| # 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): |
| 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(1000)) |
| 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): |
| 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(1000)) |
| 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 |
