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model_53.py
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model_53.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial, validate_shape=False)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial, validate_shape=False)
class Model:
def __init__(self, x, y_, weights, layers):
in_dim = int(x.get_shape()[1])
out_dim = int(y_.get_shape()[1])
self.x = x
self.var_list = []
self.var_list.append(weight_variable([in_dim, weights]))
self.var_list.append(bias_variable([weights]))
for layer in range(layers - 1):
self.var_list.append(weight_variable([weights, weights]))
self.var_list.append(bias_variable([weights]))
self.var_list.append(weight_variable([weights, out_dim]))
self.var_list.append(bias_variable([out_dim]))
self.architecture = []
self.architecture.append(x)
self.error_sum_array = [0]
count = 0
for i in range(layers):
self.architecture.append(tf.nn.relu(tf.matmul(self.architecture[i], self.var_list[i + count]) + self.var_list[i + count + 1]))
count += 1
self.architecture.append(tf.matmul(self.architecture[len(self.architecture) - 1], self.var_list[len(self.var_list) - 2]) + self.var_list[len(self.var_list) - 1])
# vanilla single-task loss
self.cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=self.architecture[len(self.architecture) - 1]))
self.set_vanilla_loss()
#the variables below are lists because of the expand function (see below)
self.correct_prediction = tf.equal(tf.argmax(self.architecture[len(self.architecture) - 1],1), tf.argmax(y_,1))
self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32))
def compute_fisher(self, imgset, sess, F_archives, num_samples=200):
# initialize Fisher information for most recent task
self.F_accum = []
for v in range(len(self.var_list)):
self.F_accum.append(np.zeros(self.var_list[v].eval().shape))
# sampling a random class from softmax
probs = tf.nn.softmax(self.architecture[len(self.architecture) - 1])
class_ind = tf.to_int32(tf.multinomial(tf.log(probs), 1)[0][0])
tensorGraphCreate = tf.gradients(tf.log(probs[0,class_ind]), self.var_list)
for i in range(num_samples):
# select random input image
im_ind = np.random.randint(imgset.shape[0])
# compute first-order derivatives
ders = sess.run(tensorGraphCreate, feed_dict={self.x: imgset[im_ind:im_ind+1]})
# square the derivatives and add to total
for v in range(len(self.F_accum)):
self.F_accum[v] += np.square(ders[v])
# divide totals by number of samples
for v in range(len(self.F_accum)):
self.F_accum[v] /= num_samples
F_archives.append(self.F_accum)
def star(self):
# used for saving optimal weights after most recent task training
self.star_vars = []
for v in range(len(self.var_list)):
self.star_vars.append(self.var_list[v].eval())
def restore(self, sess, expanding=False):
# reassign optimal weights for latest task
if hasattr(self, "star_vars"):
if expanding == True:
expanded_vars = []
for model_var in range(len(self.var_list) - 1):
if model_var % 2 == 0:
expanded_vars.append(weight_variable(list(self.var_list[model_var].eval().shape)))
else:
expanded_vars.append(bias_variable(list(self.var_list[model_var].eval().shape)))
sess.run(tf.variables_initializer(expanded_vars))
for ex_var in range(len(expanded_vars)):
axis = -1
both_axes = False
if ex_var == 0:
axis = 1
elif ex_var == len(expanded_vars) - 1:
axis = 0
elif ex_var % 2 != 0:
axis = 0
else:
both_axes = True
if both_axes == False:
sess.run(tf.assign(self.var_list[ex_var], tf.concat([self.var_list[ex_var], expanded_vars[ex_var]], axis), validate_shape=False))
else:
sess.run(tf.assign(self.var_list[ex_var], tf.concat([self.var_list[ex_var], expanded_vars[ex_var]], 0), validate_shape=False))
sess.run(tf.assign(self.var_list[ex_var], tf.concat([self.var_list[ex_var], expanded_vars[ex_var]], 1), validate_shape=False))
for saved_var in range(len(self.star_vars) - 1):
axis = -1
both_axes = False
if saved_var == 0:
axis = 1
elif saved_var == len(self.star_vars) - 2:
axis = 0
elif saved_var % 2 != 0:
axis = 0
else:
both_axes = True
if both_axes == False:
self.star_vars[saved_var] = np.append(self.star_vars[saved_var], expanded_vars[saved_var].eval(), axis = axis)
else:
self.star_vars[saved_var] = np.append(self.star_vars[saved_var], expanded_vars[saved_var].eval(), axis = 0)
self.star_vars[saved_var] = np.append(self.star_vars[saved_var], expanded_vars[saved_var].eval(), axis = 1)
for v in range(len(self.star_vars)):
sess.run(tf.assign(self.var_list[v], self.star_vars[v]))
def set_vanilla_loss(self):
self.train_step = tf.train.GradientDescentOptimizer(0.1).minimize(self.cross_entropy)
def update_ewc_loss(self, lam, F_archives, dim_dict):
# elastic weight consolidation
# lam is weighting for previous task(s) constraints
self.ewc_loss = self.cross_entropy
expansion = 100
penalty = tf.constant(0.0)
if len(F_archives) <= expansion - 2:
for F_matrix in range(len(F_archives)):
for net_var in range(len(self.var_list)):
penalty += tf.reduce_sum(tf.multiply(F_archives[F_matrix][net_var].astype(np.float32),tf.square(self.var_list[net_var] - self.star_vars[net_var])))
self.ewc_loss += (lam/2.0) * penalty
else:
for F_matrix_index in range(expansion - 2):
for tensor in range(len(self.var_list) - 1):
if tensor % 2 == 0:
self.ewc_loss += (lam/2) * \
tf.reduce_sum(tf.multiply(F_archives[F_matrix_index][tensor].astype(np.float32),tf.square(tf.slice(self.var_list[tensor],
[0,0], dim_dict[expansion - 1][tensor]) -
tf.slice(self.star_vars[tensor], [0,0],
dim_dict[expansion - 1][tensor]))))
else:
self.ewc_loss += (lam/2) * \
tf.reduce_sum(tf.multiply(F_archives[F_matrix_index][tensor].astype(np.float32),tf.square(tf.slice(self.var_list[tensor],
[0], dim_dict[expansion - 1][tensor]) -
tf.slice(self.star_vars[tensor], [0],
dim_dict[expansion - 1][tensor]))))
self.ewc_loss += (lam/2) * \
tf.reduce_sum(tf.multiply(F_archives[F_matrix_index][len(F_archives[F_matrix_index])
-
1].astype(np.float32),tf.square(self.var_list[len(self.var_list)
- 1] - self.star_vars[len(self.star_vars) - 1])))
for v in range(len(self.var_list)):
for F_matrix_expanded in range(expansion - 2, len(F_archives)):
self.ewc_loss += (lam/2) * \
tf.reduce_sum(tf.multiply(F_archives[F_matrix_expanded][v].astype(np.float32),tf.square(self.var_list[v]
- self.star_vars[v])))
self.train_step = tf.train.GradientDescentOptimizer(0.1).minimize(self.ewc_loss)
self.penalty = penalty