def __init__(self, numpy_rng, theano_rng=None, cfg = None, dnn_shared = None, shared_layers=[]): self.layers = [] self.dropout_layers = [] self.params = [] self.delta_params = [] self.cfg = cfg self.n_ins = cfg.n_ins; self.n_outs = cfg.n_outs self.hidden_layers_sizes = cfg.hidden_layers_sizes self.hidden_layers_number = len(self.hidden_layers_sizes) self.activation = cfg.activation self.do_maxout = cfg.do_maxout; self.pool_size = cfg.pool_size self.input_dropout_factor = cfg.input_dropout_factor; self.dropout_factor = cfg.dropout_factor self.max_col_norm = cfg.max_col_norm self.l1_reg = cfg.l1_reg self.l2_reg = cfg.l2_reg if not theano_rng: theano_rng = RandomStreams(numpy_rng.randint(2 ** 30)) # allocate symbolic variables for the data self.x = T.matrix('x') self.y = T.ivector('y') for i in range(self.hidden_layers_number): # construct the hidden layer if i == 0: input_size = self.n_ins layer_input = self.x if self.input_dropout_factor > 0.0: dropout_layer_input = _dropout_from_layer(theano_rng, self.x, self.input_dropout_factor) else: dropout_layer_input = self.x else: input_size = self.hidden_layers_sizes[i - 1] layer_input = (1 - self.dropout_factor[i - 1]) * self.layers[-1].output dropout_layer_input = self.dropout_layers[-1].dropout_output W = None; b = None if (i in shared_layers) : W = dnn_shared.layers[i].W; b = dnn_shared.layers[i].b if self.do_maxout == False: dropout_layer = DropoutHiddenLayer(rng=numpy_rng, input=dropout_layer_input, n_in=input_size, n_out=self.hidden_layers_sizes[i], W = W, b = b, activation= self.activation, dropout_factor=self.dropout_factor[i]) hidden_layer = HiddenLayer(rng=numpy_rng, input=layer_input, n_in=input_size, n_out=self.hidden_layers_sizes[i], activation= self.activation, W=dropout_layer.W, b=dropout_layer.b) else: dropout_layer = DropoutHiddenLayer(rng=numpy_rng, input=dropout_layer_input, n_in=input_size, n_out=self.hidden_layers_sizes[i] * self.pool_size, W = W, b = b, activation= (lambda x: 1.0*x), dropout_factor=self.dropout_factor[i], do_maxout = True, pool_size = self.pool_size) hidden_layer = HiddenLayer(rng=numpy_rng, input=layer_input, n_in=input_size, n_out=self.hidden_layers_sizes[i] * self.pool_size, activation= (lambda x: 1.0*x), W=dropout_layer.W, b=dropout_layer.b, do_maxout = True, pool_size = self.pool_size) # add the layer to our list of layers self.layers.append(hidden_layer) self.dropout_layers.append(dropout_layer) self.params.extend(dropout_layer.params) self.delta_params.extend(dropout_layer.delta_params) # We now need to add a logistic layer on top of the MLP self.dropout_logLayer = LogisticRegression( input=self.dropout_layers[-1].dropout_output, n_in=self.hidden_layers_sizes[-1], n_out=self.n_outs) self.logLayer = LogisticRegression( input=(1 - self.dropout_factor[-1]) * self.layers[-1].output, n_in=self.hidden_layers_sizes[-1], n_out=self.n_outs, W=self.dropout_logLayer.W, b=self.dropout_logLayer.b) self.dropout_layers.append(self.dropout_logLayer) self.layers.append(self.logLayer) self.params.extend(self.dropout_logLayer.params) self.delta_params.extend(self.dropout_logLayer.delta_params) # compute the cost self.finetune_cost = self.dropout_logLayer.negative_log_likelihood(self.y) self.errors = self.logLayer.errors(self.y) if self.l1_reg is not None: for i in range(self.hidden_layers_number): W = self.layers[i].W self.finetune_cost += self.l1_reg * (abs(W).sum()) if self.l2_reg is not None: for i in range(self.hidden_layers_number): W = self.layers[i].W self.finetune_cost += self.l2_reg * T.sqr(W).sum()
def __init__(self, numpy_rng, theano_rng=None, n_ins=784, hidden_layers_sizes=[500, 500], n_outs=10, activation = T.nnet.sigmoid, input_dropout_factor = 0, dropout_factor = [0.2,0.2,0.2,0.2,0.2,0.2,0.2], do_maxout = False, pool_size = 1, max_col_norm = None, l1_reg = None, l2_reg = None): self.sigmoid_layers = [] self.dropout_layers = [] self.params = [] self.delta_params = [] self.n_layers = len(hidden_layers_sizes) self.max_col_norm = max_col_norm self.l1_reg = l1_reg self.l2_reg = l2_reg self.input_dropout_factor = input_dropout_factor self.dropout_factor = dropout_factor 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') self.y = T.ivector('y') for i in xrange(self.n_layers): # construct the sigmoidal layer if i == 0: input_size = n_ins layer_input = self.x if input_dropout_factor > 0.0: dropout_layer_input = _dropout_from_layer(theano_rng, self.x, input_dropout_factor) else: dropout_layer_input = self.x else: input_size = hidden_layers_sizes[i - 1] layer_input = (1 - self.dropout_factor[i - 1]) * self.sigmoid_layers[-1].output dropout_layer_input = self.dropout_layers[-1].dropout_output if do_maxout == False: dropout_layer = DropoutHiddenLayer(rng=numpy_rng, input=dropout_layer_input, n_in=input_size, n_out=hidden_layers_sizes[i], activation= activation, dropout_factor=self.dropout_factor[i]) sigmoid_layer = HiddenLayer(rng=numpy_rng, input=layer_input, n_in=input_size, n_out=hidden_layers_sizes[i], activation=activation, W=dropout_layer.W, b=dropout_layer.b) else: dropout_layer = DropoutHiddenLayer(rng=numpy_rng, input=dropout_layer_input, n_in=input_size, n_out=hidden_layers_sizes[i] * pool_size, activation= (lambda x: 1.0*x), dropout_factor=self.dropout_factor[i], do_maxout = True, pool_size = pool_size) sigmoid_layer = HiddenLayer(rng=numpy_rng, input=layer_input, n_in=input_size, n_out=hidden_layers_sizes[i] * pool_size, activation= (lambda x: 1.0*x), W=dropout_layer.W, b=dropout_layer.b, do_maxout = True, pool_size = pool_size) # add the layer to our list of layers self.sigmoid_layers.append(sigmoid_layer) self.dropout_layers.append(dropout_layer) self.params.extend(dropout_layer.params) self.delta_params.extend(dropout_layer.delta_params) # We now need to add a logistic layer on top of the MLP self.dropout_logLayer = LogisticRegression( input=self.dropout_layers[-1].dropout_output, n_in=hidden_layers_sizes[-1], n_out=n_outs) self.logLayer = LogisticRegression( input=(1 - self.dropout_factor[-1]) * self.sigmoid_layers[-1].output, n_in=hidden_layers_sizes[-1], n_out=n_outs, W=self.dropout_logLayer.W, b=self.dropout_logLayer.b) self.dropout_layers.append(self.dropout_logLayer) self.sigmoid_layers.append(self.logLayer) self.params.extend(self.dropout_logLayer.params) self.delta_params.extend(self.dropout_logLayer.delta_params) # compute the cost self.finetune_cost = self.dropout_logLayer.negative_log_likelihood(self.y) self.errors = self.logLayer.errors(self.y) if self.l1_reg is not None: for i in xrange(self.n_layers): W = self.params[i * 2] self.finetune_cost += self.l1_reg * (abs(W).sum()) if self.l2_reg is not None: for i in xrange(self.n_layers): W = self.params[i * 2] self.finetune_cost += self.l2_reg * T.sqr(W).sum()
def __init__(self, numpy_rng, theano_rng=None, n_ins=784, hidden_layers_sizes=[500, 500], n_outs=10, activation = T.nnet.sigmoid, input_dropout_factor = 0, dropout_factor = [0.2,0.2,0.2,0.2,0.2,0.2,0.2], adv_activation = None, max_col_norm = None, l1_reg = None, l2_reg = None): super(DNN_Dropout, self).__init__() self.layers = [] self.dropout_layers = [] self.n_layers = len(hidden_layers_sizes) self.max_col_norm = max_col_norm self.l1_reg = l1_reg self.l2_reg = l2_reg self.input_dropout_factor = input_dropout_factor self.dropout_factor = dropout_factor 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') self.y = T.ivector('y') for i in xrange(self.n_layers): # construct the sigmoidal layer if i == 0: input_size = n_ins layer_input = self.x if input_dropout_factor > 0.0: dropout_layer_input = _dropout_from_layer(theano_rng, self.x, input_dropout_factor) else: dropout_layer_input = self.x else: input_size = hidden_layers_sizes[i - 1] layer_input = (1 - self.dropout_factor[i - 1]) * self.layers[-1].output dropout_layer_input = self.dropout_layers[-1].dropout_output if not adv_activation is None: dropout_layer = DropoutHiddenLayer(rng=numpy_rng, input=dropout_layer_input, n_in=input_size, n_out=hidden_layers_sizes[i] * adv_activation['pool_size'], activation= activation, adv_activation_method = adv_activation['method'], pool_size = adv_activation['pool_size'], pnorm_order = adv_activation['pnorm_order'], dropout_factor=self.dropout_factor[i]) sigmoid_layer = HiddenLayer(rng=numpy_rng, input=layer_input, n_in=input_size, n_out=hidden_layers_sizes[i] * adv_activation['pool_size'], activation=activation, adv_activation_method = adv_activation['method'], pool_size = adv_activation['pool_size'], pnorm_order = adv_activation['pnorm_order'], W=dropout_layer.W, b=dropout_layer.b) else: dropout_layer = DropoutHiddenLayer(rng=numpy_rng, input=dropout_layer_input, n_in=input_size, n_out=hidden_layers_sizes[i], activation= activation, dropout_factor=self.dropout_factor[i]) sigmoid_layer = HiddenLayer(rng=numpy_rng, input=layer_input, n_in=input_size, n_out=hidden_layers_sizes[i] , activation= activation, W=dropout_layer.W, b=dropout_layer.b) # add the layer to our list of layers self.layers.append(sigmoid_layer) self.dropout_layers.append(dropout_layer) self.params.extend(dropout_layer.params) self.delta_params.extend(dropout_layer.delta_params) # We now need to add a logistic layer on top of the MLP self.dropout_logLayer = LogisticRegression( input=self.dropout_layers[-1].dropout_output, n_in=hidden_layers_sizes[-1], n_out=n_outs) self.logLayer = LogisticRegression( input=(1 - self.dropout_factor[-1]) * self.layers[-1].output, n_in=hidden_layers_sizes[-1], n_out=n_outs, W=self.dropout_logLayer.W, b=self.dropout_logLayer.b) self.dropout_layers.append(self.dropout_logLayer) self.layers.append(self.logLayer) self.params.extend(self.dropout_logLayer.params) self.delta_params.extend(self.dropout_logLayer.delta_params) # compute the cost self.finetune_cost = self.dropout_logLayer.negative_log_likelihood(self.y) self.errors = self.logLayer.errors(self.y) self.output = self.logLayer.prediction(); self.features = self.layers[-2].output; self.features_dim = self.layers[-2].n_out if self.l1_reg is not None: self.__l1Regularization__(); if self.l2_reg is not None: self.__l2Regularization__();
def __init__(self, numpy_rng, theano_rng, batch_size, n_outs,conv_layer_configs, hidden_layer_configs, use_fast=False,conv_activation = T.nnet.sigmoid,hidden_activation = T.nnet.sigmoid, l1_reg=None,l2_reg=None,max_col_norm=None): super(DropoutCNN, self).__init__(conv_layer_configs,hidden_layer_configs,l1_reg,l2_reg,max_col_norm) if not theano_rng: theano_rng = RandomStreams(numpy_rng.randint(2 ** 30)) for i in xrange(self.conv_layer_num): # construct the convolution layer if i == 0: #is_input layer input = self.x is_input_layer = True else: input = self.layers[-1].output #output of previous layer is_input_layer = False config = conv_layer_configs[i] conv_layer = ConvLayer(numpy_rng=numpy_rng, input=input,input_shape=config['input_shape'], filter_shape=config['filter_shape'],poolsize=config['poolsize'], activation = conv_activation, use_fast = use_fast) self.layers.append(conv_layer) self.conv_layers.append(conv_layer) if config['update']==True: # only few layers of convolution layer are considered for updation self.params.extend(conv_layer.params) self.delta_params.extend(conv_layer.delta_params) hidden_layers = hidden_layer_configs['hidden_layers']; self.conv_output_dim = config['output_shape'][1] * config['output_shape'][2] * config['output_shape'][3] adv_activation_configs = hidden_layer_configs['adv_activation'] #flattening the last convolution output layer self.features = self.conv_layers[-1].output.flatten(2); self.features_dim = self.conv_output_dim; self.dropout_layers = []; self.dropout_factor = hidden_layer_configs['dropout_factor']; self.input_dropout_factor = hidden_layer_configs['input_dropout_factor']; for i in xrange(self.hidden_layer_num): # construct the hidden layer if i == 0: # is first sigmoidla layer input_size = self.conv_output_dim if self.dropout_factor[i] > 0.0: dropout_layer_input = _dropout_from_layer(theano_rng, self.layers[-1].output, self.input_dropout_factor) else: dropout_layer_input = self.features layer_input = self.features else: input_size = hidden_layers[i - 1] # number of hidden neurons in previous layers dropout_layer_input = self.dropout_layers[-1].dropout_output layer_input = (1 - self.dropout_factor[i-1]) * self.layers[-1].output if adv_activation_configs is None: dropout_sigmoid_layer = DropoutHiddenLayer(rng=numpy_rng, input=layer_input,n_in=input_size, n_out = hidden_layers[i], activation=hidden_activation, dropout_factor = self.dropout_factor[i]); sigmoid_layer = HiddenLayer(rng=numpy_rng, input=layer_input,n_in=input_size, n_out = hidden_layers[i], activation=hidden_activation, W=dropout_sigmoid_layer.W, b=dropout_sigmoid_layer.b); else: dropout_sigmoid_layer = DropoutHiddenLayer(rng=numpy_rng, input=layer_input,n_in=input_size, n_out = hidden_layers[i]*adv_activation_configs['pool_size'], activation=hidden_activation, adv_activation_method = adv_activation_configs['method'], pool_size = adv_activation_configs['pool_size'], pnorm_order = adv_activation_configs['pnorm_order'], dropout_factor = self.dropout_factor[i]); sigmoid_layer = HiddenLayer(rng=numpy_rng, input=layer_input,n_in=input_size, n_out = hidden_layers[i]*adv_activation_configs['pool_size'], activation=hidden_activation, adv_activation_method = adv_activation_configs['method'], pool_size = adv_activation_configs['pool_size'], pnorm_order = adv_activation_configs['pnorm_order'], W=dropout_sigmoid_layer.W, b=dropout_sigmoid_layer.b); self.layers.append(sigmoid_layer) self.dropout_layers.append(dropout_sigmoid_layer) self.mlp_layers.append(sigmoid_layer) if config['update']==True: # only few layers of hidden layer are considered for updation self.params.extend(dropout_sigmoid_layer.params) self.delta_params.extend(dropout_sigmoid_layer.delta_params) self.dropout_logLayer = LogisticRegression(input=self.dropout_layers[-1].dropout_output,n_in=hidden_layers[-1],n_out=n_outs) self.logLayer = LogisticRegression( input=(1 - self.dropout_factor[-1]) * self.layers[-1].output, n_in=hidden_layers[-1],n_out=n_outs, W=self.dropout_logLayer.W, b=self.dropout_logLayer.b) self.dropout_layers.append(self.dropout_logLayer) self.layers.append(self.logLayer) self.params.extend(self.dropout_logLayer.params) self.delta_params.extend(self.dropout_logLayer.delta_params) self.finetune_cost = self.dropout_logLayer.negative_log_likelihood(self.y) self.errors = self.logLayer.errors(self.y) self.output = self.logLayer.prediction() #regularization if self.l1_reg is not None: self.__l1Regularization__(self.hidden_layer_num*2); if self.l2_reg is not None: self.__l2Regularization__(self.hidden_layer_num*2);
def __init__(self, numpy_rng, theano_rng=None, cfg=None, dnn_shared=None, shared_layers=[]): self.layers = [] self.dropout_layers = [] self.params = [] self.delta_params = [] self.cfg = cfg self.n_ins = cfg.n_ins self.n_outs = cfg.n_outs self.hidden_layers_sizes = cfg.hidden_layers_sizes self.hidden_layers_number = len(self.hidden_layers_sizes) self.activation = cfg.activation self.do_maxout = cfg.do_maxout self.pool_size = cfg.pool_size self.input_dropout_factor = cfg.input_dropout_factor self.dropout_factor = cfg.dropout_factor self.max_col_norm = cfg.max_col_norm self.l1_reg = cfg.l1_reg self.l2_reg = cfg.l2_reg if not theano_rng: theano_rng = RandomStreams(numpy_rng.randint(2**30)) # allocate symbolic variables for the data self.x = T.matrix('x') self.y = T.ivector('y') for i in xrange(self.hidden_layers_number): # construct the hidden layer if i == 0: input_size = self.n_ins layer_input = self.x if self.input_dropout_factor > 0.0: dropout_layer_input = _dropout_from_layer( theano_rng, self.x, self.input_dropout_factor) else: dropout_layer_input = self.x else: input_size = self.hidden_layers_sizes[i - 1] layer_input = ( 1 - self.dropout_factor[i - 1]) * self.layers[-1].output dropout_layer_input = self.dropout_layers[-1].dropout_output W = None b = None if (i in shared_layers): W = dnn_shared.layers[i].W b = dnn_shared.layers[i].b if self.do_maxout == False: dropout_layer = DropoutHiddenLayer( rng=numpy_rng, input=dropout_layer_input, n_in=input_size, n_out=self.hidden_layers_sizes[i], W=W, b=b, activation=self.activation, dropout_factor=self.dropout_factor[i]) hidden_layer = HiddenLayer(rng=numpy_rng, input=layer_input, n_in=input_size, n_out=self.hidden_layers_sizes[i], activation=self.activation, W=dropout_layer.W, b=dropout_layer.b) else: dropout_layer = DropoutHiddenLayer( rng=numpy_rng, input=dropout_layer_input, n_in=input_size, n_out=self.hidden_layers_sizes[i] * self.pool_size, W=W, b=b, activation=(lambda x: 1.0 * x), dropout_factor=self.dropout_factor[i], do_maxout=True, pool_size=self.pool_size) hidden_layer = HiddenLayer(rng=numpy_rng, input=layer_input, n_in=input_size, n_out=self.hidden_layers_sizes[i] * self.pool_size, activation=(lambda x: 1.0 * x), W=dropout_layer.W, b=dropout_layer.b, do_maxout=True, pool_size=self.pool_size) # add the layer to our list of layers self.layers.append(hidden_layer) self.dropout_layers.append(dropout_layer) self.params.extend(dropout_layer.params) self.delta_params.extend(dropout_layer.delta_params) # We now need to add a logistic layer on top of the MLP self.dropout_logLayer = LogisticRegression( input=self.dropout_layers[-1].dropout_output, n_in=self.hidden_layers_sizes[-1], n_out=self.n_outs) self.logLayer = LogisticRegression( input=(1 - self.dropout_factor[-1]) * self.layers[-1].output, n_in=self.hidden_layers_sizes[-1], n_out=self.n_outs, W=self.dropout_logLayer.W, b=self.dropout_logLayer.b) self.dropout_layers.append(self.dropout_logLayer) self.layers.append(self.logLayer) self.params.extend(self.dropout_logLayer.params) self.delta_params.extend(self.dropout_logLayer.delta_params) # compute the cost self.finetune_cost = self.dropout_logLayer.negative_log_likelihood( self.y) self.errors = self.logLayer.errors(self.y) if self.l1_reg is not None: for i in xrange(self.hidden_layers_number): W = self.layers[i].W self.finetune_cost += self.l1_reg * (abs(W).sum()) if self.l2_reg is not None: for i in xrange(self.hidden_layers_number): W = self.layers[i].W self.finetune_cost += self.l2_reg * T.sqr(W).sum()