero_grad()
pred = classifier.forward(inp)
loss = loss_function(pred,target)
print("fold %d epoch %d lr %s - pred %f target %f loss %f " % (fold,epoch,lrStr,pred.data[0][0],target.data[0], loss.data[0]))
loss.backward()
optimizer.step()
errors.append(loss.data[0])
lossVal.y.append(loss.data[0])
mean = torch.mean(torch.Tensor(errors))
lossVal.mean.append(mean)
if epoch % 50 == 0 and epoch != 0:
trainresults = mres.testClassifier(classifier,allDataTrain,targetDataTrain)
res = "train - lr %s mmt %s maxepoch %d epoch %d score %d/%d - trueNegPred/allNeg:%d/%d=%f truePosPred/allPos:%d/%d=%f" % (
lrStr, momentumStr, maxEpoch, epoch+1,trainresults.correct, trainresults.all,trainresults.trueNegatives,trainresults.allNegatives,
trainresults.negRate, trainresults.truePositives, trainresults.allPositives,trainresults.posRate)
results.append(res)
testresults = mres.testClassifier(classifier, allDataTest, targetDataTest)
res = "test - lr %s mmt %s maxepoch %d epoch %d score %d/%d - trueNegPred/allNeg:%d/%d=%f truePosPred/allPos:%d/%d=%f" % (
lrStr, momentumStr, maxEpoch, epoch+1,testresults.correct, testresults.all,
testresults.trueNegatives, testresults.allNegatives,
testresults.negRate, testresults.truePositives, testresults.allPositives, testresults.posRate)
results.append(res)
trainresults = mres.testClassifier(classifier, allDataTrain, targetDataTrain)
res = "train - lr %s mmt %s maxepoch %d epoch %d score %d/%d - trueNegPred/allNeg:%d/%d=%f truePosPred/allPos:%d/%d=%f" % (
lrStr, momentumStr, maxEpoch, maxEpoch, trainresults.correct, trainresults.all,
trainresults.trueNegatives, trainresults.allNegatives,
trainresults.negRate, trainresults.truePositives, trainresults.allPositives, trainresults.posRate)
results.append(res)
testresults = mres.testClassifier(classifier, allDataTest, targetDataTest)
res = "test - lr %s mmt %s maxepoch %d epoch %d score %d/%d - trueNegPred/allNeg:%d/%d=%f truePosPred/allPos:%d/%d=%f" % (
lrStr, momentumStr, maxEpoch, maxEpoch, testresults.correct, testresults.all,
testresults.trueNegatives, testresults.allNegatives,
testresults.negRate, testresults.truePositives, testresults.allPositives, testresults.posRate)
results.append(res)
if saveModel == True:
lossVal.x = range(folds * maxEpoch * int(numberOfDoc/batch_size))
lrStr = mres.floatToStr("%2.15f",learning_rate)
fname = "%scdlc-mlp-batch-loss-values-%s-%s-%d.bin" % (path,lrStr,momentumStr,maxEpoch)
fh = open(fname, 'wb') # Save model file as pickle
pickle.dump(lossVal, fh)
fh.close()
return classifier
def __init__(self, args):
print("Encoder model --- LSTM")
super(Encoder_WordLstm, self).__init__()
self.args = args
# random
self.char_embed = nn.Embedding(self.args.embed_char_num,
self.args.embed_char_dim)
for index in range(self.args.embed_char_dim):
self.char_embed.weight.data[
self.args.create_alphabet.char_PaddingID][index] = 0
self.char_embed.weight.requires_grad = True
self.bichar_embed = nn.Embedding(self.args.embed_bichar_num,
self.args.embed_bichar_dim)
for index in range(self.args.embed_bichar_dim):
self.bichar_embed.weight.data[
self.args.create_alphabet.bichar_PaddingID][index] = 0
self.bichar_embed.weight.requires_grad = True
# fix the word embedding
self.static_char_embed = nn.Embedding(self.args.static_embed_char_num,
self.args.embed_char_dim)
init.uniform(self.static_char_embed.weight,
a=-np.sqrt(3 / self.args.embed_char_dim),
b=np.sqrt(3 / self.args.embed_char_dim))
self.static_bichar_embed = nn.Embedding(
self.args.static_embed_bichar_num, self.args.embed_bichar_dim)
init.uniform(self.static_bichar_embed.weight,
a=-np.sqrt(3 / self.args.embed_bichar_dim),
b=np.sqrt(3 / self.args.embed_bichar_dim))
# load external word embedding
if args.char_Embedding is True:
print("char_Embedding")
pretrained_char_weight = np.array(args.pre_char_word_vecs)
self.static_char_embed.weight.data.copy_(
torch.from_numpy(pretrained_char_weight))
for index in range(self.args.embed_char_dim):
self.static_char_embed.weight.data[
self.args.create_static_alphabet.char_PaddingID][index] = 0
self.static_char_embed.weight.requires_grad = False
if args.bichar_Embedding is True:
print("bichar_Embedding")
pretrained_bichar_weight = np.array(args.pre_bichar_word_vecs)
self.static_bichar_embed.weight.data.copy_(
torch.from_numpy(pretrained_bichar_weight))
# print(self.static_bichar_embed.weight.data[self.args.create_static_alphabet.bichar_PaddingID])
# print(self.static_bichar_embed.weight.data[self.args.create_static_alphabet.bichar_UnkID])
for index in range(self.args.embed_bichar_dim):
self.static_bichar_embed.weight.data[
self.args.create_static_alphabet.
bichar_PaddingID][index] = 0
self.static_bichar_embed.weight.requires_grad = False
self.lstm_left = nn.LSTM(input_size=self.args.hidden_size,
hidden_size=self.args.rnn_hidden_dim,
dropout=self.args.dropout,
bias=True)
self.lstm_right = nn.LSTM(input_size=self.args.hidden_size,
hidden_size=self.args.rnn_hidden_dim,
dropout=self.args.dropout,
bias=True)
# init lstm weight and bias
init.xavier_uniform(self.lstm_left.weight_ih_l0)
init.xavier_uniform(self.lstm_left.weight_hh_l0)
init.xavier_uniform(self.lstm_right.weight_ih_l0)
init.xavier_uniform(self.lstm_right.weight_hh_l0)
value = np.sqrt(6 / (self.args.rnn_hidden_dim + 1))
self.lstm_left.bias_ih_l0.data.uniform_(-value, value)
self.lstm_left.bias_hh_l0.data.uniform_(-value, value)
self.lstm_right.bias_ih_l0.data.uniform_(-value, value)
self.lstm_right.bias_hh_l0.data.uniform_(-value, value)
self.hidden_l = self.init_hidden_cell(self.args.batch_size)
self.hidden_r = self.init_hidden_cell(self.args.batch_size)
self.dropout = nn.Dropout(self.args.dropout)
self.dropout_embed = nn.Dropout(self.args.dropout_embed)
self.input_dim = (self.args.embed_char_dim +
self.args.embed_bichar_dim) * 2
self.liner = nn.Linear(in_features=self.input_dim,
out_features=self.args.hidden_size,
bias=True)
# init linear
init.xavier_uniform(self.liner.weight)
init_linear_value = np.sqrt(6 / (self.args.hidden_size + 1))
self.liner.bias.data.uniform_(-init_linear_value, init_linear_value)
def __init__(self, args):
super(HighWay_CNN, self).__init__()
self.args = args
V = args.embed_num
D = args.embed_dim
C = args.class_num
Ci = 1
Co = args.kernel_num
Ks = args.kernel_sizes
if args.max_norm is not None:
print("max_norm = {} ".format(args.max_norm))
self.embed = nn.Embedding(V, D, max_norm=args.max_norm, scale_grad_by_freq=True)
# self.embed.weight.data.uniform(-0.1, 0.1)
else:
print("max_norm = {} ".format(args.max_norm))
self.embed = nn.Embedding(V, D, scale_grad_by_freq=True)
if args.word_Embedding:
pretrained_weight = np.array(args.pretrained_weight)
self.embed.weight.data.copy_(torch.from_numpy(pretrained_weight))
# fixed the word embedding
self.embed.weight.requires_grad = True
print("dddd {} ".format(self.embed.weight.data.size()))
if args.wide_conv is True:
print("using wide convolution")
self.convs1 = [nn.Conv2d(in_channels=Ci, out_channels=Co, kernel_size=(K, D), stride=(1, 1),
padding=(K//2, 0), dilation=1, bias=True) for K in Ks]
else:
print("using narrow convolution")
self.convs1 = [nn.Conv2d(in_channels=Ci, out_channels=Co, kernel_size=(K, D), bias=True) for K in Ks]
# self.convs1 = [nn.Conv2d(Ci, D, (K, D), stride=1, padding=(K // 2, 0)) for K in Ks]
print(self.convs1)
# for con in self.convs1:
# print("PP {} ".format(con.weight))
if args.init_weight:
print("Initing W .......")
for conv in self.convs1:
init.xavier_normal(conv.weight.data, gain=np.sqrt(args.init_weight_value))
fan_in, fan_out = HighWay_CNN.calculate_fan_in_and_fan_out(conv.weight.data)
print(" in {} out {} ".format(fan_in, fan_out))
std = np.sqrt(args.init_weight_value) * np.sqrt(2.0 / (fan_in + fan_out))
print("aaaaaaaaaaaaa {} ".format(std))
init.uniform(conv.bias, 0, 0)
self.dropout = nn.Dropout(args.dropout)
# self.dropout = nn.Dropout2d(args.dropout)
# self.dropout = nn.AlphaDropout(args.dropout)
in_fea = len(Ks) * Co
# self.fc1 = nn.Linear(in_features=in_fea, out_features=C, bias=True)
self.fc1 = nn.Linear(in_features=in_fea, out_features=in_fea, bias=True)
# self.fc2 = nn.Linear(in_features=in_fea // 2, out_features=C, bias=True)
# highway gate layer
# self.gate_layer = nn.Linear(in_features=in_fea, out_features=C, bias=True)
self.gate_layer = nn.Linear(in_features=in_fea, out_features=in_fea, bias=True)
# self.gate_layer.bias.data.fill_(-1)
# last liner
self.logit_layer = nn.Linear(in_features=in_fea, out_features=C, bias=True)
# whether to use batch normalizations
if args.batch_normalizations is True:
print("using batch_normalizations in the model......")
self.convs1_bn = nn.BatchNorm2d(num_features=Co, momentum=args.bath_norm_momentum,
affine=args.batch_norm_affine)
self.fc1_bn = nn.BatchNorm1d(num_features=in_fea//2, momentum=args.bath_norm_momentum,
affine=args.batch_norm_affine)
self.fc2_bn = nn.BatchNorm1d(num_features=C, momentum=args.bath_norm_momentum,
affine=args.batch_norm_affine)