ift6266: deep/convolutional_dae/stacked_convolutional

comparison deep/convolutional_dae/stacked_convolutional_dae.py @ 167:1f5937e9e530

More moves - transformations into data_generation, added "deep" folder

author	Dumitru Erhan <dumitru.erhan@gmail.com>
date	Fri, 26 Feb 2010 14:15:38 -0500
parents	scripts/stacked_dae/stacked_convolutional_dae.py@128507ac4edf
children	3f2cc90ad51c

comparison

equal deleted inserted replaced

-:17ae5a1a4dd1
+:1f5937e9e530
+import numpy
+import theano
+import time
+import theano.tensor as T
+from theano.tensor.shared_randomstreams import RandomStreams
+import theano.sandbox.softsign
+from theano.tensor.signal import downsample
+from theano.tensor.nnet import conv
+import gzip
+import cPickle
+class LogisticRegression(object):
+def __init__(self, input, n_in, n_out):
+self.W = theano.shared( value=numpy.zeros((n_in,n_out),
+dtype = theano.config.floatX) )
+self.b = theano.shared( value=numpy.zeros((n_out,),
+dtype = theano.config.floatX) )
+self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W)+self.b)
+self.y_pred=T.argmax(self.p_y_given_x, axis=1)
+self.params = [self.W, self.b]
+def negative_log_likelihood(self, y):
+return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
+def MSE(self, y):
+return -T.mean(abs((self.p_y_given_x)[T.arange(y.shape[0]),y]-y)**2)
+def errors(self, y):
+if y.ndim != self.y_pred.ndim:
+raise TypeError('y should have the same shape as self.y_pred',
+('y', target.type, 'y_pred', self.y_pred.type))
+if y.dtype.startswith('int'):
+return T.mean(T.neq(self.y_pred, y))
+else:
+raise NotImplementedError()
+class SigmoidalLayer(object):
+def __init__(self, rng, input, n_in, n_out):
+self.input = input
+W_values = numpy.asarray( rng.uniform( \
+low = -numpy.sqrt(6./(n_in+n_out)), \
+high = numpy.sqrt(6./(n_in+n_out)), \
+size = (n_in, n_out)), dtype = theano.config.floatX)
+self.W = theano.shared(value = W_values)
+b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
+self.b = theano.shared(value= b_values)
+self.output = T.tanh(T.dot(input, self.W) + self.b)
+self.params = [self.W, self.b]
+class dA_conv(object):
+def __init__(self, corruption_level = 0.1, input = None, shared_W = None,\
+shared_b = None, filter_shape = None, image_shape = None, poolsize = (2,2)):
+theano_rng = RandomStreams()
+fan_in = numpy.prod(filter_shape[1:])
+fan_out = filter_shape[0] * numpy.prod(filter_shape[2:])
+center = theano.shared(value = 1, name="center")
+scale = theano.shared(value = 2, name="scale")
+if shared_W != None and shared_b != None :
+self.W = shared_W
+self.b = shared_b
+else:
+initial_W = numpy.asarray( numpy.random.uniform( \
+low = -numpy.sqrt(6./(fan_in+fan_out)), \
+high = numpy.sqrt(6./(fan_in+fan_out)), \
+size = filter_shape), dtype = theano.config.floatX)
+initial_b = numpy.zeros((filter_shape[0],), dtype= theano.config.floatX)
+self.W = theano.shared(value = initial_W, name = "W")
+self.b = theano.shared(value = initial_b, name = "b")
+initial_b_prime= numpy.zeros((filter_shape[1],))
+self.W_prime=T.dtensor4('W_prime')
+self.b_prime = theano.shared(value = initial_b_prime, name = "b_prime")
+self.x = input
+self.tilde_x = theano_rng.binomial( self.x.shape, 1, 1 - corruption_level) * self.x
+conv1_out = conv.conv2d(self.tilde_x, self.W, \
+filter_shape=filter_shape, \
+image_shape=image_shape, border_mode='valid')
+self.y = T.tanh(conv1_out + self.b.dimshuffle('x', 0, 'x', 'x'))
+da_filter_shape = [ filter_shape[1], filter_shape[0], filter_shape[2],\
+filter_shape[3] ]
+da_image_shape = [ image_shape[0],filter_shape[0],image_shape[2]-filter_shape[2]+1, \
+image_shape[3]-filter_shape[3]+1 ]
+initial_W_prime =  numpy.asarray( numpy.random.uniform( \
+low = -numpy.sqrt(6./(fan_in+fan_out)), \
+high = numpy.sqrt(6./(fan_in+fan_out)), \
+size = da_filter_shape), dtype = theano.config.floatX)
+self.W_prime = theano.shared(value = initial_W_prime, name = "W_prime")
+#import pdb;pdb.set_trace()
+conv2_out = conv.conv2d(self.y, self.W_prime, \
+filter_shape = da_filter_shape, image_shape = da_image_shape ,\
+border_mode='full')
+self.z =  (T.tanh(conv2_out + self.b_prime.dimshuffle('x', 0, 'x', 'x'))+center) / scale
+scaled_x = (self.x + center) / scale
+self.L = - T.sum( scaled_x*T.log(self.z) + (1-scaled_x)*T.log(1-self.z), axis=1 )
+self.cost = T.mean(self.L)
+self.params = [ self.W, self.b, self.b_prime ]
+class LeNetConvPoolLayer(object):
+def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2,2)):
+assert image_shape[1]==filter_shape[1]
+self.input = input
+W_values = numpy.zeros(filter_shape, dtype=theano.config.floatX)
+self.W = theano.shared(value = W_values)
+b_values = numpy.zeros((filter_shape[0],), dtype= theano.config.floatX)
+self.b = theano.shared(value= b_values)
+conv_out = conv.conv2d(input, self.W,
+filter_shape=filter_shape, image_shape=image_shape)
+fan_in = numpy.prod(filter_shape[1:])
+fan_out = filter_shape[0] * numpy.prod(filter_shape[2:]) / numpy.prod(poolsize)
+W_bound = numpy.sqrt(6./(fan_in + fan_out))
+self.W.value = numpy.asarray(
+rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
+dtype = theano.config.floatX)
+pooled_out = downsample.max_pool2D(conv_out, poolsize, ignore_border=True)
+self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
+self.params = [self.W, self.b]
+class SdA():
+def __init__(self, input, n_ins_conv, n_ins_mlp, train_set_x, train_set_y, batch_size, \
+conv_hidden_layers_sizes, mlp_hidden_layers_sizes, corruption_levels, \
+rng, n_out, pretrain_lr, finetune_lr):
+self.layers = []
+self.pretrain_functions = []
+self.params = []
+self.conv_n_layers = len(conv_hidden_layers_sizes)
+self.mlp_n_layers = len(mlp_hidden_layers_sizes)
+index = T.lscalar() # index to a [mini]batch
+self.x = T.dmatrix('x') # the data is presented as rasterized images
+self.y = T.ivector('y') # the labels are presented as 1D vector of
+for i in xrange( self.conv_n_layers ):
+filter_shape=conv_hidden_layers_sizes[i][0]
+image_shape=conv_hidden_layers_sizes[i][1]
+max_poolsize=conv_hidden_layers_sizes[i][2]
+if i == 0 :
+layer_input=self.x.reshape((batch_size,1,28,28))
+else:
+layer_input=self.layers[-1].output
+layer = LeNetConvPoolLayer(rng, input=layer_input, \
+image_shape=image_shape, \
+filter_shape=filter_shape,poolsize=max_poolsize)
+print 'Convolutional layer '+str(i+1)+' created'
+self.layers += [layer]
+self.params += layer.params
+da_layer = dA_conv(corruption_level = corruption_levels[0],\
+input = layer_input, \
+shared_W = layer.W, shared_b = layer.b,\
+filter_shape = filter_shape , image_shape = image_shape )
+gparams = T.grad(da_layer.cost, da_layer.params)
+updates = {}
+for param, gparam in zip(da_layer.params, gparams):
+updates[param] = param - gparam * pretrain_lr
+update_fn = theano.function([index], da_layer.cost, \
+updates = updates,
+givens = {
+self.x : train_set_x[index*batch_size:(index+1)*batch_size]} )
+self.pretrain_functions += [update_fn]
+for i in xrange( self.mlp_n_layers ):
+if i == 0 :
+input_size = n_ins_mlp
+else:
+input_size = mlp_hidden_layers_sizes[i-1]
+if i == 0 :
+if len( self.layers ) == 0 :
+layer_input=self.x
+else :
+layer_input = self.layers[-1].output.flatten(2)
+else:
+layer_input = self.layers[-1].output
+layer = SigmoidalLayer(rng, layer_input, input_size,
+mlp_hidden_layers_sizes[i] )
+self.layers += [layer]
+self.params += layer.params
+print 'MLP layer '+str(i+1)+' created'
+self.logLayer = LogisticRegression(input=self.layers[-1].output, \
+n_in=mlp_hidden_layers_sizes[-1], n_out=n_out)
+self.params += self.logLayer.params
+cost = self.logLayer.negative_log_likelihood(self.y)
+gparams = T.grad(cost, self.params)
+updates = {}
+for param,gparam in zip(self.params, gparams):
+updates[param] = param - gparam*finetune_lr
+self.finetune = theano.function([index], cost,
+updates = updates,
+givens = {
+self.x : train_set_x[index*batch_size:(index+1)*batch_size],
+self.y : train_set_y[index*batch_size:(index+1)*batch_size]} )
+self.errors = self.logLayer.errors(self.y)
+def sgd_optimization_mnist( learning_rate=0.1, pretraining_epochs = 2, \
+pretrain_lr = 0.01, training_epochs = 1000, \
+dataset='mnist.pkl.gz'):
+f = gzip.open(dataset,'rb')
+train_set, valid_set, test_set = cPickle.load(f)
+f.close()
+def shared_dataset(data_xy):
+data_x, data_y = data_xy
+shared_x = theano.shared(numpy.asarray(data_x, dtype=theano.config.floatX))
+shared_y = theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX))
+return shared_x, T.cast(shared_y, 'int32')
+test_set_x, test_set_y = shared_dataset(test_set)
+valid_set_x, valid_set_y = shared_dataset(valid_set)
+train_set_x, train_set_y = shared_dataset(train_set)
+batch_size = 500 # size of the minibatch
+n_train_batches = train_set_x.value.shape[0] / batch_size
+n_valid_batches = valid_set_x.value.shape[0] / batch_size
+n_test_batches = test_set_x.value.shape[0] / batch_size
+# allocate symbolic variables for the data
+index = T.lscalar() # index to a [mini]batch
+x = T.matrix('x') # the data is presented as rasterized images
+y = T.ivector('y') # the labels are presented as 1d vector of
+# [int] labels
+layer0_input = x.reshape((batch_size,1,28,28))
+# Setup the convolutional layers with their DAs(add as many as you want)
+corruption_levels = [ 0.2, 0.2, 0.2]
+rng = numpy.random.RandomState(1234)
+ker1=2
+ker2=2
+conv_layers=[]
+conv_layers.append([[ker1,1,5,5], [batch_size,1,28,28], [2,2] ])
+conv_layers.append([[ker2,ker1,5,5], [batch_size,ker1,12,12], [2,2] ])
+# Setup the MLP layers of the network
+mlp_layers=[500]
+network = SdA(input = layer0_input, n_ins_conv = 28*28, n_ins_mlp = ker2*4*4, \
+train_set_x = train_set_x, train_set_y = train_set_y, batch_size = batch_size,
+conv_hidden_layers_sizes = conv_layers,  \
+mlp_hidden_layers_sizes = mlp_layers, \
+corruption_levels = corruption_levels , n_out = 10, \
+rng = rng , pretrain_lr = pretrain_lr , finetune_lr = learning_rate )
+test_model = theano.function([index], network.errors,
+givens = {
+network.x: test_set_x[index*batch_size:(index+1)*batch_size],
+network.y: test_set_y[index*batch_size:(index+1)*batch_size]})
+validate_model = theano.function([index], network.errors,
+givens = {
+network.x: valid_set_x[index*batch_size:(index+1)*batch_size],
+network.y: valid_set_y[index*batch_size:(index+1)*batch_size]})
+start_time = time.clock()
+for i in xrange(len(network.layers)-len(mlp_layers)):
+for epoch in xrange(pretraining_epochs):
+for batch_index in xrange(n_train_batches):
+c = network.pretrain_functions[i](batch_index)
+print 'pre-training convolution layer %i, epoch %d, cost '%(i,epoch),c
+patience = 10000 # look as this many examples regardless
+patience_increase = 2. # WAIT THIS MUCH LONGER WHEN A NEW BEST IS
+# FOUND
+improvement_threshold = 0.995 # a relative improvement of this much is
+validation_frequency = min(n_train_batches, patience/2)
+best_params = None
+best_validation_loss = float('inf')
+test_score = 0.
+start_time = time.clock()
+done_looping = False
+epoch = 0
+while (epoch < training_epochs) and (not done_looping):
+epoch = epoch + 1
+for minibatch_index in xrange(n_train_batches):
+cost_ij = network.finetune(minibatch_index)
+iter = epoch * n_train_batches + minibatch_index
+if (iter+1) % validation_frequency == 0:
+validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
+this_validation_loss = numpy.mean(validation_losses)
+print('epoch %i, minibatch %i/%i, validation error %f %%' % \
+(epoch, minibatch_index+1, n_train_batches, \
+this_validation_loss*100.))
+# if we got the best validation score until now
+if this_validation_loss < best_validation_loss:
+#improve patience if loss improvement is good enough
+if this_validation_loss < best_validation_loss * \
+improvement_threshold :
+patience = max(patience, iter * patience_increase)
+# save best validation score and iteration number
+best_validation_loss = this_validation_loss
+best_iter = iter
+# test it on the test set
+test_losses = [test_model(i) for i in xrange(n_test_batches)]
+test_score = numpy.mean(test_losses)
+print((' epoch %i, minibatch %i/%i, test error of best '
+'model %f %%') %
+(epoch, minibatch_index+1, n_train_batches,
+test_score*100.))
+if patience <= iter :
+done_looping = True
+break
+end_time = time.clock()
+print(('Optimization complete with best validation score of %f %%,'
+'with test performance %f %%') %
+(best_validation_loss * 100., test_score*100.))
+print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
+if __name__ == '__main__':
+sgd_optimization_mnist()

Mercurial > ift6266

comparison deep/convolutional_dae/stacked_convolutional_dae.py @ 167:1f5937e9e530