Python LogisticRegression.errors方法代码示例

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]

#.........这里部分代码省略.........
                                input=layer_input,
                                n_in=input_size,
                                n_out=tower2_hidden_layers_sizes[i],
                                activation=T.nnet.sigmoid)
            # add the layer to our list of layers
            self.tower2_layers.append(layer)
            self.params.extend(layer.params)
            self.delta_params.extend(layer.delta_params)

        for i in xrange(len(upper_hidden_layers_sizes)):
            # construct the sigmoidal layer
            if i == 0:
                input_size = tower1_hidden_layers_sizes[-1] + tower2_hidden_layers_sizes[-1]
                layer_input = T.concatenate([self.tower1_layers[-1].output, self.tower2_layers[-1].output], axis=1)
            else:
                input_size = upper_hidden_layers_sizes[i - 1]
                layer_input = self.upper_layers[-1].output

            sigmoid_layer = HiddenLayer(rng=numpy_rng,
                                        input=layer_input,
                                        n_in=input_size,
                                        n_out=upper_hidden_layers_sizes[i],
                                        activation=activation)
            # add the layer to our list of layers
            self.upper_layers.append(sigmoid_layer)
            self.params.extend(sigmoid_layer.params)
            self.delta_params.extend(sigmoid_layer.delta_params)
        # We now need to add a logistic layer on top of the MLP
        self.logLayer = LogisticRegression(
                         input=self.upper_layers[-1].output,
                         n_in=upper_hidden_layers_sizes[-1], n_out=n_outs)

        self.upper_layers.append(self.logLayer)
        self.params.extend(self.logLayer.params)
        self.delta_params.extend(self.logLayer.delta_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)
        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 build_finetune_functions(self, train_shared_xy, valid_shared_xy, batch_size):

        (train_set_x, train_set_y) = train_shared_xy
        (valid_set_x, valid_set_y) = valid_shared_xy

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

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

        # compute list of fine-tuning updates
        updates = collections.OrderedDict()
        for dparam, gparam in zip(self.delta_params, gparams):
            updates[dparam] = momentum * dparam - gparam*learning_rate
        for dparam, param in zip(self.delta_params, self.params):
            updates[param] = param + updates[dparam]

        if self.max_col_norm is not None:
            for i in xrange(self.n_layers):
                W = self.params[i * 2]
                if W in updates:
                    updated_W = updates[W]
                    col_norms = T.sqrt(T.sum(T.sqr(updated_W), axis=0))
                    desired_norms = T.clip(col_norms, 0, self.max_col_norm)
                    updates[W] = updated_W * (desired_norms / (1e-7 + col_norms))

        train_fn = theano.function(inputs=[index, theano.Param(learning_rate, default = 0.0001),
              theano.Param(momentum, default = 0.5)],
              outputs=self.errors,
              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]})

        valid_fn = theano.function(inputs=[index],
              outputs=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]})

        return train_fn, valid_fn

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]
class DNN(object):

    def __init__(self, numpy_rng, theano_rng=None,
                 cfg = None,  # the network configuration
                 dnn_shared = None, shared_layers=[], input = None):

        self.layers = []
        self.params = []
        self.delta_params = []

        self.rnn_layerX = 2
        print "Use DRN"

        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.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
        if input == None:
            self.x = T.matrix('x')
        else:
            self.x = input 
        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
            else:
                input_size = self.hidden_layers_sizes[i - 1]
                layer_input = self.layers[-1].output

            W = None; b = None
            if (i in shared_layers) :
                W = dnn_shared.layers[i].W; b = dnn_shared.layers[i].b
            if i == self.rnn_layerX:
                hidden_layer = RnnLayer(rng=numpy_rng,
                                        input=layer_input,
                                        n_in=input_size,
                                        n_out=self.hidden_layers_sizes[i],
                                        W = W, b = b,
                                        activation=self.activation) 
            else:
                if self.do_maxout == True:
                    hidden_layer = HiddenLayer(rng=numpy_rng,
                                        input=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),
                                        do_maxout = True, pool_size = self.pool_size)
                else:
                    hidden_layer = HiddenLayer(rng=numpy_rng,
                                        input=layer_input,
                                        n_in=input_size,
                                        n_out=self.hidden_layers_sizes[i],
                                        W = W, b = b,
                                        activation=self.activation)
            # add the layer to our list of layers
            self.layers.append(hidden_layer)
            self.params.extend(hidden_layer.params)
            self.delta_params.extend(hidden_layer.delta_params)
        # We now need to add a logistic layer on top of the MLP
        self.logLayer = LogisticRegression(
                         input=self.layers[-1].output,
                         n_in=self.hidden_layers_sizes[-1], n_out=self.n_outs)

        if self.n_outs > 0:
            self.layers.append(self.logLayer)
            self.params.extend(self.logLayer.params)
            self.delta_params.extend(self.logLayer.delta_params)
       
        # compute the cost for second phase of training,
        # defined as the negative log likelihood
        self.finetune_cost = self.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()


    def build_finetune_functions(self, train_shared_xy, valid_shared_xy, batch_size):
#.........这里部分代码省略.........

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]

#.........这里部分代码省略.........
                                        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 build_finetune_functions(self, train_shared_xy, valid_shared_xy, batch_size):

        (train_set_x, train_set_y) = train_shared_xy
        (valid_set_x, valid_set_y) = valid_shared_xy

        index = T.lscalar('index')  # index to a [mini]batch
        updates = self.cfg.lrate.getOptimizerUpdates(self.finetune_cost,self.delta_params,self.params)
        for dparam, param in zip(self.delta_params, self.params):
            updates[param] = param + updates[dparam]

        if self.max_col_norm is not None:
            for i in range(self.hidden_layers_number):
                W = self.layers[i].W
                if W in updates:
                    updated_W = updates[W]
                    col_norms = T.sqrt(T.sum(T.sqr(updated_W), axis=0))
                    desired_norms = T.clip(col_norms, 0, self.max_col_norm)
                    updates[W] = updated_W * (desired_norms / (1e-7 + col_norms))

        train_fn = theano.function(inputs=[index],
              outputs=self.errors,

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]

#.........这里部分代码省略.........
                                    input=self.ivec_layers[-1].output,
                                    n_in=ivec_layers_sizes[-1],
                                    n_out=n_ins,
                                    activation=linear_func)
        self.ivec_layers.append(sigmoid_layer)
        if 0 in update_part:
            self.params.extend(sigmoid_layer.params)
            self.delta_params.extend(sigmoid_layer.delta_params)


        for i in xrange(self.conv_layer_num):
            if i == 0:
                input = self.raw + self.ivec_layers[-1].output 
            else:
                input = self.conv_layers[-1].output
            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, flatten = config['flatten'], use_fast = use_fast)
	    self.conv_layers.append(conv_layer)
            if 1 in update_part:
	        self.params.extend(conv_layer.params)
                self.delta_params.extend(conv_layer.delta_params)

        self.conv_output_dim = config['output_shape'][1] * config['output_shape'][2] * config['output_shape'][3]

        for i in xrange(self.full_layer_num):
            # construct the sigmoidal layer
            if i == 0:
                input_size = self.conv_output_dim
                layer_input = self.conv_layers[-1].output
            else:
                input_size = hidden_layers_sizes[i - 1]
                layer_input = self.full_layers[-1].output

            sigmoid_layer = HiddenLayer(rng=numpy_rng,
                                        input=layer_input,
                                        n_in=input_size,
                                        n_out=hidden_layers_sizes[i],
                                        activation=full_activation)
            # add the layer to our list of layers
            self.full_layers.append(sigmoid_layer)
            if 1 in update_part:
                self.params.extend(sigmoid_layer.params)
                self.delta_params.extend(sigmoid_layer.delta_params)
	# We now need to add a logistic layer on top of the MLP
	self.logLayer = LogisticRegression(
			       input=self.full_layers[-1].output,
			       n_in=hidden_layers_sizes[-1], n_out=n_outs)
        self.full_layers.append(self.logLayer)
        if 1 in update_part:
            self.params.extend(self.logLayer.params)
            self.delta_params.extend(self.logLayer.delta_params)

        self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)

        self.errors = self.logLayer.errors(self.y)

    def kl_divergence(self, p, p_hat):
        return p * T.log(p / p_hat) + (1 - p) * T.log((1 - p) / (1 - p_hat))

    def build_finetune_functions(self, train_shared_xy, valid_shared_xy, batch_size):

        (train_set_x, train_set_y) = train_shared_xy
        (valid_set_x, valid_set_y) = valid_shared_xy

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

        # 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 dparam, gparam in zip(self.delta_params, gparams):
            updates[dparam] = momentum * dparam - gparam*learning_rate
        for dparam, param in zip(self.delta_params, self.params):
            updates[param] = param + updates[dparam]

        train_fn = theano.function(inputs=[index, theano.Param(learning_rate, default = 0.0001),
              theano.Param(momentum, default = 0.5)],
              outputs=self.errors,
              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]})

        valid_fn = theano.function(inputs=[index],
              outputs=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]})

        return train_fn, valid_fn

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]
    def __init__(self, numpy_rng, theano_rng=None,
                 cfg = None,  # the network configuration
                 dnn_shared = None, shared_layers=[], input = None):

        self.cfg = cfg
        self.params = []
        self.delta_params   = []
        self.n_ins = cfg.n_ins; self.n_outs = cfg.n_outs
        self.l1_reg = cfg.l1_reg
        self.l2_reg = cfg.l2_reg
        self.do_maxout = cfg.do_maxout; self.pool_size = cfg.pool_size
        self.max_col_norm = cfg.max_col_norm
        print self.max_col_norm

        self.layers = []
        self.lstm_layers = []
        self.fc_layers = []

        # 1. lstm
        self.lstm_layers_sizes = cfg.lstm_layers_sizes
        self.lstm_layers_number = len(self.lstm_layers_sizes)
        # 2. dnn
        self.hidden_layers_sizes = cfg.hidden_layers_sizes
        self.hidden_layers_number = len(self.hidden_layers_sizes)
        self.activation = cfg.activation


        if not theano_rng:
            theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
        if input == None:
            self.x = T.matrix('x')
        else:
            self.x = input 
        self.y = T.ivector('y')

        #######################
        # build lstm layers   #
        #######################
        print '1. start to build lstm layer: '+ str(self.lstm_layers_number)
        for i in xrange(self.lstm_layers_number):
            if i == 0:
                input_size = self.n_ins
                input = self.x
            else:
                input_size = self.lstm_layers_sizes[i - 1]
                input = self.layers[-1].output
            lstm_layer = LSTMLayer(rng=numpy_rng, input=input, n_in=input_size, n_out=self.lstm_layers_sizes[i])
            print '\tbuild lstm layer: ' + str(input_size) +' x '+ str(lstm_layer.n_out)
            self.layers.append(lstm_layer)
            self.lstm_layers.append(lstm_layer)
            self.params.extend(lstm_layer.params)
            self.delta_params.extend(lstm_layer.delta_params)
        print '1. finish lstm layer: '+ str(self.layers[-1].n_out)

        #######################
        # build dnnv layers   #
        #######################
        #print '2. start to build dnnv layer: '+ str(self.hidden_layers_number)
        #for i in xrange(self.hidden_layers_number):
        #    if i == 0:
        #        input_size = self.layers[-1].n_out
        #    else:
        #        input_size = self.hidden_layers_sizes[i - 1]
        #    input = self.layers[-1].output
        #    fc_layer = HiddenLayer(rng=numpy_rng, input=input, n_in=input_size, n_out=self.hidden_layers_sizes[i], activation=self.activation)
        #    print '\tbuild dnnv layer: ' + str(input_size) +' x '+ str(fc_layer.n_out)
        #    self.layers.append(fc_layer)
        #    self.fc_layers.append(fc_layer)
        #    self.params.extend(fc_layer.params)
        #    self.delta_params.extend(fc_layer.delta_params)
        #print '2. finish dnnv layer: '+ str(self.layers[-1].n_out)

        #######################
        # build log layers   #
        #######################
        print '3. start to build log layer: 1'
        input_size = self.layers[-1].n_out
        input = self.layers[-1].output
        logLayer = LogisticRegression(input=input, n_in=input_size, n_out=self.n_outs)
        print '\tbuild final layer: ' + str(input_size) +' x '+ str(self.n_outs)
        self.layers.append(logLayer)
        self.params.extend(logLayer.params)
        self.delta_params.extend(logLayer.delta_params)
        print '3. finish log layer: '+ str(self.layers[-1].n_out)
        print 'Total layers: '+ str(len(self.layers))

        sys.stdout.flush()

        self.finetune_cost = logLayer.negative_log_likelihood(self.y)
        self.errors = logLayer.errors(self.y)

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]
class CNN(object):

    def __init__(self, numpy_rng, theano_rng=None,
                 batch_size = 256, n_outs=500,
		 sparsity = None, sparsity_weight = None, sparse_layer = 3,
                 conv_layer_configs = [],
                 hidden_layers_sizes=[500, 500],
                 conv_activation = T.nnet.sigmoid,
                 full_activation = T.nnet.sigmoid,
                 use_fast = False):

        self.layers = []
        self.params = []
        self.delta_params   = []

        self.sparsity = sparsity
        self.sparsity_weight = sparsity_weight
        self.sparse_layer = sparse_layer

        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') 
        
        self.conv_layer_num = len(conv_layer_configs)
        self.full_layer_num = len(hidden_layers_sizes)

        for i in xrange(self.conv_layer_num):
            if i == 0:
                input = self.x
                is_input_layer = True
            else:
                input = self.layers[-1].output
                is_input_layer = False
            config = conv_layer_configs[i]
            conv_layer = ConvLayer(numpy_rng=numpy_rng, input=input, is_input_layer = is_input_layer,
			input_shape = config['input_shape'], filter_shape = config['filter_shape'], poolsize = config['poolsize'],
			activation = conv_activation, flatten = config['flatten'])
	    self.layers.append(conv_layer)
	    self.params.extend(conv_layer.params)
            self.delta_params.extend(conv_layer.delta_params)

        self.conv_output_dim = config['output_shape'][1] * config['output_shape'][2] * config['output_shape'][3]

        for i in xrange(self.full_layer_num):
            # construct the sigmoidal layer
            if i == 0:
                input_size = self.conv_output_dim
            else:
                input_size = hidden_layers_sizes[i - 1]
            layer_input = self.layers[-1].output

            sigmoid_layer = HiddenLayer(rng=numpy_rng,
                                        input=layer_input,
                                        n_in=input_size,
                                        n_out=hidden_layers_sizes[i],
                                        activation=full_activation)
            # add the layer to our list of layers
            self.layers.append(sigmoid_layer)
            self.params.extend(sigmoid_layer.params)
            self.delta_params.extend(sigmoid_layer.delta_params)

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

	if self.sparsity_weight is not None:
            sparsity_level = T.extra_ops.repeat(self.sparsity, 630)
	    avg_act = self.sigmoid_layers[sparse_layer].output.mean(axis=0)
	    kl_div = self.kl_divergence(sparsity_level, avg_act)
	    self.finetune_cost = self.logLayer.negative_log_likelihood(self.y) + self.sparsity_weight * kl_div.sum()     
	else:
            self.finetune_cost = self.logLayer.negative_log_likelihood(self.y)

        self.errors = self.logLayer.errors(self.y)

    def kl_divergence(self, p, p_hat):
        return p * T.log(p / p_hat) + (1 - p) * T.log((1 - p) / (1 - p_hat))

    def build_finetune_functions(self, train_shared_xy, valid_shared_xy, batch_size):

        (train_set_x, train_set_y) = train_shared_xy
        (valid_set_x, valid_set_y) = valid_shared_xy

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

        # 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 dparam, gparam in zip(self.delta_params, gparams):
#.........这里部分代码省略.........

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]
    def __init__(self, numpy_rng, theano_rng=None,
                 cfg = None,  # the network configuration
                 dnn_shared = None, shared_layers=[], input = None, draw=None):

        self.cfg = cfg
        self.params = []
        self.delta_params   = []
        self.n_ins = cfg.n_ins; self.n_outs = cfg.n_outs
        self.l1_reg = cfg.l1_reg
        self.l2_reg = cfg.l2_reg
        self.do_maxout = cfg.do_maxout; self.pool_size = cfg.pool_size
        self.max_col_norm = 1
        print self.max_col_norm

        self.layers = []
        self.lstm_layers = []
        self.fc_layers = []
        self.bilayers = []

        # 1. lstm
        self.lstm_layers_sizes = cfg.lstm_layers_sizes
        self.lstm_layers_number = len(self.lstm_layers_sizes)
        # 2. dnn
        self.hidden_layers_sizes = cfg.hidden_layers_sizes
        self.hidden_layers_number = len(self.hidden_layers_sizes)
        self.activation = cfg.activation


        if not theano_rng:
            theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
        if input == None:
            self.x = T.matrix('x')
        else:
            self.x = input 
        self.y = T.ivector('y')

        #######################
        # build lstm layers   #
        #######################
        print '1. start to build AttendLSTMLayer : '+ str(self.lstm_layers_number) + ', n_attendout: '+ str(cfg.batch_size)
        for i in xrange(1):
            if i == 0:
                input_size = self.n_ins
                input = self.x
            else:
                input_size = self.lstm_layers_sizes[i - 1]
                input = self.bilayers[-1].output

            # Forward
            f_lstm_layer = AttendLSTMLayer(rng=numpy_rng, input=input, n_in=input_size, n_out=self.lstm_layers_sizes[i],
                                                steps=cfg.batch_size, draw=draw)
            print '\tbuild f_lstm layer: ' + str(input_size) +' x '+ str(f_lstm_layer.n_out)
            self.layers.append(f_lstm_layer)
            self.lstm_layers.append(f_lstm_layer)
            self.params.extend(f_lstm_layer.params)
            self.delta_params.extend(f_lstm_layer.delta_params)

            # Backward
            b_lstm_layer = AttendLSTMLayer(rng=numpy_rng, input=input, n_in=input_size, n_out=self.lstm_layers_sizes[i], backwards=True,
                                                steps=cfg.batch_size, draw=draw)
            print '\tbuild b_lstm layer: ' + str(input_size) +' x '+ str(b_lstm_layer.n_out)
            self.layers.append(b_lstm_layer)
            self.lstm_layers.append(b_lstm_layer)
            self.params.extend(b_lstm_layer.params)
            self.delta_params.extend(b_lstm_layer.delta_params)

            # Sum forward + backward
            bi_layer = SUMLayer(finput=f_lstm_layer.output,binput=b_lstm_layer.output[::-1], n_out=self.lstm_layers_sizes[i - 1])
            self.bilayers.append(bi_layer)
            print '\tbuild sum layer: ' + str(input_size) +' x '+ str(bi_layer.n_out)

        print '1. finish AttendLSTMLayer: '+ str(self.bilayers[-1].n_out)

        #######################
        # build log layers   #
        #######################
        print '3. start to build log layer: 1'
        input_size = self.bilayers[-1].n_out
        input = self.bilayers[-1].output
        logLayer = LogisticRegression(input=input, n_in=input_size, n_out=self.n_outs)
        print '\tbuild final layer: ' + str(input_size) +' x '+ str(self.n_outs)
        self.layers.append(logLayer)
        self.params.extend(logLayer.params)
        self.delta_params.extend(logLayer.delta_params)
        print '3. finish log layer: '+ str(self.bilayers[-1].n_out)
        print 'Total layers: '+ str(len(self.layers))

        sys.stdout.flush()

        self.finetune_cost = logLayer.negative_log_likelihood(self.y)
        self.errors = logLayer.errors(self.y)

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]

#.........这里部分代码省略.........
                                        activation=activation)

            # add the layer to our list of layers
            self.layers.append(sigmoid_layer)

            # the parameters of the sigmoid_layers are parameters of the DBN. 
            # The visible biases in the RBM are parameters of those RBMs, 
            # but not of the DBN.
            self.params.extend(sigmoid_layer.params)
            self.delta_params.extend(sigmoid_layer.delta_params)

            # Construct an RBM that shared weights with this layer
            # the first layer could be Gaussian-Bernoulli RBM
            # other layers are Bernoulli-Bernoulli RBMs
            if i == 0 and first_layer_gb:
                rbm_layer = GBRBM(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,
                              hbias=sigmoid_layer.b)
            else:
                rbm_layer = RBM(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,
                              hbias=sigmoid_layer.b)
            self.rbm_layers.append(rbm_layer)            

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

        # compute the cost for second phase of training, defined as the
        # negative log likelihood of the logistic regression (output) layer
        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)

        self.output = self.logLayer.prediction();
        self.features = self.layers[-2].output;
        self.features_dim = self.layers[-2].n_out

    def pretraining_functions(self, train_set_x, batch_size, weight_cost):
        '''Generates a list of functions, for performing one step of
        gradient descent at a given layer. The function will require
        as input the minibatch index, and to train an RBM 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 var. that contains all datapoints used
                            for training the RBM
        :type batch_size: int
        :param batch_size: size of a [mini]batch
        :param weight_cost: weigth cost

        '''

        # index to a [mini]batch
        index = T.lscalar('index')  # index to a minibatch
        momentum = T.scalar('momentum')
        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 rbm in self.rbm_layers:

            # get the cost and the updates list
            # using CD-k here (persisent=None,k=1) for training each RBM.
            r_cost, fe_cost, updates = rbm.get_cost_updates(batch_size, learning_rate,
                                                            momentum, weight_cost)
            # compile the theano function
            fn = theano.function(inputs=[index,
                              theano.Param(learning_rate, default=0.0001),
                              theano.Param(momentum, default=0.5)],
                              outputs= [r_cost, fe_cost],
                              updates=updates,
                              givens={self.x: train_set_x[batch_begin:batch_end]})
            # append function to the list of functions
            pretrain_fns.append(fn)

        return pretrain_fns

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]
class DNN(nnet):

    def __init__(self, numpy_rng, theano_rng=None, n_ins=784,
                 hidden_layers_sizes=[500, 500], n_outs=10,
                 activation = T.nnet.sigmoid, adv_activation = None,
                 max_col_norm = None, l1_reg = None, l2_reg = None):

        super(DNN, self).__init__()
        
        self.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

        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
            else:
                input_size = hidden_layers_sizes[i - 1]
                layer_input = self.layers[-1].output

            if not adv_activation is  None:
                sigmoid_layer = HiddenLayer(rng=numpy_rng,
                                        input=layer_input,
                                        n_in=input_size,
                                        n_out=hidden_layers_sizes[i] * pool_size,
                                        activation = activation,
                                        adv_activation_method = adv_activation['method'],
                                        pool_size = adv_activation['pool_size'],
                                        pnorm_order = adv_activation['pnorm_order'])
            else:
                sigmoid_layer = HiddenLayer(rng=numpy_rng,
                                        input=layer_input,
                                        n_in=input_size,
                                        n_out=hidden_layers_sizes[i],
                                        activation=activation)
                                        
            # add the layer to our list of layers
            self.layers.append(sigmoid_layer)
            self.params.extend(sigmoid_layer.params)
            self.delta_params.extend(sigmoid_layer.delta_params)
            
        # We now need to add a logistic layer on top of the MLP
        self.logLayer = LogisticRegression(
                         input=self.layers[-1].output,
                         n_in=hidden_layers_sizes[-1], n_out=n_outs)

        self.layers.append(self.logLayer)
        self.params.extend(self.logLayer.params)
        self.delta_params.extend(self.logLayer.delta_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)
        self.errors = self.logLayer.errors(self.y)

        if self.l1_reg is not None:
            self.__l1Regularization__();

        if self.l2_reg is not None:
            self.__l2Regularization__();

        self.output = self.logLayer.prediction();
        self.features = self.layers[-2].output;
        self.features_dim = self.layers[-2].n_out

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]
class DNN_Dropout(object):

    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]
#.........这里部分代码省略.........

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]
class DropoutCNN(CNNBase):
	""" Instantiation of Convolution neural network ... """
	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)
#.........这里部分代码省略.........

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]

#.........这里部分代码省略.........
            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 save(self,filename,start_layer = 0,max_layer_num = -1,withfinal=True):
        nnet_dict = {}
        if max_layer_num == -1:
           max_layer_num = self.n_layers

        for i in range(start_layer, max_layer_num):
           dict_a = str(i) + ' W'
           if i == 0:
               nnet_dict[dict_a] = _array2string((1.0 - self.input_dropout_factor) * (
                self.layers[i].params[0].get_value()))
           else:
               nnet_dict[dict_a] = _array2string((1.0 - self.dropout_factor[i - 1])* (
                self.layers[i].params[0].get_value()))
           dict_a = str(i) + ' b'
           nnet_dict[dict_a] = _array2string(self.layers[i].params[1].get_value())

        if withfinal: 
            dict_a = 'logreg W'
            nnet_dict[dict_a] = _array2string((1.0 - self.dropout_factor[-1])* (
                self.logLayer.params[0].get_value()))
            dict_a = 'logreg b'
            nnet_dict[dict_a] = _array2string(self.logLayer.params[1].get_value())
   
        with open(filename, 'wb') as fp:
            json.dump(nnet_dict, fp, indent=2, sort_keys = True)
            fp.flush()

    def load(self,filename,start_layer = 0,max_layer_num = -1,withfinal=True):
        nnet_dict = {}
        if max_layer_num == -1:
            max_layer_num = self.n_layers

        with open(filename, 'rb') as fp:
            nnet_dict = json.load(fp)
        
        for i in xrange(max_layer_num):
            dict_key = str(i) + ' W'
            self.layers[i].params[0].set_value(numpy.asarray(_string2array(nnet_dict[dict_key]),
                dtype=theano.config.floatX))
            dict_key = str(i) + ' b' 
            self.layers[i].params[1].set_value(numpy.asarray(_string2array(nnet_dict[dict_key]),
                dtype=theano.config.floatX))

        if withfinal:
            dict_key = 'logreg W'
            self.logLayer.params[0].set_value(numpy.asarray(_string2array(nnet_dict[dict_key]),
                dtype=theano.config.floatX))
            dict_key = 'logreg b'
            self.logLayer.params[1].set_value(numpy.asarray(_string2array(nnet_dict[dict_key]),
                dtype=theano.config.floatX))

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]

#.........这里部分代码省略.........
            self.ivec_params.extend(ivec_layer.params)
            self.ivec_delta_params.extend(ivec_layer.delta_params)

        # the final output layer which has the same dimension as the input features
        linear_func = lambda x: x
        ivec_layer = HiddenLayer(rng=numpy_rng,
                                 input=self.ivec_layers[-1].output,
                                 n_in=ivec_layers_sizes[-1],
                                 n_out=n_ins,
                                 activation=linear_func)
        self.ivec_layers.append(ivec_layer)
        self.ivec_params.extend(ivec_layer.params)
        self.ivec_delta_params.extend(ivec_layer.delta_params)

        # construct the DNN (canonical model)
        for i in xrange(self.n_layers):
            if i == 0:
                input_size = n_ins
                layer_input = self.raw + self.ivec_layers[-1].output
            else:
                input_size = hidden_layers_sizes[i - 1]
                layer_input = self.sigmoid_layers[-1].output

            if do_maxout == True:
                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),
                                        do_maxout = True, pool_size = pool_size)
            elif do_pnorm == True:
                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),
                                        do_pnorm = True, pool_size = pool_size, pnorm_order = pnorm_order)
            else:
                sigmoid_layer = HiddenLayer(rng=numpy_rng,
                                        input=layer_input,
                                        n_in=input_size,
                                        n_out=hidden_layers_sizes[i],
                                        activation=activation)
            # add the layer to our list of layers
            self.sigmoid_layers.append(sigmoid_layer)
            self.sigmoid_params.extend(sigmoid_layer.params)
            self.sigmoid_delta_params.extend(sigmoid_layer.delta_params)
        # 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.sigmoid_layers.append(self.logLayer)
        self.sigmoid_params.extend(self.logLayer.params)
        self.sigmoid_delta_params.extend(self.logLayer.delta_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)
        self.errors = self.logLayer.errors(self.y)

    def build_finetune_functions(self, train_shared_xy, valid_shared_xy, batch_size):

        (train_set_x, train_set_y) = train_shared_xy
        (valid_set_x, valid_set_y) = valid_shared_xy

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

        # 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 dparam, gparam in zip(self.delta_params, gparams):
            updates[dparam] = momentum * dparam - gparam*learning_rate
        for dparam, param in zip(self.delta_params, self.params):
            updates[param] = param + updates[dparam]

        train_fn = theano.function(inputs=[index, theano.Param(learning_rate, default = 0.0001),
              theano.Param(momentum, default = 0.5)],
              outputs=self.errors,
              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]})

        valid_fn = theano.function(inputs=[index],
              outputs=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]})

        return train_fn, valid_fn

# 需要导入模块: from layers.logistic_sgd import LogisticRegression [as 别名]
# 或者: from layers.logistic_sgd.LogisticRegression import errors [as 别名]

#.........这里部分代码省略.........

            # 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.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.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)
            self.delta_params.extend(sigmoid_layer.delta_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,
                          activation=T.nnet.sigmoid)
            self.dA_layers.append(dA_layer)

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

        self.output = self.logLayer.prediction();
        self.features = self.layers[-2].output;
        self.features_dim = self.layers[-2].n_out

    def pretraining_functions(self, train_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_x: theano.tensor.TensorType
        :param train_x: Shared variable that contains all datapoints used
                            for training the dA

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

        # 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_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_x[batch_begin:
                                                             batch_end]})
            # append `fn` to the list of functions
            pretrain_fns.append(fn)

        return pretrain_fns