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layer.py
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# Copyright 2015 Matthieu Courbariaux
# This file is part of BinaryConnect.
# BinaryConnect is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# BinaryConnect is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with BinaryConnect. If not, see <http://www.gnu.org/licenses/>.
import gzip
import cPickle
import numpy as np
import os
import os.path
import sys
import theano
import theano.tensor as T
import theano.printing as P
from theano import pp
import time
import scipy.stats
# for convolution layers
# from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs
# from theano.sandbox.cuda.basic_ops import gpu_contiguous
# from pylearn2.sandbox.cuda_convnet.pool import MaxPool
class linear_layer(object):
def __init__(self, rng, n_inputs, n_units,
BN=False, BN_epsilon=1e-4,
dropout=1.,
binary_training=False, stochastic_training=False,
binary_test=False, stochastic_test=0):
self.rng = rng
self.n_units = n_units
print " n_units = "+str(n_units)
self.n_inputs = n_inputs
print " n_inputs = "+str(n_inputs)
self.BN = BN
print " BN = "+str(BN)
self.BN_epsilon = BN_epsilon
print " BN_epsilon = "+str(BN_epsilon)
self.dropout = dropout
print " dropout = "+str(dropout)
self.binary_training = binary_training
print " binary_training = "+str(binary_training)
self.stochastic_training = stochastic_training
print " stochastic_training = "+str(stochastic_training)
self.binary_test = binary_test
print " binary_test = "+str(binary_test)
self.stochastic_test = stochastic_test
print " stochastic_test = "+str(stochastic_test)
self.high = np.float32(np.sqrt(6. / (n_inputs + n_units)))
self.W0 = np.float32(self.high/2)
W_values = np.asarray(self.rng.uniform(low=-self.high,high=self.high,size=(n_inputs, n_units)),dtype=theano.config.floatX)
b_values = np.zeros((n_units), dtype=theano.config.floatX)
a_values = np.ones((n_units), dtype=theano.config.floatX)
# creation of shared symbolic variables
# shared variables are the state of the built function
# in practice, we put them in the GPU memory
self.W = theano.shared(value=W_values, name='W')
self.b = theano.shared(value=b_values, name='b')
self.a = theano.shared(value=a_values, name='a')
self.mean = theano.shared(value=b_values, name='mean')
self.var = theano.shared(value=b_values, name='var')
self.n_samples = theano.shared(value=np.float32(0),name='n_samples')
# momentum
self.update_W = theano.shared(value=np.zeros((n_inputs, n_units), dtype=theano.config.floatX), name='update_W')
self.update_b = theano.shared(value=b_values, name='update_b')
def activation(self, z):
return z
def hard_sigm(self,x):
return T.clip((x+1)/2,0,1)
def binarize_weights(self,W,eval):
binary_deterministic_training = (self.binary_training == True) and (self.stochastic_training == False)
binary_stochastic_training = (self.binary_training == True) and (self.stochastic_training == True)
binary_deterministic_test = (self.binary_test == True) and (self.stochastic_test == False)
binary_stochastic_test = (self.binary_test == True) and (self.stochastic_test == True)
binary_deterministic = ((binary_deterministic_training == True) and (eval==False)) or ((binary_deterministic_test==True) and (eval==True))
binary_stochastic = ((binary_stochastic_training == True) and (eval==False)) or ((binary_stochastic_test==True) and (eval==True))
# print " binary_training = "+str(self.binary_training)
# print " stochastic_training = "+str(self.stochastic_training)
# print " binary_test = "+str(self.binary_test)
# print " stochastic_test = "+str(self.stochastic_test)
# print " eval = "+str(eval)
# print " binary_deterministic = "+str(binary_deterministic)
# print " binary_stochastic = "+str(binary_stochastic)
# Binary weights
# I could scale x or z instead of W
# and the dot product would become an accumulation
# I am not doing it to keep the code simple
if binary_deterministic == True:
# in the round to nearest case, we use binary weights during eval and training
# [?,?] -> -W0 or W0
Wb = T.switch(T.ge(W,0),self.W0,-self.W0)
elif binary_stochastic == True:
# apply hard sigmoid to get the probability
# [?,?] -> [0,1]
p = self.hard_sigm(W/self.W0)
# much slower :(
# srng = T.shared_randomstreams.RandomStreams(rng.randint(999999))
# much faster :)
# https://github.com/Theano/Theano/issues/1233#event-262671708
# does it work though ?? It seems so :)
srng = theano.sandbox.rng_mrg.MRG_RandomStreams(self.rng.randint(999999))
# Bernouilli distribution = binomial with n = 1
p_mask = T.cast(srng.binomial(n=1, p=p, size=T.shape(W)), theano.config.floatX)
# [0,1] -> -W0 or W0
Wb = T.switch(p_mask,self.W0,-self.W0)
# print "OK"
# continuous weights
else:
Wb = W
return Wb
def fprop(self, x, can_fit, eval):
# shape the input as a matrix (batch_size, n_inputs)
self.x = x.flatten(2)
# apply dropout mask
if self.dropout < 1.:
if eval == False:
# The cast is important because
# int * float32 = float64 which pulls things off the gpu
# very slow ??
# srng = T.shared_randomstreams.RandomStreams(self.rng.randint(999999))
srng = theano.sandbox.rng_mrg.MRG_RandomStreams(self.rng.randint(999999))
mask = T.cast(srng.binomial(n=1, p=self.dropout, size=T.shape(self.x)), theano.config.floatX)
# apply the mask
self.x = self.x * mask
else:
self.x = self.x * self.dropout
# binarize the weights
self.Wb = self.binarize_weights(self.W,eval)
z = T.dot(self.x, self.Wb)
# for BN updates
self.z = z
# batch normalization
if self.BN == True:
self.batch_mean = T.mean(z,axis=0)
self.batch_var = T.var(z,axis=0)
if can_fit == True:
mean = self.batch_mean
var = self.batch_var
else:
mean = self.mean
var = self.var
z = (z - mean)/(T.sqrt(var+self.BN_epsilon))
z = self.a * z
self.z = z + self.b
# activation function
y = self.activation(self.z)
return y
def quantized_bprop(self, cost):
"""
bprop equals:
(active_prime) *elem_multiply* error_signal_in * (rep of previous layer)
(rep of previous layer) is recoded as self.x during fprop() process.
Here we quantize (rep of previous layer) and leave the rest as it is.
"""
# the lower 2**(integer power)
index_low = T.switch(self.x > 0., T.floor(T.log2(self.x)), T.floor(T.log2(-self.x)))
sign = T.switch(self.x > 0., 1., -1.)
# index_up = index_low + 1 # the upper 2**(integer power) though not used explicitly.
p_up = sign * self.x / 2**(index_low) - 1 # percentage of upper index.
srng = theano.sandbox.rng_mrg.MRG_RandomStreams(self.rng.randint(999999))
index_random = index_low + srng.binomial(n=1, p=p_up, size=T.shape(self.x), dtype=theano.config.floatX)
quantized_rep = sign * 2**index_random
# there is sth wrong with this self-made backprop:
# the code is using BN, but this type of explicitly computation is not considering
# gradients caused by BN.
# error = self.activation_prime(self.z) * error_signal_in
error = T.grad(cost=cost, wrt=self.z)
self.dEdW = T.dot(quantized_rep.T, error)
self.dEdb = T.grad(cost=cost, wrt=self.b)
if self.BN == True:
self.dEda = T.grad(cost=cost, wrt=self.a)
def bprop(self, cost):
if self.binary_training == True:
dEdWb = T.grad(cost=cost, wrt=self.Wb)
self.dEdW = dEdWb
else:
self.dEdW = T.grad(cost=cost, wrt=self.W)
self.dEdb = T.grad(cost=cost, wrt=self.b)
if self.BN == True:
self.dEda = T.grad(cost=cost, wrt=self.a)
def parameters_updates(self, LR, M):
updates = []
# compute updates
new_update_W = M * self.update_W - LR * self.dEdW
new_update_b = M * self.update_b - LR * self.dEdb
# compute new parameters. Note that we use a better precision than the other operations
new_W = self.W + new_update_W
new_b = self.b + new_update_b
# clip the new weights when using binary weights
# it is equivalent to doing 2 things:
# 1) clip the weights during propagations
# 2) backprop the clip function with a rule for the boundaries:
# if W is equal to W0, then I can only reduce W
# if W is equal to -W0, then I can only augment W
if self.binary_training==True:
new_W = T.clip(new_W, -self.W0, self.W0)
updates.append((self.W, new_W))
updates.append((self.b, new_b))
updates.append((self.update_W, new_update_W))
updates.append((self.update_b, new_update_b))
if self.BN == True:
new_a = self.a - LR * self.dEda
updates.append((self.a, new_a))
return updates
def BN_updates(self):
updates = []
# batch_size = T.shape(self.z)[0]
new_n_samples = self.n_samples + 1
new_mean = (self.n_samples/new_n_samples) * self.mean + (1/new_n_samples) * self.batch_mean
# very sligthly biased variance estimation
new_var = (self.n_samples/new_n_samples) * self.var + (1/new_n_samples) * self.batch_var
updates.append((self.n_samples, new_n_samples))
updates.append((self.mean, new_mean))
updates.append((self.var, new_var))
return updates
def BN_reset(self):
updates = []
updates.append((self.mean, self.mean*0.))
updates.append((self.var, self.var*0.))
updates.append((self.n_samples, self.n_samples*0.))
return updates
class ReLU_layer(linear_layer):
def activation(self,z):
return T.maximum(0.,z)
class Maxout_layer(linear_layer):
def __init__(self, rng, n_inputs, n_units, n_pieces,
BN=False, BN_epsilon=1e-4,
dropout=1.,
binary_training=False, stochastic_training=False,
binary_test=False, stochastic_test=0):
linear_layer.__init__(self, rng=rng, n_inputs=n_inputs,
n_units = n_units*n_pieces,
BN=BN, BN_epsilon=BN_epsilon,
dropout=dropout,
binary_training=binary_training, stochastic_training=stochastic_training,
binary_test=binary_test, stochastic_test=stochastic_test)
self.n_pieces = n_pieces
def activation(self,z):
y = T.reshape(z,(T.shape(z)[0], self.n_units//self.n_pieces, self.n_pieces))
y = T.max(y,axis=2)
y = T.reshape(y,(T.shape(z)[0],self.n_units//self.n_pieces))
return y
class conv_layer(linear_layer):
def __init__(self, rng,
# image_shape,
filter_shape, pool_shape, pool_stride,
BN, BN_epsilon=1e-4,
binary_training=False, stochastic_training=False,
binary_test=False, stochastic_test=0):
self.rng = rng
# self.image_shape = image_shape
# print " image_shape = "+str(image_shape)
self.filter_shape = filter_shape
print " filter_shape = "+str(filter_shape)
self.pool_shape = pool_shape
print " pool_shape = "+str(pool_shape)
self.pool_stride = pool_stride
print " pool_stride = "+str(pool_stride)
self.BN = BN
print " BN = "+str(BN)
self.BN_epsilon = BN_epsilon
print " BN_epsilon = "+str(BN_epsilon)
# self.W_lr_scale = W_lr_scale
# print " W_lr_scale = "+str(W_lr_scale)
self.binary_training = binary_training
print " binary_training = "+str(binary_training)
self.stochastic_training = stochastic_training
print " stochastic_training = "+str(stochastic_training)
self.binary_test = binary_test
print " binary_test = "+str(binary_test)
self.stochastic_test = stochastic_test
print " stochastic_test = "+str(stochastic_test)
# range of init
n_inputs = np.prod(filter_shape[1:])
n_units = (filter_shape[0] * np.prod(filter_shape[2:])/ np.prod(pool_shape))
# initialize weights with random weights
self.high = np.float32(np.sqrt(6. / (n_inputs + n_units)))
self.W0 = np.float32(self.high/2)
# filters parameters
W_values = np.asarray(rng.uniform(low=-self.high, high=self.high, size=self.filter_shape),dtype=theano.config.floatX)
self.W = theano.shared(W_values)
# the bias is a 1D tensor -- one bias per output feature map
b_values = np.zeros((self.filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values)
# BN stuff
a_values = np.ones((self.filter_shape[0],), dtype=theano.config.floatX)
self.a = theano.shared(value=a_values, name='a')
self.mean = theano.shared(value=b_values, name='mean')
self.var = theano.shared(value=b_values, name='var')
self.n_samples = theano.shared(value=np.float32(0),name='n_samples')
# momentum
self.update_W = theano.shared(value=np.zeros(self.filter_shape, dtype=theano.config.floatX), name='update_W')
self.update_b = theano.shared(value=b_values, name='update_b')
def fprop(self, x, can_fit, eval):
# shape the input as it should be (not necessary)
# x = x.reshape(self.image_shape)
self.x = x
# binarize the weights
self.Wb = self.binarize_weights(self.W,eval)
# self.Wb = self.W
# convolution
z = T.nnet.conv.conv2d(x, self.Wb, border_mode='valid')
self.conv_z = z
# Maxpooling
if self.pool_shape != (1,1):
z = T.signal.downsample.max_pool_2d(input=z, ds=self.pool_shape, st=self.pool_stride)
# for BN
self.z = z
# batch normalization
if self.BN == True:
# in the convolutional case, there is only a mean per feature map and not per location
# http://arxiv.org/pdf/1502.03167v3.pdf
self.batch_mean = T.mean(z,axis=(0,2,3))
self.batch_var = T.var(z,axis=(0,2,3))
if can_fit == True:
mean = self.batch_mean
var = self.batch_var
else:
mean = self.mean
var = self.var
z = (z - mean.dimshuffle('x', 0, 'x', 'x'))/(T.sqrt(var.dimshuffle('x', 0, 'x', 'x')+self.BN_epsilon))
z = self.a.dimshuffle('x', 0, 'x', 'x') * z
# bias
z = z + self.b.dimshuffle('x', 0, 'x', 'x')
# activation
y = self.activation(z)
return y
def quantized_bprop(self, cost):
"""
bprop for convolution layer equals:
(
self.x.dimshuffle(1, 0, 2, 3) (*)
T.grad(cost, wrt=#convoutput).dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1]
).dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1]
'(*)'stands for convolution.
Here we quantize (rep of previous layer) and leave the rest as it is.
"""
# the lower 2**(integer power)
index_low = T.switch(self.x > 0., T.floor(T.log2(self.x)), T.floor(T.log2(-self.x)))
index_low = T.clip(index_low, -4, 3)
sign = T.switch(self.x > 0., 1., -1.)
#index_up = index_low + 1 # the upper 2**(integer power) though not used explicitly.
p_up = sign * self.x / 2**(index_low) - 1 # percentage of upper index.
srng = theano.sandbox.rng_mrg.MRG_RandomStreams(self.rng.randint(999999))
index_random = index_low + srng.binomial(n=1, p=p_up, size=T.shape(self.x), dtype=theano.config.floatX)
quantized_rep = sign * 2**index_random
error = T.grad(cost=cost, wrt=self.conv_z)
self.dEdW = T.nnet.conv.conv2d(
input=quantized_rep.dimshuffle(1, 0, 2, 3),
filters=error.dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1]
).dimshuffle(1, 0, 2, 3)[:, :, ::-1, ::-1]
self.dEdb = T.grad(cost=cost, wrt=self.b)
if self.BN == True:
self.dEda = T.grad(cost=cost, wrt=self.a)
class ReLU_conv_layer(conv_layer):
def activation(self,z):
return T.maximum(0.,z)
class Maxout_conv_layer(conv_layer):
def __init__(self, rng,
filter_shape, pool_shape, pool_stride, n_pieces,
BN, BN_epsilon=1e-4,
binary_training=False, stochastic_training=False,
binary_test=False, stochastic_test=0):
new_filter_shape = (filter_shape[0]*n_pieces,filter_shape[1],filter_shape[2],filter_shape[3])
conv_layer.__init__(self, rng=rng,
filter_shape=new_filter_shape, pool_shape=pool_shape, pool_stride=pool_stride,
BN=BN, BN_epsilon=BN_epsilon,
binary_training=binary_training, stochastic_training=stochastic_training,
binary_test=binary_test, stochastic_test=stochastic_test)
self.n_pieces = n_pieces
def activation(self,z):
z = T.reshape(z,(T.shape(z)[0], T.shape(z)[1]//self.n_pieces, self.n_pieces,T.shape(z)[2],T.shape(z)[3]))
return T.max(z,axis=2)