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LSTM_bidi.py
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LSTM_bidi.py
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'''
@author: Leila Arras
@maintainer: Leila Arras
@date: 21.06.2017
@version: 1.0+
@copyright: Copyright (c) 2017, Leila Arras, Gregoire Montavon, Klaus-Robert Mueller, Wojciech Samek
@license: see LICENSE file in repository root
'''
import numpy as np
import pickle
from numpy import newaxis as na
from code.LSTM.LRP_linear_layer import *
class LSTM_bidi:
def __init__(self, model_path='./model/'):
"""
Load trained model from file.
"""
# vocabulary
f_voc = open(model_path + "vocab", 'rb')
self.voc = pickle.load(f_voc)
f_voc.close()
# word embeddings
self.E = np.load(model_path + 'embeddings.npy', mmap_mode='r') # shape V*e
# model weights
f_model = open(model_path + 'model', 'rb')
model = pickle.load(f_model)
f_model.close()
# LSTM left encoder
self.Wxh_Left = model["Wxh_Left"] # shape 4d*e
self.bxh_Left = model["bxh_Left"] # shape 4d
self.Whh_Left = model["Whh_Left"] # shape 4d*d
self.bhh_Left = model["bhh_Left"] # shape 4d
# LSTM right encoder
self.Wxh_Right = model["Wxh_Right"]
self.bxh_Right = model["bxh_Right"]
self.Whh_Right = model["Whh_Right"]
self.bhh_Right = model["bhh_Right"]
# linear output layer
self.Why_Left = model["Why_Left"] # shape C*d
self.Why_Right = model["Why_Right"] # shape C*d
def set_input(self, w, delete_pos=None):
"""
Build the numerical input sequence x/x_rev from the word indices w (+ initialize hidden layers h, c).
Optionally delete words at positions delete_pos.
"""
T = len(w) # sequence length
d = int(self.Wxh_Left.shape[0]/4) # hidden layer dimension
e = self.E.shape[1] # word embedding dimension
x = np.zeros((T, e))
x[:,:] = self.E[w,:]
if delete_pos is not None:
x[delete_pos, :] = np.zeros((len(delete_pos), e))
self.w = w
self.x = x
self.x_rev = x[::-1,:].copy()
self.h_Left = np.zeros((T+1, d))
self.c_Left = np.zeros((T+1, d))
self.h_Right = np.zeros((T+1, d))
self.c_Right = np.zeros((T+1, d))
def forward(self):
"""
Standard forward pass.
Compute the hidden layer values (assuming input x/x_rev was previously set)
"""
T = len(self.w)
d = int(self.Wxh_Left.shape[0]/4)
# gate indices (assuming the gate ordering in the LSTM weights is i,g,f,o):
idx = np.hstack((np.arange(0,d), np.arange(2*d,4*d))).astype(int) # indices of gates i,f,o together
idx_i, idx_g, idx_f, idx_o = np.arange(0,d), np.arange(d,2*d), np.arange(2*d,3*d), np.arange(3*d,4*d) # indices of gates i,g,f,o separately
# initialize
self.gates_xh_Left = np.zeros((T, 4*d))
self.gates_hh_Left = np.zeros((T, 4*d))
self.gates_pre_Left = np.zeros((T, 4*d)) # gates pre-activation
self.gates_Left = np.zeros((T, 4*d)) # gates activation
self.gates_xh_Right = np.zeros((T, 4*d))
self.gates_hh_Right = np.zeros((T, 4*d))
self.gates_pre_Right= np.zeros((T, 4*d))
self.gates_Right = np.zeros((T, 4*d))
for t in range(T):
self.gates_xh_Left[t] = np.dot(self.Wxh_Left, self.x[t])
self.gates_hh_Left[t] = np.dot(self.Whh_Left, self.h_Left[t-1])
self.gates_pre_Left[t] = self.gates_xh_Left[t] + self.gates_hh_Left[t] + self.bxh_Left + self.bhh_Left
self.gates_Left[t,idx] = 1.0/(1.0 + np.exp(- self.gates_pre_Left[t,idx]))
self.gates_Left[t,idx_g] = np.tanh(self.gates_pre_Left[t,idx_g])
self.c_Left[t] = self.gates_Left[t,idx_f]*self.c_Left[t-1] + self.gates_Left[t,idx_i]*self.gates_Left[t,idx_g]
self.h_Left[t] = self.gates_Left[t,idx_o]*np.tanh(self.c_Left[t])
self.gates_xh_Right[t] = np.dot(self.Wxh_Right, self.x_rev[t])
self.gates_hh_Right[t] = np.dot(self.Whh_Right, self.h_Right[t-1])
self.gates_pre_Right[t] = self.gates_xh_Right[t] + self.gates_hh_Right[t] + self.bxh_Right + self.bhh_Right
self.gates_Right[t,idx] = 1.0/(1.0 + np.exp(- self.gates_pre_Right[t,idx]))
self.gates_Right[t,idx_g] = np.tanh(self.gates_pre_Right[t,idx_g])
self.c_Right[t] = self.gates_Right[t,idx_f]*self.c_Right[t-1] + self.gates_Right[t,idx_i]*self.gates_Right[t,idx_g]
self.h_Right[t] = self.gates_Right[t,idx_o]*np.tanh(self.c_Right[t])
self.y_Left = np.dot(self.Why_Left, self.h_Left[T-1])
self.y_Right = np.dot(self.Why_Right, self.h_Right[T-1])
self.s = self.y_Left + self.y_Right
return self.s.copy() # prediction scores
def backward(self, w, sensitivity_class):
"""
Standard gradient backpropagation backward pass.
Compute the hidden layer gradients by backpropagating a gradient of 1.0 for the class sensitivity_class
"""
# forward pass
self.set_input(w)
self.forward()
T = len(self.w)
d = int(self.Wxh_Left.shape[0]/4)
C = self.Why_Left.shape[0] # number of classes
idx = np.hstack((np.arange(0,d), np.arange(2*d,4*d))).astype(int) # indices of gates i,f,o together
idx_i, idx_g, idx_f, idx_o = np.arange(0,d), np.arange(d,2*d), np.arange(2*d,3*d), np.arange(3*d,4*d) # indices of gates i,g,f,o separately
# initialize
self.dx = np.zeros(self.x.shape)
self.dx_rev = np.zeros(self.x.shape)
self.dh_Left = np.zeros((T+1, d))
self.dc_Left = np.zeros((T+1, d))
self.dgates_pre_Left = np.zeros((T, 4*d)) # gates pre-activation
self.dgates_Left = np.zeros((T, 4*d)) # gates activation
self.dh_Right = np.zeros((T+1, d))
self.dc_Right = np.zeros((T+1, d))
self.dgates_pre_Right = np.zeros((T, 4*d))
self.dgates_Right = np.zeros((T, 4*d))
ds = np.zeros((C))
ds[sensitivity_class] = 1.0
dy_Left = ds.copy()
dy_Right = ds.copy()
self.dh_Left[T-1] = np.dot(self.Why_Left.T, dy_Left)
self.dh_Right[T-1] = np.dot(self.Why_Right.T, dy_Right)
for t in reversed(range(T)):
self.dgates_Left[t,idx_o] = self.dh_Left[t] * np.tanh(self.c_Left[t]) # do[t]
self.dc_Left[t] += self.dh_Left[t] * self.gates_Left[t,idx_o] * (1.-(np.tanh(self.c_Left[t]))**2) # dc[t]
self.dgates_Left[t,idx_f] = self.dc_Left[t] * self.c_Left[t-1] # df[t]
self.dc_Left[t-1] = self.dc_Left[t] * self.gates_Left[t,idx_f] # dc[t-1]
self.dgates_Left[t,idx_i] = self.dc_Left[t] * self.gates_Left[t,idx_g] # di[t]
self.dgates_Left[t,idx_g] = self.dc_Left[t] * self.gates_Left[t,idx_i] # dg[t]
self.dgates_pre_Left[t,idx] = self.dgates_Left[t,idx] * self.gates_Left[t,idx] * (1.0 - self.gates_Left[t,idx]) # d ifo pre[t]
self.dgates_pre_Left[t,idx_g]= self.dgates_Left[t,idx_g] * (1.-(self.gates_Left[t,idx_g])**2) # d g pre[t]
self.dh_Left[t-1] = np.dot(self.Whh_Left.T, self.dgates_pre_Left[t])
self.dx[t] = np.dot(self.Wxh_Left.T, self.dgates_pre_Left[t])
self.dgates_Right[t,idx_o] = self.dh_Right[t] * np.tanh(self.c_Right[t])
self.dc_Right[t] += self.dh_Right[t] * self.gates_Right[t,idx_o] * (1.-(np.tanh(self.c_Right[t]))**2)
self.dgates_Right[t,idx_f] = self.dc_Right[t] * self.c_Right[t-1]
self.dc_Right[t-1] = self.dc_Right[t] * self.gates_Right[t,idx_f]
self.dgates_Right[t,idx_i] = self.dc_Right[t] * self.gates_Right[t,idx_g]
self.dgates_Right[t,idx_g] = self.dc_Right[t] * self.gates_Right[t,idx_i]
self.dgates_pre_Right[t,idx] = self.dgates_Right[t,idx] * self.gates_Right[t,idx] * (1.0 - self.gates_Right[t,idx])
self.dgates_pre_Right[t,idx_g]= self.dgates_Right[t,idx_g] * (1.-(self.gates_Right[t,idx_g])**2)
self.dh_Right[t-1] = np.dot(self.Whh_Right.T, self.dgates_pre_Right[t])
self.dx_rev[t] = np.dot(self.Wxh_Right.T, self.dgates_pre_Right[t])
return self.dx.copy(), self.dx_rev[::-1,:].copy()
def lrp(self, w, LRP_class, eps=0.001, bias_factor=0.0):
"""
Layer-wise Relevance Propagation (LRP) backward pass.
Compute the hidden layer relevances by performing LRP for the target class LRP_class
(according to the papers:
- https://doi.org/10.1371/journal.pone.0130140
- https://doi.org/10.18653/v1/W17-5221 )
"""
# forward pass
self.set_input(w)
self.forward()
T = len(self.w)
d = int(self.Wxh_Left.shape[0]/4)
e = self.E.shape[1]
C = self.Why_Left.shape[0] # number of classes
idx = np.hstack((np.arange(0,d), np.arange(2*d,4*d))).astype(int) # indices of gates i,f,o together
idx_i, idx_g, idx_f, idx_o = np.arange(0,d), np.arange(d,2*d), np.arange(2*d,3*d), np.arange(3*d,4*d) # indices of gates i,g,f,o separately
# initialize
Rx = np.zeros(self.x.shape)
Rx_rev = np.zeros(self.x.shape)
Rh_Left = np.zeros((T+1, d))
Rc_Left = np.zeros((T+1, d))
Rg_Left = np.zeros((T, d)) # gate g only
Rh_Right = np.zeros((T+1, d))
Rc_Right = np.zeros((T+1, d))
Rg_Right = np.zeros((T, d)) # gate g only
Rout_mask = np.zeros((C))
Rout_mask[LRP_class] = 1.0
# format reminder: lrp_linear(hin, w, b, hout, Rout, bias_nb_units, eps, bias_factor)
Rh_Left[T-1] = lrp_linear(self.h_Left[T-1], self.Why_Left.T , np.zeros((C)), self.s, self.s*Rout_mask, 2*d, eps, bias_factor, debug=False)
Rh_Right[T-1] = lrp_linear(self.h_Right[T-1], self.Why_Right.T, np.zeros((C)), self.s, self.s*Rout_mask, 2*d, eps, bias_factor, debug=False)
for t in reversed(range(T)):
Rc_Left[t] += Rh_Left[t]
Rc_Left[t-1] = lrp_linear(self.gates_Left[t,idx_f]*self.c_Left[t-1], np.identity(d), np.zeros((d)), self.c_Left[t], Rc_Left[t], 2*d, eps, bias_factor, debug=False)
Rg_Left[t] = lrp_linear(self.gates_Left[t,idx_i]*self.gates_Left[t,idx_g], np.identity(d), np.zeros((d)), self.c_Left[t], Rc_Left[t], 2*d, eps, bias_factor, debug=False)
Rx[t] = lrp_linear(self.x[t], self.Wxh_Left[idx_g].T, self.bxh_Left[idx_g]+self.bhh_Left[idx_g], self.gates_pre_Left[t,idx_g], Rg_Left[t], d+e, eps, bias_factor, debug=False)
Rh_Left[t-1] = lrp_linear(self.h_Left[t-1], self.Whh_Left[idx_g].T, self.bxh_Left[idx_g]+self.bhh_Left[idx_g], self.gates_pre_Left[t,idx_g], Rg_Left[t], d+e, eps, bias_factor, debug=False)
Rc_Right[t] += Rh_Right[t]
Rc_Right[t-1] = lrp_linear(self.gates_Right[t,idx_f]*self.c_Right[t-1], np.identity(d), np.zeros((d)), self.c_Right[t], Rc_Right[t], 2*d, eps, bias_factor, debug=False)
Rg_Right[t] = lrp_linear(self.gates_Right[t,idx_i]*self.gates_Right[t,idx_g], np.identity(d), np.zeros((d)), self.c_Right[t], Rc_Right[t], 2*d, eps, bias_factor, debug=False)
Rx_rev[t] = lrp_linear(self.x_rev[t], self.Wxh_Right[idx_g].T, self.bxh_Right[idx_g]+self.bhh_Right[idx_g], self.gates_pre_Right[t,idx_g], Rg_Right[t], d+e, eps, bias_factor, debug=False)
Rh_Right[t-1] = lrp_linear(self.h_Right[t-1], self.Whh_Right[idx_g].T, self.bxh_Right[idx_g]+self.bhh_Right[idx_g], self.gates_pre_Right[t,idx_g], Rg_Right[t], d+e, eps, bias_factor, debug=False)
return Rx, Rx_rev[::-1,:], Rh_Left[-1].sum()+Rc_Left[-1].sum()+Rh_Right[-1].sum()+Rc_Right[-1].sum()