我正在从头开始做LSTM并遵循本指南，但损失并没有减少，而是在增加。这是迄今为止我发现的最好的指南，但即使这个指南不完整也没有多说。除了发现我的代码特有的问题，我将非常感谢任何显示LSTM完整序列的来源。
https://wiseodd.github.io/techblog/2016/08/12/lstm-backprop/
模型定义：
import numpy as np
H = 128 # Number of LSTM layer's neurons
D = ... # Number of input dimension == number of items in vocabulary
Z = H + D # Because we will concatenate LSTM state with the input
model = dict(
Wf=np.random.randn(Z, H) / np.sqrt(Z / 2.),
Wi=np.random.randn(Z, H) / np.sqrt(Z / 2.),
Wc=np.random.randn(Z, H) / np.sqrt(Z / 2.),
Wo=np.random.randn(Z, H) / np.sqrt(Z / 2.),
Wy=np.random.randn(H, D) / np.sqrt(D / 2.),
bf=np.zeros((1, H)),
bi=np.zeros((1, H)),
bc=np.zeros((1, H)),
bo=np.zeros((1, H)),
by=np.zeros((1, D))
)
我的模特：
# RNN class
class RNN:
def __init__(self, n, d, RL, LR):
"""Pass input size (n), number of memory cells (d), recurrence length (RL), and learning rate (LR)"""
self.n, self.d, self.z, z = n, d, n + d, n + d
self.d = d
self.z, z = n + d, n + d
self.RL = RL
self.LR = LR
self.x = []
self.Cells = [Cell(n, d, self)]
self.Wi, self.Wf, self.Wo, self.Wc, self.Wy = randn(z, d) / sqrt(z / 2), randn(z, d) / sqrt(z / 2), randn(z, d) / sqrt(z / 2), randn(z, d) / sqrt(z / 2), randn(d, n) / sqrt(d / 2)
self.bi, self.bf, self.bo, self.bc, self.by = randn(d, 1), randn(d, 1), randn(d, 1), randn(d, 1), randn(n, 1)
self.dWi, self.dWf, self.dWo, self.dWc, self.dWy = zeros((z, d)), zeros((z, d)), zeros((z, d)), zeros((z, d)), zeros((d, n))
self.dbi, self.dbf, self.dbo, self.dbc, self.dby = zeros((d, 1)), zeros((d, 1)), zeros((d, 1)), zeros((d, 1)), zeros((n, 1))
我的手机：
class Cell:
def __init__(self, n, d, rnn):
"""Pass the input size (n) and memory cell size (d), create hidden state of size d, pass rnn (self)"""
self.n, self.d, self.h, self.z, z = n, d, zeros((d, 1)), n + d, n + d
self.rnn = rnn
他们的前馈：
def lstm_forward(X, state):
m = model
Wf, Wi, Wc, Wo, Wy = m['Wf'], m['Wi'], m['Wc'], m['Wo'], m['Wy']
bf, bi, bc, bo, by = m['bf'], m['bi'], m['bc'], m['bo'], m['by']
h_old, c_old = state
# One-hot encode
X_one_hot = np.zeros(D)
X_one_hot[X] = 1.
X_one_hot = X_one_hot.reshape(1, -1)
# Concatenate old state with current input
X = np.column_stack((h_old, X_one_hot))
hf = sigmoid(X @ Wf + bf)
hi = sigmoid(X @ Wi + bi)
ho = sigmoid(X @ Wo + bo)
hc = tanh(X @ Wc + bc)
c = hf * c_old + hi * hc
h = ho * tanh(c)
y = h @ Wy + by
prob = softmax(y)
state = (h, c) # Cache the states of current h & c for next iter
cache = ... # Add all intermediate variables to this cache
return prob, state, cache
我的前馈：
def feedforward(self, x, c_, h_):
"""Pass an input of size n, the previous hidden state(ht), and the previous cell state(c)"""
n, d = self.n, self.d
Wi, Wf, Wo, Wc, Wy = self.rnn.Wi, self.rnn.Wf, self.rnn.Wo, self.rnn.Wc, self.rnn.Wy
bi, bf, bo, bc, by = self.rnn.bi, self.rnn.bf, self.rnn.bo, self.rnn.bc, self.rnn.by
index = x # one hot encoding
x = zeros((n, 1))
x[index] = 1
g = concat((x, h_)) # input g is input x + previous hidden state
it = sigmoid(dot(Wi.T, g) + bi) # gate activations
ft = sigmoid(dot(Wf.T, g) + bf)
ot = sigmoid(dot(Wo.T, g) + bo)
ct = tanh(dot(Wc.T, g) + bc) # non linearity activation
c = ft * c_ + it * ct # cell state
ht = ot * tanh(c) # squashed hidden state
yt = dot(Wy.T, ht) + by # output state
p = softmax(yt) # call softmax, get probability
self.c_, self.h_ = c_, h_
self.it, self.ft, self.ot, self.ct = it, ft, ot, ct
self.c, self.ht, self.yt, self.p, self.g = c, ht, yt, p, g
return ht, c
他们的支持：
def lstm_backward(prob, y_train, d_next, cache):
# Unpack the cache variable to get the intermediate variables used in forward step
... = cache
dh_next, dc_next = d_next
# Softmax loss gradient
dy = prob.copy()
dy[1, y_train] -= 1.
# Hidden to output gradient
dWy = h.T @ dy
dby = dy
# Note we're adding dh_next here
dh = dy @ Wy.T + dh_next
# Gradient for ho in h = ho * tanh(c)
dho = tanh(c) * dh
dho = dsigmoid(ho) * dho
# Gradient for c in h = ho * tanh(c), note we're adding dc_next here
dc = ho * dh * dtanh(c)
dc = dc + dc_next
# Gradient for hf in c = hf * c_old + hi * hc
dhf = c_old * dc
dhf = dsigmoid(hf) * dhf
# Gradient for hi in c = hf * c_old + hi * hc
dhi = hc * dc
dhi = dsigmoid(hi) * dhi
# Gradient for hc in c = hf * c_old + hi * hc
dhc = hi * dc
dhc = dtanh(hc) * dhc
# Gate gradients, just a normal fully connected layer gradient
dWf = X.T @ dhf
dbf = dhf
dXf = dhf @ Wf.T
dWi = X.T @ dhi
dbi = dhi
dXi = dhi @ Wi.T
dWo = X.T @ dho
dbo = dho
dXo = dho @ Wo.T
dWc = X.T @ dhc
dbc = dhc
dXc = dhc @ Wc.T
# As X was used in multiple gates, the gradient must be accumulated here
dX = dXo + dXc + dXi + dXf
# Split the concatenated X, so that we get our gradient of h_old
dh_next = dX[:, :H]
# Gradient for c_old in c = hf * c_old + hi * hc
dc_next = hf * dc
grad = dict(Wf=dWf, Wi=dWi, Wc=dWc, Wo=dWo, Wy=dWy, bf=dbf, bi=dbi, bc=dbc, bo=dbo, by=dby)
state = (dh_next, dc_next)
return grad, state
我的支持：
def backpropagate(self, y, ht1, ct1):
n, d = self.n, self.d
Wi, Wf, Wo, Wc, Wy = self.rnn.Wi, self.rnn.Wf, self.rnn.Wo, self.rnn.Wc, self.rnn.Wy
dWi, dWf, dWo, dWc, dWy = self.rnn.dWi, self.rnn.dWf, self.rnn.dWo, self.rnn.dWc, self.rnn.dWy
dbi, dbf, dbo, dbc, dby = self.rnn.dbi, self.rnn.dbf, self.rnn.dbo, self.rnn.dbc, self.rnn.dby
c_, h_ = self.c_, self.h_
it, ft, ot, ct = self.it, self.ft, self.ot, self.ct
c, ht, yt, p = self.c, self.ht, self.yt, self.p
g = self.g
dy = copy(p)
dy[y] -= 1
loss = cross_ent(p, y)
dh = dot(Wy, dy) + ht1
dh = clip(dh, -6, 6)
do = tanh(c) * dh
do = dsigmoid(ot) * do
dc = ot * dh * dtanh(c)
dc = dc + ct1
dc = clip(dc, -6, 6)
df = c_ * dc
df = dsigmoid(ft) * df
di = ct * dc
di = dsigmoid(it) * di
dct = it * dc
dct = dtanh(ct) * dct
dWf += dot(g, df.T)
dWi += dot(g, di.T)
dWo += dot(g, do.T)
dWc += dot(g, dc.T)
dWy += dot(ht, dy.T)
dbf += df
dbi += di
dbo += do
dbc += dc
dby += dy
dxi = dot(Wi, di)
dxf = dot(Wf, df)
dxo = dot(Wo, do)
dxc = dot(Wc, dct)
dx = dxf + dxi + dxo + dxc
dht1 = dx[n:]
dct1 = ft * dc
return loss, dht1, dct1
他们的训练步骤：
def train_step(X_train, y_train, state):
probs = []
caches = []
loss = 0.
h, c = state
# Forward Step
for x, y_true in zip(X_train, y_train):
prob, state, cache = lstm_forward(x, state, train=True)
loss += cross_entropy(prob, y_true)
# Store forward step result to be used in backward step
probs.append(prob)
caches.append(cache)
# The loss is the average cross entropy
loss /= X_train.shape[0]
# Backward Step
# Gradient for dh_next and dc_next is zero for the last timestep
d_next = (np.zeros_like(h), np.zeros_like(c))
grads = {k: np.zeros_like(v) for k, v in model.items()}
# Go backward from the last timestep to the first
for prob, y_true, cache in reversed(list(zip(probs, y_train, caches))):
grad, d_next = lstm_backward(prob, y_true, d_next, cache)
# Accumulate gradients from all timesteps
for k in grads.keys():
grads[k] += grad[k]
return grads, loss, state
我的训练步骤：
def FeedForward(self, inputs, ht_, ct_):
n, d, rl, Cells = self.n, self.d, self.RL, self.Cells
while len(Cells) < rl:
Cells.append(Cell(n, d, self))
for cell, x in zip(Cells, range(len(inputs))):
ht_, ct_ = cell.feedforward(x, ht_, ct_)
return ht_, ct_
def BPTT(self, outputs, ht1, ct1):
n, d, z, rl = self.n, self.d, self.n + self.d, self.RL
Cells = self.Cells
avg_loss = 0
for i in reversed(range(rl)):
loss, ht1, ct1 = Cells[i].backpropagate(outputs[i], ht1, ct1)
avg_loss += loss
avg_loss /= rl
return avg_loss, ht1, ct1
def train(self, inputs, outputs):
n, d, z, rl = self.n, self.d, self.n + self.d, self.RL
index = 0
LR = 0.1
loss = 0
ht_, ct_ = zeros((d, 1)), zeros((d, 1))
ht1, ct1 = zeros((d, 1)), zeros((d, 1))
while index < len(outputs):
xlist = inputs[index:index + rl]
ylist = outputs[index:index + rl]
ht_, ct_ = self.FeedForward(xlist, ht_, ct_)
loss, ht1, ct1 = self.BPTT(ylist, ht1, ct1)
#print(loss)
self.update(LR)
index += rl
def update(self, LR):
n, d, z = self.n, self.d, self.n + self.d
self.Wi -= LR * self.dWi
self.Wf -= LR * self.dWf
self.Wo -= LR * self.dWo
self.Wc -= LR * self.dWc
self.Wy -= LR * self.dWy
self.bi -= LR * self.dbi
self.bf -= LR * self.dbf
self.bo -= LR * self.dbo
self.bc -= LR * self.dbc
self.by -= LR * self.dby
self.dWi, self.dWf, self.dWo, self.dWc, self.dWy = zeros((z, d)), zeros((z, d)), zeros((z, d)), zeros((z, d)), zeros((d, n))
self.dbi, self.dbf, self.dbo, self.dbc, self.dby = zeros((d, 1)), zeros((d, 1)), zeros((d, 1)), zeros((d, 1)), zeros((n, 1))
我的整体代码：
# Import logistic function that doesn't explode outside a 64 bit float
from scipy.special import expit as sigmoid
from numpy import zeros, zeros_like, tanh, exp, sum, dot, sqrt, log, argmax, concatenate as concat, copy
from numpy.random import randn
# derivative of sigmoid function
def dsigmoid(z):
return sigmoid(z) * (1 - sigmoid(z))
# derivative of hyperbolic tangent
def dtanh(z):
return 1 - tanh(z) ** 2
# probability function
def softmax(z):
return exp(z) / sum(exp(z))
# cross entropy loss
def cross_ent(p, y):
return -log(p[y])
# RNN class
class RNN:
def __init__(self, n, d, RL, LR):
"""Pass input size (n), number of memory cells (d), recurrence length (RL), and learning rate (LR)"""
self.n, self.d, self.z, z = n, d, n + d, n + d
self.d = d
self.z, z = n + d, n + d
self.RL = RL
self.LR = LR
self.x = []
self.Cells = [Cell(n, d, self)]
self.Wi, self.Wf, self.Wo, self.Wc, self.Wy = randn(z, d) / sqrt(z / 2), randn(z, d) / sqrt(z / 2), randn(z, d) / sqrt(z / 2), randn(z, d) / sqrt(z / 2), randn(d, n) / sqrt(d / 2)
self.bi, self.bf, self.bo, self.bc, self.by = randn(d, 1), randn(d, 1), randn(d, 1), randn(d, 1), randn(n, 1)
self.dWi, self.dWf, self.dWo, self.dWc, self.dWy = zeros((z, d)), zeros((z, d)), zeros((z, d)), zeros((z, d)), zeros((d, n))
self.dbi, self.dbf, self.dbo, self.dbc, self.dby = zeros((d, 1)), zeros((d, 1)), zeros((d, 1)), zeros((d, 1)), zeros((n, 1))
def FeedForward(self, inputs, ht_, ct_):
n, d, rl, Cells = self.n, self.d, self.RL, self.Cells
while len(Cells) < rl:
Cells.append(Cell(n, d, self))
for cell, x in zip(Cells, range(len(inputs))):
ht_, ct_ = cell.feedforward(x, ht_, ct_)
return ht_, ct_
def BPTT(self, outputs, ht1, ct1):
n, d, z, rl = self.n, self.d, self.n + self.d, self.RL
Cells = self.Cells
avg_loss = 0
for i in reversed(range(rl)):
loss, ht1, ct1 = Cells[i].backpropagate(outputs[i], ht1, ct1)
avg_loss += loss
avg_loss /= rl
return avg_loss, ht1, ct1
def train(self, inputs, outputs):
n, d, z, rl = self.n, self.d, self.n + self.d, self.RL
index = 0
LR = 0.1
loss = 0
ht_, ct_ = zeros((d, 1)), zeros((d, 1))
ht1, ct1 = zeros((d, 1)), zeros((d, 1))
while index < len(outputs):
xlist = inputs[index:index + rl]
ylist = outputs[index:index + rl]
ht_, ct_ = self.FeedForward(xlist, ht_, ct_)
loss, ht1, ct1 = self.BPTT(ylist, ht1, ct1)
#print(loss)
self.update(LR)
index += rl
def update(self, LR):
n, d, z = self.n, self.d, self.n + self.d
self.Wi -= LR * self.dWi
self.Wf -= LR * self.dWf
self.Wo -= LR * self.dWo
self.Wc -= LR * self.dWc
self.Wy -= LR * self.dWy
self.bi -= LR * self.dbi
self.bf -= LR * self.dbf
self.bo -= LR * self.dbo
self.bc -= LR * self.dbc
self.by -= LR * self.dby
self.dWi, self.dWf, self.dWo, self.dWc, self.dWy = zeros((z, d)), zeros((z, d)), zeros((z, d)), zeros((z, d)), zeros((d, n))
self.dbi, self.dbf, self.dbo, self.dbc, self.dby = zeros((d, 1)), zeros((d, 1)), zeros((d, 1)), zeros((d, 1)), zeros((n, 1))
类Cell：
def __init__(self, n, d, rnn):
"""Pass the input size (n) and memory cell size (d), create hidden state of size d, pass rnn (self)"""
self.n, self.d, self.h, self.z, z = n, d, zeros((d, 1)), n + d, n + d
self.rnn = rnn
def feedforward(self, x, c_, h_):
"""Pass an input of size n, the previous hidden state(ht), and the previous cell state(c)"""
n, d = self.n, self.d
Wi, Wf, Wo, Wc, Wy = self.rnn.Wi, self.rnn.Wf, self.rnn.Wo, self.rnn.Wc, self.rnn.Wy
bi, bf, bo, bc, by = self.rnn.bi, self.rnn.bf, self.rnn.bo, self.rnn.bc, self.rnn.by
index = x # one hot encoding
x = zeros((n, 1))
x[index] = 1
g = concat((x, h_)) # input g is input x + previous hidden state
it = sigmoid(dot(Wi.T, g) + bi) # gate activations
ft = sigmoid(dot(Wf.T, g) + bf)
ot = sigmoid(dot(Wo.T, g) + bo)
ct = tanh(dot(Wc.T, g) + bc) # non linearity activation
c = ft * c_ + it * ct # cell state
ht = ot * tanh(c) # squashed hidden state
yt = dot(Wy.T, ht) + by # output state
p = softmax(yt) # call softmax, get probability
self.c_, self.h_ = c_, h_
self.it, self.ft, self.ot, self.ct = it, ft, ot, ct
self.c, self.ht, self.yt, self.p, self.g = c, ht, yt, p, g
return ht, c
def backpropagate(self, y, ht1, ct1):
n, d = self.n, self.d
Wi, Wf, Wo, Wc, Wy = self.rnn.Wi, self.rnn.Wf, self.rnn.Wo, self.rnn.Wc, self.rnn.Wy
dWi, dWf, dWo, dWc, dWy = self.rnn.dWi, self.rnn.dWf, self.rnn.dWo, self.rnn.dWc, self.rnn.dWy
dbi, dbf, dbo, dbc, dby = self.rnn.dbi, self.rnn.dbf, self.rnn.dbo, self.rnn.dbc, self.rnn.dby
c_, h_ = self.c_, self.h_
it, ft, ot, ct = self.it, self.ft, self.ot, self.ct
c, ht, yt, p = self.c, self.ht, self.yt, self.p
g = self.g
dy = copy(p)
dy[y] -= 1
loss = cross_ent(p, y)
dh = dot(Wy, dy) + ht1
dh = clip(dh, -6, 6)
do = tanh(c) * dh
do = dsigmoid(ot) * do
dc = ot * dh * dtanh(c)
dc = dc + ct1
dc = clip(dc, -6, 6)
df = c_ * dc
df = dsigmoid(ft) * df
di = ct * dc
di = dsigmoid(it) * di
dct = it * dc
dct = dtanh(ct) * dct
dWf += dot(g, df.T)
dWi += dot(g, di.T)
dWo += dot(g, do.T)
dWc += dot(g, dc.T)
dWy += dot(ht, dy.T)
dbf += df
dbi += di
dbo += do
dbc += dc
dby += dy
dxi = dot(Wi, di)
dxf = dot(Wf, df)
dxo = dot(Wo, do)
dxc = dot(Wc, dct)
dx = dxf + dxi + dxo + dxc
dht1 = dx[n:]
dct1 = ft * dc
return loss, dht1, dct1
file = open("trumptweets.txt", 'r', encoding='utf8').read()
text = list(file)
alphabet = list(set(text))
n = (len(alphabet))
d = 100
encode = {ch:i for i,ch in enumerate(alphabet)}
decode = {i:ch for i,ch in enumerate(alphabet)}
inputs = [encode[ch] for ch in text]
outputs = [inputs[i + 1] for i in range(len(inputs)-1)]
RNN = LSTM.RNN(n, d, 100, 0.1)
RNN.train(inputs, outputs)
随着网络的训练，损失会有所增加。我不确定该错误是什么，因为我无法找到关于LSTM如何工作的任何确定来源。我此时已经实施了5个不完整，破损或不正确的指南。
对于那些不想阅读我的代码的人，我遵循以下算法：
答案 0 :(得分：0)
缺少的是正确的更新规则。我主要遵循的指南使用ADAM，所以我从研究论文中实现了它并且它有效。
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