コード例 #1
0
ファイル: SdA.py プロジェクト: kensk8er/object_recognition
	def __init__(self, numpy_rng, theano_rng=None, n_ins=1024,
				 hidden_layers_sizes=[784, 784], n_outs=10,
				 corruption_levels=[0.1, 0.1]):
		""" This class is made to support a variable number of layers.

		:type numpy_rng: numpy.random.RandomState
		:param numpy_rng: numpy random number generator used to draw initial
					weights

		:type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
		:param theano_rng: Theano random generator; if None is given one is
						   generated based on a seed drawn from `rng`

		:type n_ins: int
		:param n_ins: dimension of the input to the sdA

		:type n_layers_sizes: list of ints
		:param n_layers_sizes: intermediate layers size, must contain
							   at least one value

		:type n_outs: int
		:param n_outs: dimension of the output of the network

		:type corruption_levels: list of float
		:param corruption_levels: amount of corruption to use for each
								  layer
		"""

		self.sigmoid_layers = []
		self.dA_layers = []
		self.params = []
		self.n_layers = len(hidden_layers_sizes)

		assert self.n_layers > 0

		if not theano_rng:
			theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
		# allocate symbolic variables for the data
		self.x = T.matrix('x')  # the data is presented as rasterized images
		self.y = T.ivector('y')  # the labels are presented as 1D vector of
								 # [int] labels

		# The SdA is an MLP, for which all weights of intermediate layers
		# are shared with a different denoising autoencoders
		# We will first construct the SdA as a deep multilayer perceptron,
		# and when constructing each sigmoidal layer we also construct a
		# denoising autoencoder that shares weights with that layer
		# During pretraining we will train these autoencoders (which will
		# lead to chainging the weights of the MLP as well)
		# During finetunining we will finish training the SdA by doing
		# stochastich gradient descent on the MLP

		for i in xrange(self.n_layers):
			# construct the sigmoidal layer

			# the size of the input is either the number of hidden units of
			# the layer below or the input size if we are on the first layer
			if i == 0:
				input_size = n_ins
			else:
				input_size = hidden_layers_sizes[i - 1]

			# the input to this layer is either the activation of the hidden
			# layer below or the input of the SdA if you are on the first
			# layer
			if i == 0:
				layer_input = self.x
			else:
				layer_input = self.sigmoid_layers[-1].output

			sigmoid_layer = HiddenLayer(rng=numpy_rng,
										input=layer_input,
										n_in=input_size,
										n_out=hidden_layers_sizes[i],
										activation=T.nnet.sigmoid)
			# add the layer to our list of layers
			self.sigmoid_layers.append(sigmoid_layer)
			# its arguably a philosophical question...
			# but we are going to only declare that the parameters of the
			# sigmoid_layers are parameters of the StackedDAA
			# the visible biases in the dA are parameters of those
			# dA, but not the SdA
			self.params.extend(sigmoid_layer.params)

			# Construct a denoising autoencoder that shared weights with this
			# layer
			dA_layer = dA(numpy_rng=numpy_rng,
						  theano_rng=theano_rng,
						  input=layer_input,
						  n_visible=input_size,
						  n_hidden=hidden_layers_sizes[i],
						  W=sigmoid_layer.W,
						  bhid=sigmoid_layer.b)
			self.dA_layers.append(dA_layer)

		# We now need to add a logistic layer on top of the MLP
		self.logLayer = LogisticRegression(
						 input=self.sigmoid_layers[-1].output,
						 n_in=hidden_layers_sizes[-1], n_out=n_outs)

		self.params.extend(self.logLayer.params)
		# construct a function that implements one step of finetunining

		# compute the cost for second phase of training,
		# defined as the negative log likelihood
		self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
		# compute the gradients with respect to the model parameters
		# symbolic variable that points to the number of errors made on the
		# minibatch given by self.x and self.y
		self.errors = self.logLayer.errors(self.y)
コード例 #2
0
def evaluate_lenet5(learning_rate=0.1, learning_rate2=0.05, learning_rate3=0.01, n_epochs=200,
					dataset='cifar-10-batches-py',
					nkerns=[6, 16], batch_size=20, mode='train', amount='full'): # nkerns coule be ok with [10, 50]
	""" Demonstrates lenet on MNIST dataset

	:type learning_rate: float
	:param learning_rate: learning rate used (factor for the stochastic
						  gradient)

	:type n_epochs: int
	:param n_epochs: maximal number of epochs to run the optimizer

	:type dataset: string
	:param dataset: path to the dataset used for training /testing (MNIST here)

	:type nkerns: list of ints
	:param nkerns: number of kernels on each layer
	"""

	#learning_rate = theano.shared(value=learning_rate, borrow=True)

	rng = numpy.random.RandomState(23455)

	datasets = load_data(dataset, mode=mode, amount=amount)

	if mode == 'train':
		train_set_x, train_set_y = datasets[0]
		valid_set_x, valid_set_y = datasets[1]
	else:
		test_set_x, test_set_y = datasets[0]

	# compute number of minibatches for training, validation and testing
	if mode == 'train':
		n_train_batches = train_set_x.get_value(borrow=True).shape[0]
		n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
		n_train_batches /= batch_size
		n_valid_batches /= batch_size
	else:
		n_test_batches = test_set_x.get_value(borrow=True).shape[0]
		n_test_batches /= 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

	ishape = (32, 32)  # this is the size of CIFIA-10 images (gray-scaled)

	######################
	# BUILD ACTUAL MODEL #
	######################
	print '... building the model'

	# Reshape matrix of rasterized images of shape (batch_size,32*32)
	# to a 4D tensor, compatible with our LeNetConvPoolLayer
	layer0_input = x.reshape((batch_size, 1, 32, 32))

	# Construct the first convolutional pooling layer:
	# filtering reduces the image size to (32-5+1,32-5+1)=(28,28)
	# maxpooling reduces this further to (28/2,28/2) = (14,14)
	# 4D output tensor is thus of shape (batch_size,nkerns[0],14,14)
	layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
			image_shape=(batch_size, 1, 32, 32),
			filter_shape=(nkerns[0], 1, 5, 5), poolsize=(2, 2))

	# Construct the second convolutional pooling layer
	# filtering reduces the image size to (14-5+1,14-5+1)=(10,10)
	# maxpooling reduces this further to (10/2,10/2) = (5,5)
	# 4D output tensor is thus of shape (nkerns[0],nkerns[1],5,5)
	layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
			image_shape=(batch_size, nkerns[0], 14, 14),
			filter_shape=(nkerns[1], nkerns[0], 5, 5), poolsize=(2, 2))

	# the HiddenLayer being fully-connected, it operates on 2D matrices of
	# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
	# This will generate a matrix of shape (20,50*5*5) = (20,1250) <-??
	layer2_input = layer1.output.flatten(2)

	# construct a fully-connected sigmoidal layer
	layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[1] * 5 * 5,
						 n_out=500, activation=T.tanh)

	# classify the values of the fully-connected sigmoidal layer
	layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)

	## load the saved parameters
	if mode == 'test':
		learned_params = unpickle('params/convolutional_mlp_gray.pkl')

	# the cost we minimize during training is the NLL of the model
	cost = layer3.negative_log_likelihood(y)

	# create a function to compute the mistakes that are made by the model
	if mode == 'test':
		test_model = theano.function([index], layer3.errors(y),
				givens={
					x: test_set_x[index * batch_size: (index + 1) * batch_size],
					y: test_set_y[index * batch_size: (index + 1) * batch_size]})
	else:
		validate_model = theano.function([index], layer3.errors(y),
				givens={
					x: valid_set_x[index * batch_size: (index + 1) * batch_size],
					y: valid_set_y[index * batch_size: (index + 1) * batch_size]})

		check_label = theano.function(inputs=[index],
				outputs=layer3.y_pair(y),
					givens={
						x: train_set_x[index * batch_size: (index + 1) * batch_size],
						y: train_set_y[index * batch_size: (index + 1) * batch_size]})

	# create a function to get the labels predicted by the model
	if mode == 'test':
		get_test_labels = theano.function([index], layer3.y_pred,
				givens={
					x: test_set_x[index * batch_size: (index + 1) * batch_size],
					layer0.W: learned_params[0],
					layer0.b: learned_params[1],
					layer1.W: learned_params[2],
					layer1.b: learned_params[3],
					layer2.W: learned_params[4],
					layer2.b: learned_params[5],
					layer3.W: learned_params[6],
					layer3.b: learned_params[7]})


	if mode == 'train':
		# create a list of all model parameters to be fit by gradient descent
		params = layer3.params + layer2.params + layer1.params + layer0.params
	
		# create a list of gradients for all model parameters
		grads = T.grad(cost, params)

	# train_model is a function that updates the model parameters by
	# SGD Since this model has many parameters, it would be tedious to
	# manually create an update rule for each model parameter. We thus
	# create the updates list by automatically looping over all
	# (params[i],grads[i]) pairs.
	if mode == 'train':
		updates = []
		for param_i, grad_i in zip(params, grads):
			updates.append((param_i, param_i - learning_rate * grad_i))

		updates2 = []
		for param_i, grad_i in zip(params, grads):
			updates2.append((param_i, param_i - learning_rate2 * grad_i))

		updates3 = []
		for param_i, grad_i in zip(params, grads):
			updates3.append((param_i, param_i - learning_rate3 * grad_i))

	if mode == 'train':
		train_model = theano.function([index], cost, updates=updates,
			  givens={
				x: train_set_x[index * batch_size: (index + 1) * batch_size],
				y: train_set_y[index * batch_size: (index + 1) * batch_size]})
		
		train_model2 = theano.function([index], cost, updates=updates2,
			  givens={
				x: train_set_x[index * batch_size: (index + 1) * batch_size],
				y: train_set_y[index * batch_size: (index + 1) * batch_size]})

		train_model3 = theano.function([index], cost, updates=updates3,
			  givens={
				x: train_set_x[index * batch_size: (index + 1) * batch_size],
				y: train_set_y[index * batch_size: (index + 1) * batch_size]})

	###############
	# TRAIN MODEL #
	###############
	print '... training the model'
	# early-stopping parameters
	if mode == 'train':
		patience = 10000  # look as this many examples regardless
		patience_increase = 2  # wait this much longer when a new best is
							   # found
		improvement_threshold = 0.999  # a relative improvement of this much is
									   # considered significant
		validation_frequency = min(n_train_batches, patience / 2)
								  # go through this many
								  # minibatche before checking the network
								  # on the validation set; in this case we
								  # check every epoch

	start_time = time.clock()

	if mode == 'train':
		best_params = None
		best_validation_loss = numpy.inf
		best_iter = 0
		test_score = 0.
		done_looping = False
	else:
		done_looping = True

	epoch = 0

	while (epoch < n_epochs) and (not done_looping):
		epoch = epoch + 1
		for minibatch_index in xrange(n_train_batches):

			iter = (epoch - 1) * n_train_batches + minibatch_index

			if iter % 100 == 0:
				print 'training @ iter = ', iter

			if epoch == 1:
				cost_ij = train_model(minibatch_index)
			elif this_validation_loss < 0.45 and this_validation_loss > 0.35:
				cost_ij = train_model2(minibatch_index)
			elif this_validation_loss < 0.35:
				cost_ij = train_model3(minibatch_index)
			else:
				cost_ij = train_model(minibatch_index)

			## check the contents of predictions occasionaly
			'''
			if iter % 100 == 0:
				[prediction, true_label] = check_label(minibatch_index)
				print 'prediction:'
				print prediction
				print 'true_label:'
				print true_label
			'''

			## save the parameters
			if mode == 'train':
				get_params = theano.function(inputs=[], outputs=[layer0.W, layer0.b, layer1.W, layer1.b, layer2.W, layer2.b, layer3.W, layer3.b])
				save_parameters(get_params(), 'convolutional_mlp_gray')


			if (iter + 1) % validation_frequency == 0:

				# compute zero-one loss on validation set
				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
			'''


	if mode == 'test':
		print 'predicting the labels...'
		pred_labels = [[0 for j in xrange(batch_size)] for i in xrange(n_test_batches)]
		for i in xrange(n_test_batches):
			print str(i+1), '/', str(n_test_batches)
			pred_labels[i] = get_test_labels(i)

		writer = csv.writer(file('result/convolutional_mlp_gray.csv', 'w'))
		row = 1

		print 'output test labels...'
		for i in xrange(len(pred_labels)): # TBF: hard code
			print str(i+1), '/', str(len(pred_labels))
			for j in xrange(len(pred_labels[i])):
				writer.writerow([row, pred_labels[i][j]])
				row += 1


	end_time = time.clock()
	if mode == 'train':
		print('Optimization complete.')
		print('Best validation score of %f %% obtained at iteration %i,'\
			  'with test performance %f %%' %
			  (best_validation_loss * 100., best_iter + 1, test_score * 100.))
	print >> sys.stderr, ('The code for file ' +
						  os.path.split(__file__)[1] +
						  ' ran for %.2fm' % ((end_time - start_time) / 60.))
コード例 #3
0
ファイル: SdA.py プロジェクト: kensk8er/object_recognition
class SdA(object):
	"""Stacked denoising auto-encoder class (SdA)

	A stacked denoising autoencoder model is obtained by stacking several
	dAs. The hidden layer of the dA at layer `i` becomes the input of
	the dA at layer `i+1`. The first layer dA gets as input the input of
	the SdA, and the hidden layer of the last dA represents the output.
	Note that after pretraining, the SdA is dealt with as a normal MLP,
	the dAs are only used to initialize the weights.
	"""

	def __init__(self, numpy_rng, theano_rng=None, n_ins=1024,
				 hidden_layers_sizes=[784, 784], n_outs=10,
				 corruption_levels=[0.1, 0.1]):
		""" This class is made to support a variable number of layers.

		:type numpy_rng: numpy.random.RandomState
		:param numpy_rng: numpy random number generator used to draw initial
					weights

		:type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
		:param theano_rng: Theano random generator; if None is given one is
						   generated based on a seed drawn from `rng`

		:type n_ins: int
		:param n_ins: dimension of the input to the sdA

		:type n_layers_sizes: list of ints
		:param n_layers_sizes: intermediate layers size, must contain
							   at least one value

		:type n_outs: int
		:param n_outs: dimension of the output of the network

		:type corruption_levels: list of float
		:param corruption_levels: amount of corruption to use for each
								  layer
		"""

		self.sigmoid_layers = []
		self.dA_layers = []
		self.params = []
		self.n_layers = len(hidden_layers_sizes)

		assert self.n_layers > 0

		if not theano_rng:
			theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
		# allocate symbolic variables for the data
		self.x = T.matrix('x')  # the data is presented as rasterized images
		self.y = T.ivector('y')  # the labels are presented as 1D vector of
								 # [int] labels

		# The SdA is an MLP, for which all weights of intermediate layers
		# are shared with a different denoising autoencoders
		# We will first construct the SdA as a deep multilayer perceptron,
		# and when constructing each sigmoidal layer we also construct a
		# denoising autoencoder that shares weights with that layer
		# During pretraining we will train these autoencoders (which will
		# lead to chainging the weights of the MLP as well)
		# During finetunining we will finish training the SdA by doing
		# stochastich gradient descent on the MLP

		for i in xrange(self.n_layers):
			# construct the sigmoidal layer

			# the size of the input is either the number of hidden units of
			# the layer below or the input size if we are on the first layer
			if i == 0:
				input_size = n_ins
			else:
				input_size = hidden_layers_sizes[i - 1]

			# the input to this layer is either the activation of the hidden
			# layer below or the input of the SdA if you are on the first
			# layer
			if i == 0:
				layer_input = self.x
			else:
				layer_input = self.sigmoid_layers[-1].output

			sigmoid_layer = HiddenLayer(rng=numpy_rng,
										input=layer_input,
										n_in=input_size,
										n_out=hidden_layers_sizes[i],
										activation=T.nnet.sigmoid)
			# add the layer to our list of layers
			self.sigmoid_layers.append(sigmoid_layer)
			# its arguably a philosophical question...
			# but we are going to only declare that the parameters of the
			# sigmoid_layers are parameters of the StackedDAA
			# the visible biases in the dA are parameters of those
			# dA, but not the SdA
			self.params.extend(sigmoid_layer.params)

			# Construct a denoising autoencoder that shared weights with this
			# layer
			dA_layer = dA(numpy_rng=numpy_rng,
						  theano_rng=theano_rng,
						  input=layer_input,
						  n_visible=input_size,
						  n_hidden=hidden_layers_sizes[i],
						  W=sigmoid_layer.W,
						  bhid=sigmoid_layer.b)
			self.dA_layers.append(dA_layer)

		# We now need to add a logistic layer on top of the MLP
		self.logLayer = LogisticRegression(
						 input=self.sigmoid_layers[-1].output,
						 n_in=hidden_layers_sizes[-1], n_out=n_outs)

		self.params.extend(self.logLayer.params)
		# construct a function that implements one step of finetunining

		# compute the cost for second phase of training,
		# defined as the negative log likelihood
		self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)
		# compute the gradients with respect to the model parameters
		# symbolic variable that points to the number of errors made on the
		# minibatch given by self.x and self.y
		self.errors = self.logLayer.errors(self.y)

	def pretraining_functions(self, train_set_x, batch_size):
		''' Generates a list of functions, each of them implementing one
		step in trainnig the dA corresponding to the layer with same index.
		The function will require as input the minibatch index, and to train
		a dA you just need to iterate, calling the corresponding function on
		all minibatch indexes.

		:type train_set_x: theano.tensor.TensorType
		:param train_set_x: Shared variable that contains all datapoints used
							for training the dA

		:type batch_size: int
		:param batch_size: size of a [mini]batch

		:type learning_rate: float
		:param learning_rate: learning rate used during training for any of
							  the dA layers
		'''

		# index to a [mini]batch
		index = T.lscalar('index')  # index to a minibatch
		corruption_level = T.scalar('corruption')  # % of corruption to use
		learning_rate = T.scalar('lr')  # learning rate to use
		# number of batches
		n_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
		# begining of a batch, given `index`
		batch_begin = index * batch_size
		# ending of a batch given `index`
		batch_end = batch_begin + batch_size

		pretrain_fns = []
		for dA in self.dA_layers:
			# get the cost and the updates list
			cost, updates = dA.get_cost_updates(corruption_level,
												learning_rate)
			# compile the theano function
			fn = theano.function(inputs=[index,
							  theano.Param(corruption_level, default=0.2),
							  theano.Param(learning_rate, default=0.1)],
								 outputs=cost,
								 updates=updates,
								 givens={self.x: train_set_x[batch_begin:
															 batch_end]})
			# append `fn` to the list of functions
			pretrain_fns.append(fn)

		return pretrain_fns

	def build_finetune_functions(self, datasets, batch_size, learning_rate):
		'''Generates a function `train` that implements one step of
		finetuning, a function `validate` that computes the error on
		a batch from the validation set, and a function `test` that
		computes the error on a batch from the testing set

		:type datasets: list of pairs of theano.tensor.TensorType
		:param datasets: It is a list that contain all the datasets;
						 the has to contain three pairs, `train`,
						 `valid`, `test` in this order, where each pair
						 is formed of two Theano variables, one for the
						 datapoints, the other for the labels

		:type batch_size: int
		:param batch_size: size of a minibatch

		:type learning_rate: float
		:param learning_rate: learning rate used during finetune stage
		'''

		(train_set_x, train_set_y) = datasets[0]
		(valid_set_x, valid_set_y) = datasets[1]

		# compute number of minibatches for training, validation and testing
		n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
		n_valid_batches /= batch_size

		index = T.lscalar('index')  # index to a [mini]batch

		# compute the gradients with respect to the model parameters
		gparams = T.grad(self.finetune_cost, self.params)

		# compute list of fine-tuning updates
		updates = []
		for param, gparam in zip(self.params, gparams):
			updates.append((param, param - gparam * learning_rate))

		train_fn = theano.function(inputs=[index],
			  outputs=self.finetune_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]},
			  name='train')

		valid_score_i = theano.function([index], self.errors,
			  givens={
				 self.x: valid_set_x[index * batch_size:
									 (index + 1) * batch_size],
				 self.y: valid_set_y[index * batch_size:
									 (index + 1) * batch_size]},
					  name='valid')

		# Create a function that scans the entire validation set
		def valid_score():
			return [valid_score_i(i) for i in xrange(n_valid_batches)]

		return train_fn, valid_score