def __init__(self,
latent_dim=100,
sparsity_weight=1,
sparsity_objective=0.1):
"""
Create a sparse shallow AE with the custom kl divergence regularizer.
Arguments:
sparsity_weight: positive float - the weight of the sparsity cost.
sparsity_objective: float between 0 and 1 - the sparsity parameter
"""
self.latent_dim = latent_dim
self.sparsity_weight = sparsity_weight
self.sparsity_objective = sparsity_objective
input_img = Input(shape=(
28, 28,
1)) # adapt this if using `channels_first` image data format
x = Flatten()(input_img)
encoded = Dense(
latent_dim,
activation='sigmoid',
activity_regularizer=custom_regularizers.KL_divergence_sum(
beta=self.sparsity_weight, rho=self.sparsity_objective))(x)
self.encoder = Model(input_img, encoded, name='encoder')
encoded_img = Input(shape=(self.latent_dim, ))
x = Dense(28 * 28)(encoded_img)
x = LeakyReLU(alpha=0.1)(x)
decoded = Reshape((28, 28, 1))(x)
self.decoder = Model(encoded_img, decoded, name='decoder')
encoded = self.encoder(input_img)
decoded = self.decoder(encoded)
self.autoencoder = Model(input_img, decoded)
self.autoencoder.compile(optimizer='adadelta',
loss='mean_squared_error')
def __init__(self, latent_dim=100, nb_rows=28, nb_columns=28, nb_input_channels=1, one_channel_output=True,
sparsity_weight=0.1, sparsity_objective=0.1):
"""
Create a sparse shallow AE with the custom kl divergence regularizer, enforcing weights non negativity with Keras NonNeg constraint.
Arguments:
sparsity_weight: positive float - the weight of the sparsity cost.
sparsity_objective: float between 0 and 1 - the sparsity parameter.
"""
self.latent_dim = latent_dim
self.nb_rows=nb_rows
self.nb_columns=nb_columns
self.nb_input_channels=nb_input_channels
if one_channel_output:
self.nb_output_channels=1
else:
self.nb_output_channels=nb_input_channels
self.sparsity_weight = sparsity_weight
self.sparsity_objective = sparsity_objective
input_img = Input(shape=(self.nb_rows, self.nb_columns, nb_input_channels)) # adapt this if using `channels_first` image data format
x = Flatten()(input_img)
encoded = Dense(latent_dim, activation='sigmoid',
activity_regularizer=custom_regularizers.KL_divergence_sum(beta=self.sparsity_weight,
rho=self.sparsity_objective))(x)
self.encoder = Model(input_img, encoded, name='encoder')
encoded_img = Input(shape=(self.latent_dim,))
x = Dense(self.nb_rows*self.nb_columns*self.nb_output_channels,
kernel_constraint=constraints.non_neg())(encoded_img)
x = LeakyReLU(alpha=0.1)(x)
decoded = Reshape((self.nb_rows,self.nb_columns,self.nb_output_channels))(x)
self.decoder = Model(encoded_img, decoded, name='decoder')
encoded = self.encoder(input_img)
decoded = self.decoder(encoded)
self.autoencoder = Model(input_img, decoded)
self.autoencoder.compile(optimizer='adadelta', loss='mean_squared_error', metrics=['mse'])
def __init__(self, latent_dim=100, nb_rows=28, nb_columns=28, nb_input_channels=1, one_channel_output=True,
sparsity_weight=0.1, sparsity_objective=0.1, dropout_rate=None):
"""
Create and initialize an autoencoder.
"""
self.latent_dim = latent_dim
self.nb_input_channels=nb_input_channels
self.nb_rows=nb_rows
self.nb_columns=nb_columns
self.sparsity_weight = sparsity_weight
self.sparsity_objective = sparsity_objective
if one_channel_output:
self.nb_output_channels=1
else:
self.nb_output_channels=nb_input_channels
input_img = Input(shape=(self.nb_rows, self.nb_columns, nb_input_channels)) # adapt this if using `channels_first` image data format
x = Flatten()(input_img)
encoded = Dense(latent_dim, activation='sigmoid',
activity_regularizer=custom_regularizers.KL_divergence_sum(beta=self.sparsity_weight,
rho=self.sparsity_objective))(x)
self.encoder = Model(input_img, encoded, name='encoder')
encoded_img = Input(shape=(self.latent_dim,))
if dropout_rate is None:
x = MaxPlusDense(self.nb_rows*self.nb_columns*self.nb_output_channels, use_bias=False,
kernel_constraint=custom_constraints.Between_0_and_1())(encoded_img)
else:
x = Dropout(dropout_rate)(encoded_img)
x = MaxPlusDense(self.nb_rows*self.nb_columns*self.nb_output_channels, use_bias=False,
kernel_constraint=custom_constraints.Between_0_and_1())(x)
decoded = Reshape((self.nb_rows,self.nb_columns,self.nb_output_channels))(x)
self.decoder = Model(encoded_img, decoded, name='decoder')
encoded = self.encoder(input_img)
decoded = self.decoder(encoded)
self.autoencoder = Model(input_img, decoded)
self.autoencoder.compile(optimizer='adadelta', loss='mean_squared_error', metrics=['mse'])
