我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用lasagne.layers.FlattenLayer()。
def _invert_layer(self, layer, feeder): layer_type = type(layer) if L.get_output_shape(feeder) != L.get_output_shape(layer): feeder = L.ReshapeLayer(feeder, (-1,)+L.get_output_shape(layer)[1:]) if layer_type is L.InputLayer: return self._invert_InputLayer(layer, feeder) elif layer_type is L.FlattenLayer: return self._invert_FlattenLayer(layer, feeder) elif layer_type is L.DenseLayer: return self._invert_DenseLayer(layer, feeder) elif layer_type is L.Conv2DLayer: return self._invert_Conv2DLayer(layer, feeder) elif layer_type is L.DropoutLayer: return self._invert_DropoutLayer(layer, feeder) elif layer_type in [L.MaxPool2DLayer, L.MaxPool1DLayer]: return self._invert_MaxPoolingLayer(layer, feeder) elif layer_type is L.PadLayer: return self._invert_PadLayer(layer, feeder) elif layer_type is L.SliceLayer: return self._invert_SliceLayer(layer, feeder) elif layer_type is L.LocalResponseNormalization2DLayer: return self._invert_LocalResponseNormalisation2DLayer(layer, feeder) elif layer_type is L.GlobalPoolLayer: return self._invert_GlobalPoolLayer(layer, feeder) else: return self._invert_UnknownLayer(layer, feeder)
def create_network(): l = 1000 pool_size = 5 test_size1 = 13 test_size2 = 7 test_size3 = 5 kernel1 = 128 kernel2 = 128 kernel3 = 128 layer1 = InputLayer(shape=(None, 1, 4, l+1024)) layer2_1 = SliceLayer(layer1, indices=slice(0, l), axis = -1) layer2_2 = SliceLayer(layer1, indices=slice(l, None), axis = -1) layer2_3 = SliceLayer(layer2_2, indices = slice(0,4), axis = -2) layer2_f = FlattenLayer(layer2_3) layer3 = Conv2DLayer(layer2_1,num_filters = kernel1, filter_size = (4,test_size1)) layer4 = Conv2DLayer(layer3,num_filters = kernel1, filter_size = (1,test_size1)) layer5 = Conv2DLayer(layer4,num_filters = kernel1, filter_size = (1,test_size1)) layer6 = MaxPool2DLayer(layer5, pool_size = (1,pool_size)) layer7 = Conv2DLayer(layer6,num_filters = kernel2, filter_size = (1,test_size2)) layer8 = Conv2DLayer(layer7,num_filters = kernel2, filter_size = (1,test_size2)) layer9 = Conv2DLayer(layer8,num_filters = kernel2, filter_size = (1,test_size2)) layer10 = MaxPool2DLayer(layer9, pool_size = (1,pool_size)) layer11 = Conv2DLayer(layer10,num_filters = kernel3, filter_size = (1,test_size3)) layer12 = Conv2DLayer(layer11,num_filters = kernel3, filter_size = (1,test_size3)) layer13 = Conv2DLayer(layer12,num_filters = kernel3, filter_size = (1,test_size3)) layer14 = MaxPool2DLayer(layer13, pool_size = (1,pool_size)) layer14_d = DenseLayer(layer14, num_units= 256) layer3_2 = DenseLayer(layer2_f, num_units = 128) layer15 = ConcatLayer([layer14_d,layer3_2]) layer16 = DropoutLayer(layer15,p=0.5) layer17 = DenseLayer(layer16, num_units=256) network = DenseLayer(layer17, num_units= 2, nonlinearity=softmax) return network #random search to initialize the weights
def create_network(): l = 1000 pool_size = 5 test_size1 = 13 test_size2 = 7 test_size3 = 5 kernel1 = 128 kernel2 = 128 kernel3 = 128 layer1 = InputLayer(shape=(None, 1, 4, l+1024)) layer2_1 = SliceLayer(layer1, indices=slice(0, l), axis = -1) layer2_2 = SliceLayer(layer1, indices=slice(l, None), axis = -1) layer2_3 = SliceLayer(layer2_2, indices = slice(0,4), axis = -2) layer2_f = FlattenLayer(layer2_3) layer3 = Conv2DLayer(layer2_1,num_filters = kernel1, filter_size = (4,test_size1)) layer4 = Conv2DLayer(layer3,num_filters = kernel1, filter_size = (1,test_size1)) layer5 = Conv2DLayer(layer4,num_filters = kernel1, filter_size = (1,test_size1)) layer6 = MaxPool2DLayer(layer5, pool_size = (1,pool_size)) layer7 = Conv2DLayer(layer6,num_filters = kernel2, filter_size = (1,test_size2)) layer8 = Conv2DLayer(layer7,num_filters = kernel2, filter_size = (1,test_size2)) layer9 = Conv2DLayer(layer8,num_filters = kernel2, filter_size = (1,test_size2)) layer10 = MaxPool2DLayer(layer9, pool_size = (1,pool_size)) layer11 = Conv2DLayer(layer10,num_filters = kernel3, filter_size = (1,test_size3)) layer12 = Conv2DLayer(layer11,num_filters = kernel3, filter_size = (1,test_size3)) layer13 = Conv2DLayer(layer12,num_filters = kernel3, filter_size = (1,test_size3)) layer14 = MaxPool2DLayer(layer13, pool_size = (1,pool_size)) layer14_d = DenseLayer(layer14, num_units= 256) layer3_2 = DenseLayer(layer2_f, num_units = 128) layer15 = ConcatLayer([layer14_d,layer3_2]) #layer16 = DropoutLayer(layer15,p=0.5) layer17 = DenseLayer(layer15, num_units=256) network = DenseLayer(layer17, num_units= 1, nonlinearity=None) return network #random search to initialize the weights
def _initialize_network(self, img_input_shape, misc_len, output_size, img_input, misc_input=None, **kwargs): input_layers = [] inputs = [img_input] # weights_init = lasagne.init.GlorotUniform("relu") weights_init = lasagne.init.HeNormal("relu") network = ls.InputLayer(shape=img_input_shape, input_var=img_input) input_layers.append(network) network = ls.Conv2DLayer(network, num_filters=32, filter_size=8, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=4) network = ls.Conv2DLayer(network, num_filters=64, filter_size=4, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=2) network = ls.Conv2DLayer(network, num_filters=64, filter_size=3, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=1) if self.misc_state_included: inputs.append(misc_input) network = ls.FlattenLayer(network) misc_input_layer = ls.InputLayer(shape=(None, misc_len), input_var=misc_input) input_layers.append(misc_input_layer) if "additional_misc_layer" in kwargs: misc_input_layer = ls.DenseLayer(misc_input_layer, int(kwargs["additional_misc_layer"]), nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1)) network = ls.ConcatLayer([network, misc_input_layer]) network = ls.DenseLayer(network, 512, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1)) network = ls.DenseLayer(network, output_size, nonlinearity=None, b=lasagne.init.Constant(.1)) return network, input_layers, inputs
def _initialize_network(self, img_input_shape, misc_len, output_size, img_input, misc_input=None, **kwargs): input_layers = [] inputs = [img_input] # weights_init = lasagne.init.GlorotUniform("relu") weights_init = lasagne.init.HeNormal("relu") network = ls.InputLayer(shape=img_input_shape, input_var=img_input) input_layers.append(network) network = ls.Conv2DLayer(network, num_filters=32, filter_size=8, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(.1), stride=4) network = ls.Conv2DLayer(network, num_filters=64, filter_size=4, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(.1), stride=2) network = ls.Conv2DLayer(network, num_filters=64, filter_size=3, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(.1), stride=1) if self.misc_state_included: inputs.append(misc_input) network = ls.FlattenLayer(network) misc_input_layer = ls.InputLayer(shape=(None, misc_len), input_var=misc_input) input_layers.append(misc_input_layer) if "additional_misc_layer" in kwargs: misc_input_layer = ls.DenseLayer(misc_input_layer, int(kwargs["additional_misc_layer"]), nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1)) network = ls.ConcatLayer([network, misc_input_layer]) # Duelling here advanteges_branch = ls.DenseLayer(network, 256, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(.1)) advanteges_branch = ls.DenseLayer(advanteges_branch, output_size, nonlinearity=None, b=lasagne.init.Constant(.1)) state_value_branch = ls.DenseLayer(network, 256, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(.1)) state_value_branch = ls.DenseLayer(state_value_branch, 1, nonlinearity=None, b=lasagne.init.Constant(.1)) network = DuellingMergeLayer([advanteges_branch, state_value_branch]) return network, input_layers, inputs
def _initialize_network(self, img_input_shape, misc_len, output_size, img_input, misc_input=None, **kwargs): input_layers = [] inputs = [img_input] # weights_init = lasagne.init.GlorotUniform("relu") weights_init = lasagne.init.HeNormal("relu") network = ls.InputLayer(shape=img_input_shape, input_var=img_input) input_layers.append(network) network = ls.Conv2DLayer(network, num_filters=32, filter_size=8, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=4) network = ls.Conv2DLayer(network, num_filters=64, filter_size=4, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=2) network = ls.Conv2DLayer(network, num_filters=64, filter_size=3, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=1) network = ls.FlattenLayer(network) if self.misc_state_included: health_inputs = 4 units_per_health_input = 100 layers_for_merge = [] for i in range(health_inputs): oh_input = lasagne.utils.one_hot(misc_input[:, i] - 1, units_per_health_input) health_input_layer = ls.InputLayer(shape=(None, units_per_health_input), input_var=oh_input) inputs.append(oh_input) input_layers.append(health_input_layer) layers_for_merge.append(health_input_layer) misc_input_layer = ls.InputLayer(shape=(None, misc_len - health_inputs), input_var=misc_input[:, health_inputs:]) input_layers.append(misc_input_layer) layers_for_merge.append(misc_input_layer) inputs.append(misc_input[:, health_inputs:]) layers_for_merge.append(network) network = ls.ConcatLayer(layers_for_merge) network = ls.DenseLayer(network, 512, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1)) network = ls.DenseLayer(network, output_size, nonlinearity=None, b=lasagne.init.Constant(.1)) return network, input_layers, inputs
def _initialize_network(self, img_input_shape, misc_len, output_size, img_input, misc_input=None, **kwargs): input_layers = [] inputs = [img_input] # weights_init = lasagne.init.GlorotUniform("relu") weights_init = lasagne.init.HeNormal("relu") network = ls.InputLayer(shape=img_input_shape, input_var=img_input) input_layers.append(network) network = ls.Conv2DLayer(network, num_filters=32, filter_size=8, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=4) network = ls.Conv2DLayer(network, num_filters=64, filter_size=4, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=2) network = ls.Conv2DLayer(network, num_filters=64, filter_size=3, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=1) network = ls.FlattenLayer(network) if self.misc_state_included: health_inputs = 4 units_per_health_input = 100 layers_for_merge = [] for i in range(health_inputs): health_input_layer = ls.InputLayer(shape=(None, 1), input_var=misc_input[:, i:i + 1]) health_layer = ls.DenseLayer(health_input_layer, units_per_health_input, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1)) health_layer = ls.DenseLayer(health_layer, units_per_health_input, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1)) inputs.append(misc_input[:, i:i + 1]) input_layers.append(health_input_layer) layers_for_merge.append(health_layer) misc_input_layer = ls.InputLayer(shape=(None, misc_len - health_inputs), input_var=misc_input[:, health_inputs:]) input_layers.append(misc_input_layer) layers_for_merge.append(misc_input_layer) inputs.append(misc_input[:, health_inputs:]) layers_for_merge.append(network) network = ls.ConcatLayer(layers_for_merge) network = ls.DenseLayer(network, 512, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1)) network = ls.DenseLayer(network, output_size, nonlinearity=None, b=lasagne.init.Constant(.1)) return network, input_layers, inputs
def _initialize_network(self, img_input_shape, misc_len, output_size, img_input, misc_input=None, **kwargs): input_layers = [] inputs = [img_input] # weights_init = lasagne.init.GlorotUniform("relu") weights_init = lasagne.init.HeNormal("relu") network = ls.InputLayer(shape=img_input_shape, input_var=img_input) input_layers.append(network) network = ls.Conv2DLayer(network, num_filters=32, filter_size=8, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=4) network = ls.Conv2DLayer(network, num_filters=64, filter_size=4, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=2) network = ls.Conv2DLayer(network, num_filters=64, filter_size=3, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1), stride=1) network = ls.FlattenLayer(network) if self.misc_state_included: layers_for_merge = [] health_inputs = 4 units_per_health_input = 100 for i in range(health_inputs): oh_input = lasagne.utils.one_hot(misc_input[:, i] - 1, units_per_health_input) health_input_layer = ls.InputLayer(shape=(None, units_per_health_input), input_var=oh_input) inputs.append(oh_input) input_layers.append(health_input_layer) layers_for_merge.append(health_input_layer) time_inputs = 4 # TODO set this somewhere else cause it depends on skiprate and timeout .... units_pertime_input = 525 for i in range(health_inputs,health_inputs+time_inputs): oh_input = lasagne.utils.one_hot(misc_input[:, i] - 1, units_pertime_input) time_input_layer = ls.InputLayer(shape=(None, units_pertime_input), input_var=oh_input) inputs.append(oh_input) input_layers.append(time_input_layer) layers_for_merge.append(time_input_layer) other_misc_input = misc_input[:, health_inputs+time_inputs:] other_misc_shape = (None, misc_len - health_inputs-time_inputs) other_misc_input_layer = ls.InputLayer(shape=other_misc_shape, input_var=other_misc_input) input_layers.append(other_misc_input_layer) layers_for_merge.append(other_misc_input_layer) inputs.append(other_misc_input) layers_for_merge.append(network) network = ls.ConcatLayer(layers_for_merge) network = ls.DenseLayer(network, 512, nonlinearity=rectify, W=weights_init, b=lasagne.init.Constant(0.1)) network = ls.DenseLayer(network, output_size, nonlinearity=None, b=lasagne.init.Constant(.1)) return network, input_layers, inputs
