Example #1
0
    def __init__(self, numpy_rng, theano_rng=None, n_ins=5000,
                 hidden_layers_sizes=[500, 500], n_outs=2,
                 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.matrix('y')  # the actual values for each examples

        # 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 regression layer on top of the MLP
        self.regLayer = Regression(
            input=self.sigmoid_layers[-1].output,
            n_in=hidden_layers_sizes[-1], n_out=n_outs)

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

        # compute the cost for second phase of training,
        # defined as the squared error
        self.finetune_cost = self.regLayer.errors(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.regLayer.errors(self.y)
    def build_model(self, load_previous_weights=False):
        """Creates the net's layers from the model settings."""
 	if load_previous_weights == True:
            self.load_weights()
        # Load the data
        datasets = self.load_samples()

        # Train, Validation, Test 100000, 20000, 26... fot Mitocondria set
        # Train, Validation, Test 50000, 10000, 10000 times 28x28 = 784 for MNIST dataset
        self.train_set_x, self.train_set_y = datasets[0]
        self.valid_set_x, self.valid_set_y = datasets[1]
        self.test_set_x, self.test_set_y = datasets[2]

        # Assumes the width equals the height
        img_width_size = numpy.sqrt(self.test_set_x.shape[2].eval()).astype(int)
	print img_width_size
        assert self.test_set_x.shape[2].eval() == img_width_size * img_width_size, 'input image not square'
        print "Image shape %s x %s" % (img_width_size, img_width_size)
        nbr_channels = self.test_set_x.shape[1].eval()
        self.input_shape = (nbr_channels, img_width_size, img_width_size)

        # Compute number of minibatches for training, validation and testing
        # Divide the total number of elements in the set by the batch size
        self.n_train_batches = self.train_set_x.get_value(borrow=True).shape[0]
        self.n_valid_batches = self.valid_set_x.get_value(borrow=True).shape[0]
        self.n_test_batches = self.test_set_x.get_value(borrow=True).shape[0]
        self.n_train_batches /= self.batch_size
        self.n_valid_batches /= self.batch_size
        self.n_test_batches /= self.batch_size

        print 'Size train_batches %d, n_valid_batches %d, n_test_batches %d' % (self.n_train_batches, self.n_valid_batches, self.n_test_batches)


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

        # The input is an 4D array of size, number of images in the batch size, number of channels
        # (or number of feature maps), image width and height.
        #TODO(vpetresc) make nbr of channels variable (1 or 3)
        layer_input = self.x.reshape((self.batch_size, nbr_feature_maps, self.input_shape[1], self.input_shape[2]))
        pooled_width = self.input_shape[1]
        pooled_height = self.input_shape[2]
        # Add convolutional layers followed by pooling
        clayers = []
        idx = 0
        for clayer_params in self.convolutional_layers:
            print 'Adding conv layer nbr filter %d, Ksize %d' % (clayer_params.num_filters, clayer_params.filter_w)
            if load_previous_weights == False:
	    	layer = LeNetConvPoolLayer(self.rng, input=layer_input,
                                       image_shape=(self.batch_size, nbr_feature_maps, pooled_width, pooled_height),
                                       filter_shape=(clayer_params.num_filters, nbr_feature_maps,
                                                     clayer_params.filter_w, clayer_params.filter_w),
                                       poolsize=(self.poolsize, self.poolsize))
	    else:
		layer = LeNetConvPoolLayer(self.rng, input=layer_input,
                                       image_shape=(self.batch_size, nbr_feature_maps, pooled_width, pooled_height),
                                       filter_shape=(clayer_params.num_filters, nbr_feature_maps,
                                                     clayer_params.filter_w, clayer_params.filter_w),
                                       poolsize=(self.poolsize, self.poolsize),
                                       W=self.cached_weights[itdx+1], b=self.cached_weights[idx])
            clayers.append(layer)
            pooled_width = (pooled_width - clayer_params.filter_w + 1) / self.poolsize
            pooled_height = (pooled_height - clayer_params.filter_w + 1) / self.poolsize
            layer_input = layer.output
            nbr_feature_maps = clayer_params.num_filters
            idx += 2

        # Flatten the output of the previous layers and add
        # fully connected sigmoidal layers
        layer_input = layer_input.flatten(2)
        nbr_input = nbr_feature_maps * pooled_width * pooled_height
        hlayers = []
        for hlayer_params in self.hidden_layers:
            print 'Adding hidden layer fully connected %d' % (hlayer_params.num_outputs)
            if load_previous_weights == False:
               layer = HiddenLayer(self.rng, input=layer_input, n_in=nbr_input,
                                n_out=hlayer_params.num_outputs, activation=T.tanh)
            else:
               layer = HiddenLayer(self.rng, input=layer_input, n_in=nbr_input,
                                n_out=hlayer_params.num_outputs, activation=T.tanh,
                                W=self.cached_weights[idx+1], b=self.cached_weights[idx])
            idx +=2
	    nbr_input = hlayer_params.num_outputs
            layer_input = layer.output
            hlayers.append(layer)

        # classify the values of the fully-connected sigmoidal layer
        if load_previous_weights == False: 
	    self.output_layer = Regression(input=layer_input,
                                               n_in=nbr_input,
                                               n_out=self.last_layer.num_outputs)
        else:
	    self.output_layer = Regression(input=layer_input,
                                               n_in=nbr_input,
                                               n_out=self.last_layer.num_outputs,
					       W=self.cached_weights[idx+1], b=self.cached_weights[idx])

        # the cost we minimize during training is the NLL of the model
        self.cost = self.output_layer.squared_loss(self.y)

        # Create a list of all model parameters to be fit by gradient descent.
        # The parameters are added in reversed order because ofthe order
        # in the backpropagation algorithm.
        self.params = self.output_layer.params
        for hidden_layer in reversed(hlayers):
            self.params += hidden_layer.params
        for conv_layer in reversed(clayers):
            self.params += conv_layer.params

        # create a list of gradients for all model parameters
        self.grads = T.grad(self.cost, self.params)
Example #3
0
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=5000,
                 hidden_layers_sizes=[500, 500], n_outs=2,
                 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.matrix('y')  # the actual values for each examples

        # 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 regression layer on top of the MLP
        self.regLayer = Regression(
            input=self.sigmoid_layers[-1].output,
            n_in=hidden_layers_sizes[-1], n_out=n_outs)

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

        # compute the cost for second phase of training,
        # defined as the squared error
        self.finetune_cost = self.regLayer.errors(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.regLayer.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 = []
        first_layer = True
        for dA in self.dA_layers:
            # get the cost and the updates list
            if first_layer:
                cost, updates = dA.get_cost_updates(corruption_level,
                                                    learning_rate,
                                                    noise='gaussian')
                first_layer = False
            else:
                cost, updates = dA.get_cost_updates(corruption_level,
                                                    learning_rate,
                                                    noise='gaussian')
            # 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):
        '''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]
        (test_set_x, test_set_y) = datasets[2]

        # 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
        n_test_batches = test_set_x.get_value(borrow=True).shape[0]
        n_test_batches /= batch_size

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

        learning_rate = T.scalar('lr')

        # 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,
                                           theano.Param(learning_rate, default=0.1)],
              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')

        test_score_i = theano.function([index], self.errors,
                 givens={
                   self.x: test_set_x[index * batch_size:
                                      (index + 1) * batch_size],
                   self.y: test_set_y[index * batch_size:
                                      (index + 1) * batch_size]},
                      name='test')

        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)]

        # Create a function that scans the entire test set
        def test_score():
            return [test_score_i(i) for i in xrange(n_test_batches)]

        return train_fn, valid_score, test_score
class CNNTrainRegression(CNNBase):
    """The class takes a proto bufer as input, setups a CNN according to the
        settings, trains the network and saves the weights in a file
    """
    def __init__(self, protofile, cached_weights):
        """
        :param protofile: describes the arhitecture of the network
        :type protofile: string
        :param cached_weights: filename of the weights
        :type cached_weights: string

        """
        self.cnntype = 'TRAIN' #: extension that is added to the logfile name
        super(CNNTrain, self).__init__(protofile, cached_weights)
        #: Array of train data of size num samples x dimension of sample
        self.train_set_x = None
        #: Array of target data of size num samples x 1
        self.train_set_y = None
        #: Array of valid data of size num samples x dimension of sample
        self.valid_set_x = None
        self.valid_set_y = None
        self.test_set_x = None
        self.test_set_y = None
        #: The number of batches in which train_set_x is divided
        self.n_train_batches = 0
        self.n_valid_batches = 0
        self.n_test_batches = 0
        #: The cost that is minimized by the algorithm, usually log likelihood
        self.cost = 0
        #: Array of symbolic gradients of the cost wrt. weights
        self.grads = None
        #: Array of weights (plus biases) for which the gradient is computed
        self.params = None
        #: Usually logistic regression
        self.output_layer = None
        #: The size of the input array that is being passed to the algorithm
        #: It  has size num samples x num channels x img width x img height
        self.input_shape = None
        self.index = T.lscalar()  #: index to a [mini]batch
        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 ints

    def build_model(self, load_previous_weights=False):
        """Creates the net's layers from the model settings."""
 	if load_previous_weights == True:
            self.load_weights()
        # Load the data
        datasets = self.load_samples()

        # Train, Validation, Test 100000, 20000, 26... fot Mitocondria set
        # Train, Validation, Test 50000, 10000, 10000 times 28x28 = 784 for MNIST dataset
        self.train_set_x, self.train_set_y = datasets[0]
        self.valid_set_x, self.valid_set_y = datasets[1]
        self.test_set_x, self.test_set_y = datasets[2]

        # Assumes the width equals the height
        img_width_size = numpy.sqrt(self.test_set_x.shape[2].eval()).astype(int)
	print img_width_size
        assert self.test_set_x.shape[2].eval() == img_width_size * img_width_size, 'input image not square'
        print "Image shape %s x %s" % (img_width_size, img_width_size)
        nbr_channels = self.test_set_x.shape[1].eval()
        self.input_shape = (nbr_channels, img_width_size, img_width_size)

        # Compute number of minibatches for training, validation and testing
        # Divide the total number of elements in the set by the batch size
        self.n_train_batches = self.train_set_x.get_value(borrow=True).shape[0]
        self.n_valid_batches = self.valid_set_x.get_value(borrow=True).shape[0]
        self.n_test_batches = self.test_set_x.get_value(borrow=True).shape[0]
        self.n_train_batches /= self.batch_size
        self.n_valid_batches /= self.batch_size
        self.n_test_batches /= self.batch_size

        print 'Size train_batches %d, n_valid_batches %d, n_test_batches %d' % (self.n_train_batches, self.n_valid_batches, self.n_test_batches)


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

        # The input is an 4D array of size, number of images in the batch size, number of channels
        # (or number of feature maps), image width and height.
        #TODO(vpetresc) make nbr of channels variable (1 or 3)
        layer_input = self.x.reshape((self.batch_size, nbr_feature_maps, self.input_shape[1], self.input_shape[2]))
        pooled_width = self.input_shape[1]
        pooled_height = self.input_shape[2]
        # Add convolutional layers followed by pooling
        clayers = []
        idx = 0
        for clayer_params in self.convolutional_layers:
            print 'Adding conv layer nbr filter %d, Ksize %d' % (clayer_params.num_filters, clayer_params.filter_w)
            if load_previous_weights == False:
	    	layer = LeNetConvPoolLayer(self.rng, input=layer_input,
                                       image_shape=(self.batch_size, nbr_feature_maps, pooled_width, pooled_height),
                                       filter_shape=(clayer_params.num_filters, nbr_feature_maps,
                                                     clayer_params.filter_w, clayer_params.filter_w),
                                       poolsize=(self.poolsize, self.poolsize))
	    else:
		layer = LeNetConvPoolLayer(self.rng, input=layer_input,
                                       image_shape=(self.batch_size, nbr_feature_maps, pooled_width, pooled_height),
                                       filter_shape=(clayer_params.num_filters, nbr_feature_maps,
                                                     clayer_params.filter_w, clayer_params.filter_w),
                                       poolsize=(self.poolsize, self.poolsize),
                                       W=self.cached_weights[itdx+1], b=self.cached_weights[idx])
            clayers.append(layer)
            pooled_width = (pooled_width - clayer_params.filter_w + 1) / self.poolsize
            pooled_height = (pooled_height - clayer_params.filter_w + 1) / self.poolsize
            layer_input = layer.output
            nbr_feature_maps = clayer_params.num_filters
            idx += 2

        # Flatten the output of the previous layers and add
        # fully connected sigmoidal layers
        layer_input = layer_input.flatten(2)
        nbr_input = nbr_feature_maps * pooled_width * pooled_height
        hlayers = []
        for hlayer_params in self.hidden_layers:
            print 'Adding hidden layer fully connected %d' % (hlayer_params.num_outputs)
            if load_previous_weights == False:
               layer = HiddenLayer(self.rng, input=layer_input, n_in=nbr_input,
                                n_out=hlayer_params.num_outputs, activation=T.tanh)
            else:
               layer = HiddenLayer(self.rng, input=layer_input, n_in=nbr_input,
                                n_out=hlayer_params.num_outputs, activation=T.tanh,
                                W=self.cached_weights[idx+1], b=self.cached_weights[idx])
            idx +=2
	    nbr_input = hlayer_params.num_outputs
            layer_input = layer.output
            hlayers.append(layer)

        # classify the values of the fully-connected sigmoidal layer
        if load_previous_weights == False: 
	    self.output_layer = Regression(input=layer_input,
                                               n_in=nbr_input,
                                               n_out=self.last_layer.num_outputs)
        else:
	    self.output_layer = Regression(input=layer_input,
                                               n_in=nbr_input,
                                               n_out=self.last_layer.num_outputs,
					       W=self.cached_weights[idx+1], b=self.cached_weights[idx])

        # the cost we minimize during training is the NLL of the model
        self.cost = self.output_layer.squared_loss(self.y)

        # Create a list of all model parameters to be fit by gradient descent.
        # The parameters are added in reversed order because ofthe order
        # in the backpropagation algorithm.
        self.params = self.output_layer.params
        for hidden_layer in reversed(hlayers):
            self.params += hidden_layer.params
        for conv_layer in reversed(clayers):
            self.params += conv_layer.params

        # create a list of gradients for all model parameters
        self.grads = T.grad(self.cost, self.params)

    def train_model(self):
        """Abstract method to be implemented by subclasses"""
        raise NotImplementedError()