我们从Python开源项目中，提取了以下17个代码示例，用于说明如何使用keras.regularizers.L1L2。
def __call__(self, inputs): x = self._merge_inputs(inputs) shape = getattr(x, '_keras_shape') replicate_model = self._replicate_model(kl.Input(shape=shape[2:])) x = kl.TimeDistributed(replicate_model)(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Bidirectional(kl.GRU(128, kernel_regularizer=kernel_regularizer, return_sequences=True), merge_mode='concat')(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) gru = kl.GRU(256, kernel_regularizer=kernel_regularizer) x = kl.Bidirectional(gru)(x) x = kl.Dropout(self.dropout)(x) return self._build(inputs, x)
def __call__(self, inputs): x = inputs[0] kernel_regularizer = kr.L1L2(self.l1_decay, self.l2_decay) x = kl.Conv1D(128, 11, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu')(x) x = kl.MaxPooling1D(4)(x) x = kl.Flatten()(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Dense(self.nb_hidden, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu')(x) x = kl.Dropout(self.dropout)(x) return self._build(inputs, x)
def __call__(self, inputs): x = inputs[0] kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(128, 11, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu')(x) x = kl.MaxPooling1D(4)(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(256, 7, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu')(x) x = kl.MaxPooling1D(4)(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) gru = kl.recurrent.GRU(256, kernel_regularizer=kernel_regularizer) x = kl.Bidirectional(gru)(x) x = kl.Dropout(self.dropout)(x) return self._build(inputs, x)
def BiGRU(X_train, y_train, X_test, y_test, gru_units, dense_units, input_shape, \ batch_size, epochs, drop_out, patience): model = Sequential() reg = L1L2(l1=0.2, l2=0.2) model.add(Bidirectional(GRU(units = gru_units, dropout= drop_out, activation='relu', recurrent_regularizer = reg, return_sequences = True), input_shape = input_shape, merge_mode="concat")) model.add(BatchNormalization()) model.add(TimeDistributed(Dense(dense_units, activation='relu'))) model.add(BatchNormalization()) model.add(Bidirectional(GRU(units = gru_units, dropout= drop_out, activation='relu', recurrent_regularizer=reg, return_sequences = True), merge_mode="concat")) model.add(BatchNormalization()) model.add(Dense(units=1)) model.add(GlobalAveragePooling1D()) print(model.summary()) early_stopping = EarlyStopping(monitor="val_loss", patience = patience) model.compile(loss='mse', optimizer= 'adam') history_callback = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs,\ verbose=2, callbacks=[early_stopping], validation_data=[X_test, y_test], shuffle = True) return model, history_callback
def l1l2(l1_weight=0, l2_weight=0): if keras_2: from keras.regularizers import L1L2 return L1L2(l1_weight, l2_weight) else: from keras.regularizers import l1l2 return l1l2(l1_weight, l2_weight)
def __call__(self, inputs): x = inputs[0] kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(128, 11, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu')(x) x = kl.MaxPooling1D(4)(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(256, 3, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu')(x) x = kl.MaxPooling1D(2)(x) x = kl.Flatten()(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Dense(self.nb_hidden, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu')(x) x = kl.Dropout(self.dropout)(x) return self._build(inputs, x)
def __call__(self, inputs): x = inputs[0] kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(128, 11, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu')(x) x = kl.MaxPooling1D(4)(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(256, 3, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu')(x) x = kl.MaxPooling1D(2)(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(512, 3, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu')(x) x = kl.MaxPooling1D(2)(x) x = kl.Flatten()(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Dense(self.nb_hidden, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu')(x) x = kl.Dropout(self.dropout)(x) return self._build(inputs, x)
def __call__(self, inputs): x = inputs[0] kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(128, 11, name='conv1', kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.BatchNormalization(name='bn1')(x) x = kl.Activation('relu', name='act1')(x) x = kl.MaxPooling1D(2, name='pool1')(x) # 124 x = self._res_unit(x, [32, 32, 128], stage=1, block=1, stride=2) x = self._res_unit(x, [32, 32, 128], stage=1, block=2) x = self._res_unit(x, [32, 32, 128], stage=1, block=3) # 64 x = self._res_unit(x, [64, 64, 256], stage=2, block=1, stride=2) x = self._res_unit(x, [64, 64, 256], stage=2, block=2) x = self._res_unit(x, [64, 64, 256], stage=2, block=3) # 32 x = self._res_unit(x, [128, 128, 512], stage=3, block=1, stride=2) x = self._res_unit(x, [128, 128, 512], stage=3, block=2) x = self._res_unit(x, [128, 128, 512], stage=3, block=3) # 16 x = self._res_unit(x, [256, 256, 1024], stage=4, block=1, stride=2) x = kl.GlobalAveragePooling1D()(x) x = kl.Dropout(self.dropout)(x) return self._build(inputs, x)
def _res_unit(self, inputs, nb_filter, size=3, stride=1, stage=1, block=1): name = '%02d-%02d/' % (stage, block) id_name = '%sid_' % (name) res_name = '%sres_' % (name) # Residual branch x = kl.BatchNormalization(name=res_name + 'bn1')(inputs) x = kl.Activation('relu', name=res_name + 'act1')(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(nb_filter, size, name=res_name + 'conv1', border_mode='same', subsample_length=stride, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.BatchNormalization(name=res_name + 'bn2')(x) x = kl.Activation('relu', name=res_name + 'act2')(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(nb_filter, size, name=res_name + 'conv2', border_mode='same', kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) # Identity branch if nb_filter != inputs._keras_shape[-1] or stride > 1: kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) identity = kl.Conv1D(nb_filter, size, name=id_name + 'conv1', border_mode='same', subsample_length=stride, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(inputs) else: identity = inputs x = kl.merge([identity, x], name=name + 'merge', mode='sum') return x
def __call__(self, inputs): x = inputs[0] kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(128, 11, name='conv1', kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.BatchNormalization(name='bn1')(x) x = kl.Activation('relu', name='act1')(x) x = kl.MaxPooling1D(2, name='pool1')(x) # 124 x = self._res_unit(x, 128, stage=1, block=1, stride=2) x = self._res_unit(x, 128, stage=1, block=2) # 64 x = self._res_unit(x, 256, stage=2, block=1, stride=2) # 32 x = self._res_unit(x, 256, stage=3, block=1, stride=2) # 32 x = self._res_unit(x, 512, stage=4, block=1, stride=2) x = kl.GlobalAveragePooling1D()(x) x = kl.Dropout(self.dropout)(x) return self._build(inputs, x)
def __call__(self, inputs): x = inputs[0] kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(128, 11, name='conv1', kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.Activation('relu', name='act1')(x) x = kl.MaxPooling1D(2, name='pool1')(x) # 124 x = self._res_unit(x, [32, 32, 128], stage=1, block=1, stride=2) x = self._res_unit(x, [32, 32, 128], atrous=2, stage=1, block=2) x = self._res_unit(x, [32, 32, 128], atrous=4, stage=1, block=3) # 64 x = self._res_unit(x, [64, 64, 256], stage=2, block=1, stride=2) x = self._res_unit(x, [64, 64, 256], atrous=2, stage=2, block=2) x = self._res_unit(x, [64, 64, 256], atrous=4, stage=2, block=3) # 32 x = self._res_unit(x, [128, 128, 512], stage=3, block=1, stride=2) x = self._res_unit(x, [128, 128, 512], atrous=2, stage=3, block=2) x = self._res_unit(x, [128, 128, 512], atrous=4, stage=3, block=3) # 16 x = self._res_unit(x, [256, 256, 1024], stage=4, block=1, stride=2) x = kl.GlobalAveragePooling1D()(x) x = kl.Dropout(self.dropout)(x) return self._build(inputs, x)
def _replicate_model(self, input): kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Dense(512, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(input) x = kl.Activation('relu')(x) return km.Model(input, x)
def _replicate_model(self, input): kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Dense(256, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(input) x = kl.Activation(self.act_replicate)(x) return km.Model(input, x)
def __call__(self, inputs): x = self._merge_inputs(inputs) shape = getattr(x, '_keras_shape') replicate_model = self._replicate_model(kl.Input(shape=shape[2:])) x = kl.TimeDistributed(replicate_model)(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) gru = kl.GRU(256, kernel_regularizer=kernel_regularizer) x = kl.Bidirectional(gru)(x) x = kl.Dropout(self.dropout)(x) return self._build(inputs, x)
def denseNet(input_dim): base_model = densenet.DenseNet(input_shape=(input_dim, input_dim, 3), classes=17, dropout_rate=0.2, weights=None, include_top=False) x = Dense(17, activation='softmax', kernel_regularizer=regularizers.L1L2(l2=1E-4), bias_regularizer=regularizers.L1L2(l2=1E-4))(base_model.output) model = Model(inputs=base_model.input, outputs=x) # Load model weights_file = "../weights/DenseNet-40-12CIFAR10-tf.h5" if os.path.exists(weights_file): model.load_weights(weights_file) print("Model loaded.") return model
def _res_unit(self, inputs, nb_filter, size=3, stride=1, stage=1, block=1): name = '%02d-%02d/' % (stage, block) id_name = '%sid_' % (name) res_name = '%sres_' % (name) # Residual branch # 1x1 down-sample conv x = kl.BatchNormalization(name=res_name + 'bn1')(inputs) x = kl.Activation('relu', name=res_name + 'act1')(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(nb_filter[0], 1, name=res_name + 'conv1', subsample_length=stride, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) # LxL conv x = kl.BatchNormalization(name=res_name + 'bn2')(x) x = kl.Activation('relu', name=res_name + 'act2')(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(nb_filter[1], size, name=res_name + 'conv2', border_mode='same', kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) # 1x1 up-sample conv x = kl.BatchNormalization(name=res_name + 'bn3')(x) x = kl.Activation('relu', name=res_name + 'act3')(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(nb_filter[2], 1, name=res_name + 'conv3', kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) # Identity branch if nb_filter[-1] != inputs._keras_shape[-1] or stride > 1: kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) identity = kl.Conv1D(nb_filter[2], 1, name=id_name + 'conv1', subsample_length=stride, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(inputs) else: identity = inputs x = kl.merge([identity, x], name=name + 'merge', mode='sum') return x
def _res_unit(self, inputs, nb_filter, size=3, stride=1, atrous=1, stage=1, block=1): name = '%02d-%02d/' % (stage, block) id_name = '%sid_' % (name) res_name = '%sres_' % (name) # Residual branch # 1x1 down-sample conv x = kl.BatchNormalization(name=res_name + 'bn1')(inputs) x = kl.Activation('relu', name=res_name + 'act1')(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(nb_filter[0], 1, name=res_name + 'conv1', subsample_length=stride, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) # LxL conv x = kl.BatchNormalization(name=res_name + 'bn2')(x) x = kl.Activation('relu', name=res_name + 'act2')(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.AtrousConv1D(nb_filter[1], size, atrous_rate=atrous, name=res_name + 'conv2', border_mode='same', kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) # 1x1 up-sample conv x = kl.BatchNormalization(name=res_name + 'bn3')(x) x = kl.Activation('relu', name=res_name + 'act3')(x) kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(nb_filter[2], 1, name=res_name + 'conv3', kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) # Identity branch if nb_filter[-1] != inputs._keras_shape[-1] or stride > 1: kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) identity = kl.Conv1D(nb_filter[2], 1, name=id_name + 'conv1', subsample_length=stride, kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(inputs) else: identity = inputs x = kl.merge([identity, x], name=name + 'merge', mode='sum') return x
