关于sru源码class Model的parameters
class Model(nn.Module):
def __init__(self, words, args):
super(Model, self).__init__()
self.args = args
self.n_d = args.d
self.depth = args.depth
self.drop = nn.Dropout(args.dropout)
self.embedding_layer = EmbeddingLayer(self.n_d, words)
self.n_V = self.embedding_layer.n_V
if args.lstm:
self.rnn = nn.LSTM(self.n_d, self.n_d,
self.depth,
dropout = args.rnn_dropout
)
else:
self.rnn = MF.SRU(self.n_d, self.n_d, self.depth,
dropout = args.rnn_dropout,
rnn_dropout = args.rnn_dropout,
use_tanh = 0
)
self.output_layer = nn.Linear(self.n_d, self.n_V)
# tie weights
self.output_layer.weight = self.embedding_layer.embedding.weight#我运行了一下应该是指每个单词所对应的向量
self.init_weights()
if not args.lstm:
self.rnn.set_bias(args.bias)
def init_weights(self):
val_range = (3.0/self.n_d)**0.5
for p in self.parameters():
if p.dim() > 1: # matrix
p.data.uniform_(-val_range, val_range)
else:
p.data.zero_()
def forward(self, x, hidden):
emb = self.drop(self.embedding_layer(x))
output, hidden = self.rnn(emb, hidden)
output = self.drop(output)
output = output.view(-1, output.size(2))
output = self.output_layer(output)
return output, hidden
def init_hidden(self, batch_size):#hidden层的0初始化
weight = next(self.parameters()).data
zeros = Variable(weight.new(self.depth, batch_size, self.n_d).zero_())
if self.args.lstm:
return (zeros, zeros)
else:
return zeros
def print_pnorm(self):#p范数
norms = [ "{:.0f}".format(x.norm().data[0]) for x in self.parameters() ]
sys.stdout.write("\tp_norm: {}\n".format(
norms
))
这个问题源于我对Model类中的方法init_weight的理解，一直读不懂这个方法是做什么的，即self.parameters()，这个迭代器送出来的参数是什么呢，我假设这个里面应该是每一层更新的权重，所以我将sru源码的一部分给取了出来，让其输出Model里的parameters，代码如下（sru源码--language model）：
#coding:UTF-8
'''
Created on 2017-12-4
@author: lai
'''
import time
import random
import math
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import sys
import cuda_functional as MF
def read_corpus(path, eos="</s>"):
data = [ ]
with open(path) as fin:
for line in fin:
data += line.split() + [ eos ]
return data
def create_batches(data_text, map_to_ids, batch_size):
data_ids = map_to_ids(data_text)
N = len(data_ids)
L = ((N-1) // batch_size) * batch_size
x = np.copy(data_ids[:L].reshape(batch_size,-1).T)
y = np.copy(data_ids[1:L+1].reshape(batch_size,-1).T)
x, y = torch.from_numpy(x), torch.from_numpy(y)
x, y = x.contiguous(), y.contiguous()
return x,y
class EmbeddingLayer(nn.Module):#为语料中每一个单词对应的其相应的词向量
def __init__(self, n_d, words, fix_emb=False):
super(EmbeddingLayer, self).__init__()
word2id = {}
for w in words:
if w not in word2id:
word2id[w] = len(word2id)#把文本映射到数字上。
self.word2id = word2id
self.n_V, self.n_d = len(word2id), n_d#n_V应该是指词库大小，n_d指hidden state size
self.embedding = nn.Embedding(self.n_V, n_d)#赋予每个单词相应的词向量
def forward(self, x):
return self.embedding(x)
def map_to_ids(self, text):#映射
return np.asarray([self.word2id[x] for x in text],
dtype='int64'
)
class Model(nn.Module):
def __init__(self, words, args):
super(Model, self).__init__()
self.args = args
self.n_d = args.d
self.depth = args.depth
self.drop = nn.Dropout(args.dropout)
self.embedding_layer = EmbeddingLayer(self.n_d, words)
self.n_V = self.embedding_layer.n_V
if args.lstm:
self.rnn = nn.LSTM(self.n_d, self.n_d,
self.depth,
dropout = args.rnn_dropout
)
else:
self.rnn = MF.SRU(self.n_d, self.n_d, self.depth,
dropout = args.rnn_dropout,
rnn_dropout = args.rnn_dropout,
use_tanh = 0
)
self.output_layer = nn.Linear(self.n_d, self.n_V)
# tie weights
self.output_layer.weight = self.embedding_layer.embedding.weight#我运行了一下应该是指每个单词所对应的向量
self.init_weights()
if not args.lstm:
self.rnn.set_bias(args.bias)
def init_weights(self):
val_range = (3.0/self.n_d)**0.5
for p in self.parameters():
if p.dim() > 1: # matrix
p.data.uniform_(-val_range, val_range)
print('222222',p.data)
else:
p.data.zero_()
print('0000',p.data)
if __name__ == "__main__":
argparser = argparse.ArgumentParser(sys.argv[0], conflict_handler='resolve')
argparser.add_argument("--lstm", action="store_true")
argparser.add_argument("--train", type=str, required=True, help="training file")
argparser.add_argument("--batch_size", "--batch", type=int, default=32)
argparser.add_argument("--unroll_size", type=int, default=35)
argparser.add_argument("--max_epoch", type=int, default=300)
argparser.add_argument("--d", type=int, default=910)
argparser.add_argument("--dropout", type=float, default=0.7,
help="dropout of word embeddings and softmax output"
)
argparser.add_argument("--rnn_dropout", type=float, default=0.2,
help="dropout of RNN layers"
)
argparser.add_argument("--bias", type=float, default=-3,
help="intial bias of highway gates",
)
argparser.add_argument("--depth", type=int, default=6)
argparser.add_argument("--lr", type=float, default=1.0)
argparser.add_argument("--lr_decay", type=float, default=0.98)
argparser.add_argument("--lr_decay_epoch", type=int, default=175)
argparser.add_argument("--weight_decay", type=float, default=1e-5)
argparser.add_argument("--clip_grad", type=float, default=5)
args = argparser.parse_args()
print(args)
train = read_corpus(args.train)
model = Model(train, args)
model.cuda()
map_to_ids = model.embedding_layer.map_to_ids
train = create_batches(train, map_to_ids, args.batch_size)
print('111',model.parameters())
再终端中输入运行命令：
python 2.py --train train.txt
输出：
Namespace(batch_size=32, bias=-3, clip_grad=5, d=910, depth=6, dropout=0.7, lr=1.0, lr_decay=0.98, lr_decay_epoch=175, lstm=False, max_epoch=300, rnn_dropout=0.2, train='train.txt', unroll_size=35, weight_decay=1e-05)
222222
4.8794e-02 5.0702e-02 -3.2630e-02 ... -5.3750e-02 4.2253e-02 1.6446e-02
-5.1652e-02 -2.3051e-02 4.3890e-02 ... 1.8805e-02 1.6605e-02 2.6666e-02
2.5273e-02 -5.1426e-03 5.3130e-02 ... -4.8786e-02 4.0186e-02 -4.3724e-02
... ⋱ ...
-3.3133e-02 3.3400e-02 3.2185e-02 ... -5.0593e-02 -2.3048e-02 -2.1572e-02
2.9908e-03 -2.1938e-02 -2.1926e-02 ... -4.5163e-02 -4.1678e-02 -5.2639e-02
-2.2036e-02 2.3908e-04 1.9383e-02 ... -1.0341e-02 4.7491e-02 -5.0599e-02
[torch.FloatTensor of size 10000x910]
222222
-6.1627e-03 1.9962e-02 5.6098e-02 ... 5.2324e-02 -1.0912e-02 1.7969e-02
1.1683e-02 1.4485e-02 3.7155e-02 ... -4.6458e-02 -2.8750e-02 -1.7442e-02
5.3697e-02 3.4534e-02 -2.5292e-02 ... -3.9264e-02 -2.8864e-02 2.3790e-02
... ⋱ ...
7.6450e-03 -2.1589e-02 -7.6684e-03 ... -5.6521e-02 -5.5103e-02 -3.8065e-02
4.7252e-02 5.7209e-02 -4.9279e-02 ... -2.0944e-02 -4.3891e-03 1.8820e-02
2.7026e-02 3.5590e-02 1.3660e-02 ... -1.6219e-02 -2.1856e-02 3.2678e-02
[torch.FloatTensor of size 910x2730]
0000
0
0
0
⋮
0
0
0
[torch.FloatTensor of size 1820]
222222
-1.2439e-02 -5.5866e-02 -3.5799e-02 ... -4.9976e-02 7.3134e-03 4.5684e-03
-4.6130e-02 -4.7773e-02 -4.3640e-02 ... -3.2027e-02 -8.8562e-03 4.3218e-02
-3.5260e-02 3.1456e-02 1.3324e-02 ... 3.4487e-02 -7.7102e-03 2.9963e-02
... ⋱ ...
-1.6921e-02 -1.5771e-02 5.3847e-02 ... 4.6351e-02 4.9333e-02 -1.1978e-02
-1.8770e-02 -1.5817e-02 -7.6655e-05 ... -8.4615e-03 1.4490e-02 -5.6743e-02
4.1060e-03 -2.4452e-02 2.5512e-02 ... -2.3961e-02 -5.2609e-02 3.3445e-02
[torch.FloatTensor of size 910x2730]
0000
0
0
0
⋮
0
0
0
[torch.FloatTensor of size 1820]
222222
-3.6535e-02 -2.4697e-02 3.2514e-02 ... 3.0889e-02 -4.7916e-03 9.5873e-03
4.5222e-02 -5.7333e-02 5.4079e-02 ... 1.7790e-02 3.5510e-02 -1.2171e-02
7.5279e-03 -2.7133e-02 -5.1036e-02 ... 5.6305e-02 -2.0042e-02 -2.8884e-02
... ⋱ ...
-4.5409e-02 -1.6207e-02 3.4128e-02 ... -5.6980e-02 1.6646e-02 -2.0662e-02
2.8941e-02 3.1405e-02 5.7100e-02 ... 3.9499e-03 9.5197e-03 -2.3475e-02
-5.1939e-02 -9.6567e-03 3.1139e-02 ... -1.0642e-02 -4.8837e-02 2.7009e-02
[torch.FloatTensor of size 910x2730]
0000
0
0
0
⋮
0
0
0
[torch.FloatTensor of size 1820]
222222
1.4545e-02 -1.7484e-02 -1.3450e-02 ... 4.9990e-02 3.6013e-03 -2.5272e-02
4.6915e-02 2.4484e-02 -2.6583e-02 ... 3.4737e-02 3.9499e-02 -2.8632e-02
1.8722e-02 -2.1864e-02 2.4649e-02 ... 4.9049e-02 4.8219e-02 3.7317e-02
... ⋱ ...
-2.6708e-02 4.2176e-02 3.8287e-02 ... 3.3608e-02 -2.7229e-02 9.4752e-03
1.2404e-02 1.7356e-02 7.0494e-03 ... 1.5802e-02 -7.5168e-03 -4.1576e-02
-3.1050e-02 3.5632e-02 2.2318e-03 ... -1.9828e-02 4.4247e-02 -2.3669e-02
[torch.FloatTensor of size 910x2730]
0000
0
0
0
⋮
0
0
0
[torch.FloatTensor of size 1820]
222222
-8.6860e-03 2.4917e-02 -4.8584e-02 ... -1.1277e-02 -1.2668e-02 -1.6445e-02
-2.5161e-02 -4.4705e-03 -4.5265e-02 ... -3.1264e-02 -4.2164e-02 -2.4916e-02
-1.8575e-02 -1.8767e-02 -5.2647e-02 ... 5.4461e-02 -5.0726e-02 -3.1518e-03
... ⋱ ...
-3.1745e-02 -3.8159e-02 1.7577e-02 ... -5.6739e-02 1.9196e-02 1.6574e-02
-5.5951e-02 -6.2410e-03 -5.6714e-02 ... 2.8419e-02 5.7141e-02 2.3431e-02
-1.7646e-02 8.7587e-04 -2.3462e-02 ... -4.9807e-04 4.2565e-02 -4.5738e-02
[torch.FloatTensor of size 910x2730]
0000
0
0
0
⋮
0
0
0
[torch.FloatTensor of size 1820]
222222
-8.5008e-03 4.9589e-02 4.8005e-02 ... 5.2643e-03 1.4385e-02 -1.8161e-02
3.0520e-03 5.5756e-02 3.9487e-02 ... -2.9614e-03 -5.1740e-02 -4.8080e-02
1.8335e-02 -5.5416e-02 -1.0836e-02 ... 2.8635e-02 -8.8250e-03 -1.4533e-02
... ⋱ ...
5.2809e-02 -3.2417e-02 3.9305e-02 ... 2.2464e-02 -4.7438e-02 5.1094e-02
-5.5829e-02 -4.9564e-02 1.3892e-02 ... -3.4778e-02 4.3359e-02 8.6556e-03
-2.1687e-03 -3.7360e-03 4.2217e-03 ... 3.9019e-02 -4.2598e-02 1.6985e-02
[torch.FloatTensor of size 910x2730]
0000
0
0
0
⋮
0
0
0
[torch.FloatTensor of size 1820]
0000
0
0
0
⋮
0
0
0
[torch.FloatTensor of size 10000]
111 <generator object Module.parameters at 0x7f6fe8cc3eb8>
下面是方法init_weight的代码：
def init_weights(self):
val_range = (3.0/self.n_d)**0.5
for p in self.parameters():
if p.dim() > 1: # matrix
p.data.uniform_(-val_range, val_range)
print('222222',p.data)
else:
p.data.zero_()
print('0000',p.data)
上面运行输出的结果就是p.data.uniform_(-val_range, val_range)以及p.data.zero_()的值，这里的参数我猜测一个是sru中的权重（w）另一个是偏置（b），但是这样的话就有一个疑问，这里输出的第一个大小为10000*910的tensor是词向量化得到的10000个单词的词向量，而最后一个大小为10000的tensor是最后线性分类全连接层的参数，所以剩下有六对的w和b，但是这样的话就有一个疑问，因为循环神经网络是时间共享的，所以应该只有一对才对，为了解决这个疑问，
我将用lstm做mnist分类的代码拿了出来，并将它的model的参数打印了出来，代码和结果如下所示
代码：
import torch
from torch import nn
from torch.autograd import Variable
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
torch.manual_seed(1) # reproducible
# Hyper Parameters
EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 64
TIME_STEP = 28 # rnn time step / image height
INPUT_SIZE = 28 # rnn input size / image width
LR = 0.01 # learning rate
DOWNLOAD_MNIST = True # set to True if haven't download the data
# Mnist digital dataset
train_data = dsets.MNIST(
root='./mnist/',
train=True, # this is training data
transform=transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to
# torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
download=DOWNLOAD_MNIST, # download it if you don't have it
)
# plot one example
print(train_data.train_data.size()) # (60000, 28, 28)
print(train_data.train_labels.size()) # (60000)
plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
plt.title('%i' % train_data.train_labels[0])
plt.show()
# Data Loader for easy mini-batch return in training
train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
# convert test data into Variable, pick 2000 samples to speed up testing
test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())
test_x = Variable(test_data.test_data, volatile=True).type(torch.FloatTensor)[:2000]/255. # shape (2000, 28, 28) value in range(0,1)
test_y = test_data.test_labels.numpy().squeeze()[:2000] # covert to numpy array
class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.LSTM( # if use nn.RNN(), it hardly learns
input_size=INPUT_SIZE,
hidden_size=64, # rnn hidden unit
num_layers=2, # number of rnn layer
batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
)
self.out = nn.Linear(64, 10)
def forward(self, x):
# x shape (batch, time_step, input_size)
# r_out shape (batch, time_step, output_size)
# h_n shape (n_layers, batch, hidden_size)
# h_c shape (n_layers, batch, hidden_size)
r_out, (h_n, h_c) = self.rnn(x, None) # None represents zero initial hidden state
# choose r_out at the last time step
out = self.out(r_out[:, -1, :])
return out
def init_weights(self):
for p in self.parameters():
print('PPP',p.data)
rnn = RNN()
print(rnn.init_weights())
输出：
torch.Size([60000, 28, 28])
torch.Size([60000])
PPP
-2.0745e-02 1.2430e-01 5.5081e-02 ... -1.4137e-02 9.4529e-02 -6.7606e-02
-1.1815e-01 8.6035e-03 4.2617e-02 ... 8.2401e-02 -1.1524e-01 -5.6738e-02
-8.2542e-02 -1.1019e-01 9.4536e-02 ... 4.0159e-02 6.2041e-02 -5.0376e-02
... ⋱ ...
1.0238e-01 5.3194e-02 5.3342e-02 ... -1.5019e-02 -1.0299e-01 2.3091e-02
4.5909e-02 -5.0352e-02 -2.5497e-02 ... 1.1765e-01 -1.1448e-01 -3.1609e-02
3.1011e-06 -1.0142e-01 1.2229e-01 ... 3.1813e-02 7.6921e-02 4.4233e-03
[torch.FloatTensor of size 256x28]
PPP
-2.4325e-03 1.1478e-02 9.3458e-02 ... -1.1657e-01 -3.6968e-03 1.2013e-01
1.2265e-01 -2.3560e-02 -5.3951e-02 ... 4.1457e-02 -6.7170e-02 6.1414e-02
1.2334e-01 -6.3188e-02 3.9050e-02 ... 8.4631e-02 4.0930e-04 8.3604e-03
... ⋱ ...
5.6417e-02 3.7298e-02 5.7616e-02 ... 2.9125e-02 -6.6484e-02 -4.2838e-02
-6.0267e-02 8.6004e-02 4.4727e-02 ... -4.9643e-02 -3.5065e-03 -2.5401e-02
8.1001e-02 5.8518e-02 -9.0292e-02 ... -1.5258e-02 5.6519e-02 6.1370e-02
[torch.FloatTensor of size 256x64]
PPP
0.0282
-0.0362
0.0864
0.0677
0.0012
0.0699
0.0850
-0.0927
0.0074
-0.0183
0.0679
0.1177
0.0255
0.1012
0.1248
-0.0625
0.0023
-0.0255
0.0870
-0.0900
0.1057
0.1233
0.0982
0.0475
-0.0387
-0.0267
-0.0964
-0.0153
0.0004
-0.0410
0.0771
-0.0399
0.0746
-0.0210
-0.0396
0.1108
0.0347
0.0263
0.0244
0.1113
-0.1071
0.1036
0.0478
0.0217
0.0314
0.0138
-0.1113
-0.1192
-0.0286
-0.0674
-0.0165
-0.0097
0.0663
-0.1072
0.0048
-0.1062
0.0677
-0.0028
0.0809
0.0119
0.1111
0.0363
0.0877
0.0189
0.0396
0.0358
-0.0257
0.0966
0.0951
-0.1179
-0.0906
-0.0619
-0.0229
-0.1193
0.0254
0.0110
0.0400
0.0655
0.1200
-0.0940
0.0728
0.0882
-0.1049
0.0939
0.0041
-0.0711
0.0914
-0.0461
0.0109
-0.0800
-0.0766
-0.0265
-0.0381
-0.0433
0.0193
0.0812
0.0163
0.0358
-0.0053
-0.0900
-0.0037
0.1009
0.1084
0.1006
-0.1237
-0.1227
0.0808
-0.0083
0.0376
0.0424
-0.1121
0.0379
0.0457
0.0443
-0.0528
0.0220
-0.0690
0.0620
-0.0660
-0.1124
0.1238
0.1188
0.0121
0.0574
0.1246
0.1000
-0.1034
0.0387
0.0307
-0.0669
-0.0619
-0.0819
0.0566
0.0150
0.0271
-0.0843
-0.0209
-0.0957
-0.1174
0.1031
-0.1250
0.0180
-0.0449
0.0920
0.1114
0.0604
-0.0987
0.0378
-0.0088
-0.0471
0.0549
-0.1234
0.1069
-0.0567
0.0241
-0.0163
0.0585
0.0199
-0.0188
0.0265
-0.0673
0.0697
-0.1224
0.1042
-0.0697
0.0695
0.0575
-0.1156
0.0663
0.1177
0.0562
-0.0417
-0.0054
0.0045
0.0614
-0.0089
0.0203
-0.1049
-0.1201
-0.0638
0.0728
0.0208
-0.1018
-0.0363
0.1128
-0.0524
0.0992
0.0937
-0.0378
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PPP
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0.0708 0.0393 0.0189 0.0350 -0.0329 0.1113 0.0110 -0.0083 -0.1152 -0.0735
0.0585 0.0925 0.0616 0.0478 0.0957 0.1038 0.0545 -0.0227 -0.1126 0.0958
0.1080 -0.1215 0.0274 0.0803 -0.1214 0.0364 0.0985 -0.0505 0.0941 -0.0675
-0.0153 0.1246 -0.0902 0.0092 0.1193 -0.1020 -0.0869 0.0396 0.1078 0.0155
0.1243 0.0651 -0.0685 -0.0275 -0.0058 0.0416 -0.0851 0.0398 0.0317 -0.0656
-0.0128 0.0311 -0.0837 -0.0885 -0.0965 0.0931 -0.0942 -0.0342 0.0851 0.0435
Columns 50 to 59
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0.1047 -0.0863 0.0562 0.0295 0.0177 -0.0250 0.0261 0.1133 0.0844 0.0866
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0.0445 0.1137 0.1123 -0.0716 0.0885 -0.0383 0.0276 0.0571 0.0976 0.0298
-0.1082 -0.1132 -0.0977 -0.0630 0.1066 0.0418 0.0862 -0.0329 -0.0949 -0.1048
0.0947 0.0587 -0.0304 0.0770 -0.0187 0.0003 -0.0628 -0.1068 0.1023 0.0669
-0.0424 -0.0686 -0.0745 -0.0949 -0.0700 0.1227 -0.0021 -0.1125 -0.1001 0.0545
Columns 60 to 63
0.0592 -0.0805 -0.0735 -0.0953
0.0493 -0.0285 0.0179 0.0019
0.0548 0.0819 -0.1057 0.0855
0.0880 -0.0224 0.0091 0.0845
0.0501 -0.0397 -0.0922 0.1050
0.0109 -0.1045 0.0098 -0.0755
0.1079 0.0461 0.0320 -0.0830
0.0902 0.0743 -0.0809 -0.0330
-0.0153 0.0420 0.0624 -0.1119
-0.0138 -0.0618 0.1001 0.0437
[torch.FloatTensor of size 10x64]
PPP
0.0109
-0.0778
-0.0501
0.0163
0.0763
-0.0792
0.1141
-0.0127
0.0162
0.0808
[torch.FloatTensor of size 10]
None
关于pytorch中LSTM的可以再这里查看pytorch之LSTM。
我打印出Lstm的参数，并将它们结合pytorch的官方文档pytorch之LSTM，发现其实LSTM的这些参数都是Variables，注意到这个例子里的w和b也不只有一对，而是有两对，因为LSTM的num_layers=2，当这个值为3时就会有3对，由这里我受到启发，在改变sru的layer后，也发生了变化。由此我得出结论循环神经网络并不是只有一个神经单元，而是可以有多个，之前我一直以为只有一个。
而sru中的参数也是以Variable的形式存在与整个模型中，可以被更新。
