def transform_model(weight_loss_pix=5e-4):
inputs = Input(shape=( 128, 128, 3))
x1 = Convolution2D(64, 5, 5, border_mode='same')(inputs)
x2 = LeakyReLU(alpha=0.3, name='wkcw')(x1)
x3 = BatchNormalization()(x2)
x4 = Convolution2D(128, 4, 4, border_mode='same', subsample=(2,2))(x3)
x5 = LeakyReLU(alpha=0.3)(x4)
x6 = BatchNormalization()(x5)
x7 = Convolution2D(256, 4, 4, border_mode='same', subsample=(2,2))(x6)
x8 = LeakyReLU(alpha=0.3)(x7)
x9 = BatchNormalization()(x8)
x10 = Deconvolution2D(128, 3, 3, output_shape=(None, 64, 64, 128), border_mode='same', subsample=(2,2))(x9)
x11 = BatchNormalization()(x10)
x12 = Deconvolution2D(64, 3, 3, output_shape=(None, 128, 128, 64), border_mode='same', subsample=(2,2))(x11)
x13 = BatchNormalization()(x12)
x14 = Deconvolution2D(3, 4, 4, output_shape=(None, 128, 128, 3), border_mode='same', activity_regularizer=activity_l1(weight_loss_pix))(x13)
output = merge([inputs, x14], mode='sum')
model = Model(input=inputs, output=output)
return model
python类activity_l1()的实例源码
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 sparse_autoencoder(X, lam=1e-5):
X = X.reshape(X.shape[0], -1)
M, N = X.shape
inputs = Input(shape=(N,))
h = Dense(64, activation='sigmoid', activity_regularizer=activity_l1(lam))(inputs)
outputs = Dense(N)(h)
model = Model(input=inputs, output=outputs)
model.compile(optimizer='adam', loss='mse')
model.fit(X, X, batch_size=64, nb_epoch=3)
return model, Model(input=inputs, output=h)
def multilayer_autoencoder(X, lam=1e-5):
X = X.reshape(X.shape[0], -1)
M, N = X.shape
inputs = Input(shape=(N,))
h = Dense(128, activation='relu')(inputs)
encoded = Dense(64, activation='relu', activity_regularizer=activity_l1(lam))(h)
h = Dense(128, activation='relu')(encoded)
outputs = Dense(N)(h)
model = Model(input=inputs, output=outputs)
model.compile(optimizer='adam', loss='mse')
model.fit(X, X, batch_size=64, nb_epoch=3)
return model, Model(input=inputs, output=h)
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, 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():
(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')
assert len(model.losses) == 1
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():
(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')
assert len(model.losses) == 1
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, 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, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0)
model.evaluate(X_test[test_ids, :], Y_test[test_ids, :], verbose=0)
