本文整理汇总了Python中keras.layers.ActivityRegularization方法的典型用法代码示例。如果您正苦于以下问题：Python layers.ActivityRegularization方法的具体用法？Python layers.ActivityRegularization怎么用？Python layers.ActivityRegularization使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.layers的用法示例。
在下文中一共展示了layers.ActivityRegularization方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_activity_regularization
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ActivityRegularization [as 别名]
def test_activity_regularization():
layer = layers.ActivityRegularization(l1=0.01, l2=0.01)
# test in functional API
x = layers.Input(shape=(3,))
z = layers.Dense(2)(x)
y = layer(z)
model = Model(x, y)
model.compile('rmsprop', 'mse')
model.predict(np.random.random((2, 3)))
# test serialization
model_config = model.get_config()
model = Model.from_config(model_config)
model.compile('rmsprop', 'mse') 示例2: create_simnet_network
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ActivityRegularization [as 别名]
def create_simnet_network(input_shape, weights):
L2_REGULARIZATION = 0.001
input = Input(shape=input_shape)
# CNN 1
vgg16 = create_vgg16_network(input_shape, weights)
cnn_1 = vgg16(input)
# CNN 2
# Downsample by 4:1
cnn_2 = MaxPooling2D(pool_size=(4, 4))(input)
cnn_2 = Conv2D(128, (3, 3), padding='same', activation='relu')(cnn_2)
cnn_2 = Conv2D(128, (3, 3), padding='same', activation='relu')(cnn_2)
cnn_2 = Conv2D(256, (3, 3), padding='same', activation='relu')(cnn_2)
cnn_2 = Dropout(0.5)(cnn_2)
cnn_2 = Flatten()(cnn_2)
cnn_2 = Dense(1024, activation='relu')(cnn_2)
# CNN 3
# Downsample by 8:1
cnn_3 = MaxPooling2D(pool_size=(8, 8))(input)
cnn_3 = Conv2D(128, (3, 3), padding='same', activation='relu')(cnn_3)
cnn_3 = Conv2D(128, (3, 3), padding='same', activation='relu')(cnn_3)
cnn_3 = Dropout(0.5)(cnn_3)
cnn_3 = Flatten()(cnn_3)
cnn_3 = Dense(512, activation='relu')(cnn_3)
concat_2_3 = concatenate([cnn_2, cnn_3])
concat_2_3 = Dense(1024, activation='relu')(concat_2_3)
l2_reg = ActivityRegularization(l2=L2_REGULARIZATION)(concat_2_3)
concat_1_l2 = concatenate([cnn_1, l2_reg])
output = Dense(4096, activation='relu')(concat_1_l2)
return Model(input, output) 示例3: create_simnetlike_network
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ActivityRegularization [as 别名]
def create_simnetlike_network(input_shape, weights):
L2_REGULARIZATION = 0.005
input = Input(shape=input_shape)
# CNN 1
vgg16 = create_vgglike_network(input_shape, weights)
cnn_1 = vgg16(input)
# CNN 2
# Downsample by 4:1
cnn_2 = MaxPooling2D(pool_size=(4, 4))(input)
cnn_2 = Conv2D(32, (3, 3), padding='same', activation='relu')(cnn_2)
cnn_2 = Conv2D(32, (3, 3), padding='same', activation='relu')(cnn_2)
cnn_2 = Conv2D(64, (3, 3), padding='same', activation='relu')(cnn_2)
cnn_2 = Dropout(0.5)(cnn_2)
cnn_2 = Flatten()(cnn_2)
cnn_2 = Dense(64, activation='relu')(cnn_2)
# CNN 3
# Downsample by 8:1
cnn_3 = MaxPooling2D(pool_size=(8, 8))(input)
cnn_3 = Conv2D(16, (3, 3), padding='same', activation='relu')(cnn_3)
cnn_3 = Conv2D(16, (3, 3), padding='same', activation='relu')(cnn_3)
cnn_3 = Dropout(0.5)(cnn_3)
cnn_3 = Flatten()(cnn_3)
cnn_3 = Dense(32, activation='relu')(cnn_3)
concat_2_3 = concatenate([cnn_2, cnn_3])
concat_2_3 = Dense(128, activation='relu')(concat_2_3)
l2_reg = ActivityRegularization(l2=L2_REGULARIZATION)(concat_2_3)
concat_1_l2 = concatenate([cnn_1, l2_reg])
output = Dense(256, activation='relu')(concat_1_l2)
return Model(input, output) 示例4: regularization
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ActivityRegularization [as 别名]
def regularization(layer, layer_in, layerId, tensor=True):
l1 = layer['params']['l1']
l2 = layer['params']['l2']
out = {layerId: ActivityRegularization(l1=l1, l2=l2)}
if tensor:
out[layerId] = out[layerId](*layer_in)
return out 示例5: test_keras_import
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ActivityRegularization [as 别名]
def test_keras_import(self):
model = Sequential()
model.add(ActivityRegularization(l1=2, input_shape=(10,)))
model.build()
self.keras_type_test(model, 0, 'Regularization') 示例6: test_keras_export
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import ActivityRegularization [as 别名]
def test_keras_export(self):
tests = open(os.path.join(settings.BASE_DIR, 'tests', 'unit', 'keras_app',
'keras_export_test.json'), 'r')
response = json.load(tests)
tests.close()
net = yaml.safe_load(json.dumps(response['net']))
net = {'l0': net['Input3'], 'l1': net['Regularization']}
net['l0']['connection']['output'].append('l1')
inp = data(net['l0'], '', 'l0')['l0']
net = regularization(net['l1'], [inp], 'l1')
model = Model(inp, net['l1'])
self.assertEqual(model.layers[1].__class__.__name__, 'ActivityRegularization')