Python keras.regularizers 模块，activity_l2() 实例源码
我们从Python开源项目中，提取了以下19个代码示例，用于说明如何使用keras.regularizers.activity_l2()。
def test_dense():
from keras import regularizers
from keras import constraints
layer_test(core.Dense,
kwargs={'output_dim': 3},
input_shape=(3, 2))
layer_test(core.Dense,
kwargs={'output_dim': 3,
'W_regularizer': regularizers.l2(0.01),
'b_regularizer': regularizers.l1(0.01),
'activity_regularizer': regularizers.activity_l2(0.01),
'W_constraint': constraints.Maxnorm(1),
'b_constraint': constraints.Maxnorm(1)}, 2))
def test_maxout_dense():
from keras import regularizers
from keras import constraints
layer_test(core.MaxoutDense, 2))
layer_test(core.MaxoutDense, 2))
def test_timedistributeddense():
from keras import regularizers
from keras import constraints
layer_test(core.TimedistributedDense,
kwargs={'output_dim': 2, 'input_length': 2}, 2, 3))
layer_test(core.TimedistributedDense, 3))
def test_maxout_dense():
from keras import regularizers
from keras import constraints
layer_test(core.MaxoutDense, 2))
def test_timedistributeddense():
from keras import regularizers
from keras import constraints
layer_test(core.TimedistributedDense, 3))
def __init__(self, dim_in, encoding_dim, sparsity):
input_img = Input(shape=(dim_in,))
regulizer = regularizers.activity_l2(sparsity)
encoded = Dense(encoding_dim, activation='relu', activity_regularizer=regulizer)(input_img)
decoded = Dense(dim_in, activation='sigmoid')(encoded)
self.autoencoder = Model(input=input_img, output=decoded)
self.encoder = Model(input=input_img, output=encoded)
encoded_input = Input(shape=(encoding_dim,))
decoder_layer = self.autoencoder.layers[-1]
self.decoder = Model(input=encoded_input, output=decoder_layer(encoded_input))
self.autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
def test_maxout_dense():
from keras import regularizers
from keras import constraints
layer_test(core.MaxoutDense, 2))
def test_timedistributeddense():
from keras import regularizers
from keras import constraints
layer_test(core.TimedistributedDense, 3))
def test_A_reg():
(X_train, Y_train), (X_test, Y_test), test_ids = get_data()
for reg in [regularizers.activity_l1(), regularizers.activity_l2()]:
model = create_model(activity_reg=reg)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model.fit(X_train, Y_train, batch_size=batch_size,
nb_epoch=nb_epoch, verbose=0)
model.evaluate(X_test[test_ids, :], Y_test[test_ids, verbose=0)
def test_A_reg(self):
for reg in [regularizers.activity_l1(), regularizers.activity_l2()]:
model = create_model(activity_reg=reg)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
model.fit(X_train, nb_epoch=nb_epoch, verbose=0)
model.evaluate(X_test[test_ids, verbose=0)
def test_dense():
from keras import regularizers
from keras import constraints
layer_test(core.Dense, 4,
input_shape=(None, None, 5, 2))
def test_highway():
from keras import regularizers
from keras import constraints
layer_test(core.Highway, 2))
def test_A_reg():
(X_train, optimizer='rmsprop')
assert len(model.losses) == 1
model.fit(X_train, verbose=0)
def test_dense():
from keras import regularizers
from keras import constraints
layer_test(core.Dense, 2))
def test_highway():
from keras import regularizers
from keras import constraints
layer_test(core.Highway, 2))
def test_A_reg(self):
for reg in [regularizers.activity_l1(), verbose=0)
def test_A_reg(self):
for reg in [regularizers.activity_l1(), verbose=0)
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