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# to you under the Apache License, Version 2.0 (the
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# KIND, either express or implied. See the License for the
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# under the License.
# pylint: skip-file
import mxnet as mx
from mxnet import misc
import numpy as np
import model
import logging
from solver import Solver, Monitor
try:
import cPickle as pickle
except:
import pickle
class AutoEncoderModel(model.MXModel):
def setup(self, dims, sparseness_penalty=None, pt_dropout=None, ft_dropout=None, input_act=None, internal_act='relu', output_act=None):
self.N = len(dims) - 1
self.dims = dims
self.stacks = []
self.pt_dropout = pt_dropout
self.ft_dropout = ft_dropout
self.input_act = input_act
self.internal_act = internal_act
self.output_act = output_act
self.data = mx.symbol.Variable('data')
for i in range(self.N):
if i == 0:
decoder_act = input_act
idropout = None
else:
decoder_act = internal_act
idropout = pt_dropout
if i == self.N-1:
encoder_act = output_act
odropout = None
else:
encoder_act = internal_act
odropout = pt_dropout
istack, iargs, iargs_grad, iargs_mult, iauxs = self.make_stack(i, self.data, dims[i], dims[i+1],
sparseness_penalty, idropout, odropout, encoder_act, decoder_act)
self.stacks.append(istack)
self.args.update(iargs)
self.args_grad.update(iargs_grad)
self.args_mult.update(iargs_mult)
self.auxs.update(iauxs)
self.encoder, self.internals = self.make_encoder(self.data, dims, sparseness_penalty, ft_dropout, internal_act, output_act)
self.decoder = self.make_decoder(self.encoder, dims, sparseness_penalty, ft_dropout, internal_act, input_act)
if input_act == 'softmax':
self.loss = self.decoder
else:
self.loss = mx.symbol.LinearRegressionOutput(data=self.decoder, label=self.data)
def make_stack(self, istack, data, num_input, num_hidden, sparseness_penalty=None, idropout=None,
odropout=None, encoder_act='relu', decoder_act='relu'):
x = data
if idropout:
x = mx.symbol.Dropout(data=x, p=idropout)
x = mx.symbol.FullyConnected(name='encoder_%d'%istack, data=x, num_hidden=num_hidden)
if encoder_act:
x = mx.symbol.Activation(data=x, act_type=encoder_act)
if encoder_act == 'sigmoid' and sparseness_penalty:
x = mx.symbol.IdentityAttachKLSparseReg(data=x, name='sparse_encoder_%d' % istack, penalty=sparseness_penalty)
if odropout:
x = mx.symbol.Dropout(data=x, p=odropout)
x = mx.symbol.FullyConnected(name='decoder_%d'%istack, data=x, num_hidden=num_input)
if decoder_act == 'softmax':
x = mx.symbol.Softmax(data=x, label=data, prob_label=True, act_type=decoder_act)
elif decoder_act:
x = mx.symbol.Activation(data=x, act_type=decoder_act)
if decoder_act == 'sigmoid' and sparseness_penalty:
x = mx.symbol.IdentityAttachKLSparseReg(data=x, name='sparse_decoder_%d' % istack, penalty=sparseness_penalty)
x = mx.symbol.LinearRegressionOutput(data=x, label=data)
else:
x = mx.symbol.LinearRegressionOutput(data=x, label=data)
args = {'encoder_%d_weight'%istack: mx.nd.empty((num_hidden, num_input), self.xpu),
'encoder_%d_bias'%istack: mx.nd.empty((num_hidden,), self.xpu),
'decoder_%d_weight'%istack: mx.nd.empty((num_input, num_hidden), self.xpu),
'decoder_%d_bias'%istack: mx.nd.empty((num_input,), self.xpu),}
args_grad = {'encoder_%d_weight'%istack: mx.nd.empty((num_hidden, num_input), self.xpu),
'encoder_%d_bias'%istack: mx.nd.empty((num_hidden,), self.xpu),
'decoder_%d_weight'%istack: mx.nd.empty((num_input, num_hidden), self.xpu),
'decoder_%d_bias'%istack: mx.nd.empty((num_input,), self.xpu),}
args_mult = {'encoder_%d_weight'%istack: 1.0,
'encoder_%d_bias'%istack: 2.0,
'decoder_%d_weight'%istack: 1.0,
'decoder_%d_bias'%istack: 2.0,}
auxs = {}
if encoder_act == 'sigmoid' and sparseness_penalty:
auxs['sparse_encoder_%d_moving_avg' % istack] = mx.nd.ones((num_hidden), self.xpu) * 0.5
if decoder_act == 'sigmoid' and sparseness_penalty:
auxs['sparse_decoder_%d_moving_avg' % istack] = mx.nd.ones((num_input), self.xpu) * 0.5
init = mx.initializer.Uniform(0.07)
for k,v in args.items():
init(k,v)
return x, args, args_grad, args_mult, auxs
def make_encoder(self, data, dims, sparseness_penalty=None, dropout=None, internal_act='relu', output_act=None):
x = data
internals = []
N = len(dims) - 1
for i in range(N):
x = mx.symbol.FullyConnected(name='encoder_%d'%i, data=x, num_hidden=dims[i+1])
if internal_act and i < N-1:
x = mx.symbol.Activation(data=x, act_type=internal_act)
if internal_act=='sigmoid' and sparseness_penalty:
x = mx.symbol.IdentityAttachKLSparseReg(data=x, name='sparse_encoder_%d' % i, penalty=sparseness_penalty)
elif output_act and i == N-1:
x = mx.symbol.Activation(data=x, act_type=output_act)
if output_act=='sigmoid' and sparseness_penalty:
x = mx.symbol.IdentityAttachKLSparseReg(data=x, name='sparse_encoder_%d' % i, penalty=sparseness_penalty)
if dropout:
x = mx.symbol.Dropout(data=x, p=dropout)
internals.append(x)
return x, internals
def make_decoder(self, feature, dims, sparseness_penalty=None, dropout=None, internal_act='relu', input_act=None):
x = feature
N = len(dims) - 1
for i in reversed(range(N)):
x = mx.symbol.FullyConnected(name='decoder_%d'%i, data=x, num_hidden=dims[i])
if internal_act and i > 0:
x = mx.symbol.Activation(data=x, act_type=internal_act)
if internal_act=='sigmoid' and sparseness_penalty:
x = mx.symbol.IdentityAttachKLSparseReg(data=x, name='sparse_decoder_%d' % i, penalty=sparseness_penalty)
elif input_act and i == 0:
x = mx.symbol.Activation(data=x, act_type=input_act)
if input_act=='sigmoid' and sparseness_penalty:
x = mx.symbol.IdentityAttachKLSparseReg(data=x, name='sparse_decoder_%d' % i, penalty=sparseness_penalty)
if dropout and i > 0:
x = mx.symbol.Dropout(data=x, p=dropout)
return x
def layerwise_pretrain(self, X, batch_size, n_iter, optimizer, l_rate, decay, lr_scheduler=None, print_every=1000):
def l2_norm(label, pred):
return np.mean(np.square(label-pred))/2.0
solver = Solver(optimizer, momentum=0.9, wd=decay, learning_rate=l_rate, lr_scheduler=lr_scheduler)
solver.set_metric(mx.metric.CustomMetric(l2_norm))
solver.set_monitor(Monitor(print_every))
data_iter = mx.io.NDArrayIter({'data': X}, batch_size=batch_size, shuffle=True,
last_batch_handle='roll_over')
for i in range(self.N):
if i == 0:
data_iter_i = data_iter
else:
X_i = list(model.extract_feature(self.internals[i-1], self.args, self.auxs,
data_iter, X.shape[0], self.xpu).values())[0]
data_iter_i = mx.io.NDArrayIter({'data': X_i}, batch_size=batch_size,
last_batch_handle='roll_over')
logging.info('Pre-training layer %d...'%i)
solver.solve(self.xpu, self.stacks[i], self.args, self.args_grad, self.auxs, data_iter_i,
0, n_iter, {}, False)
def finetune(self, X, batch_size, n_iter, optimizer, l_rate, decay, lr_scheduler=None, print_every=1000):
def l2_norm(label, pred):
return np.mean(np.square(label-pred))/2.0
solver = Solver(optimizer, momentum=0.9, wd=decay, learning_rate=l_rate, lr_scheduler=lr_scheduler)
solver.set_metric(mx.metric.CustomMetric(l2_norm))
solver.set_monitor(Monitor(print_every))
data_iter = mx.io.NDArrayIter({'data': X}, batch_size=batch_size, shuffle=True,
last_batch_handle='roll_over')
logging.info('Fine tuning...')
solver.solve(self.xpu, self.loss, self.args, self.args_grad, self.auxs, data_iter,
0, n_iter, {}, False)
def eval(self, X):
batch_size = 100
data_iter = mx.io.NDArrayIter({'data': X}, batch_size=batch_size, shuffle=False,
last_batch_handle='pad')
Y = list(model.extract_feature(self.loss, self.args, self.auxs, data_iter,
X.shape[0], self.xpu).values())[0]
return np.mean(np.square(Y-X))/2.0