def test_arrhythmia_in_DeepSVDD():
input_shape = (274, )
inputs = Input(shape=input_shape, name='encoder_input')
x = Dense(125, activation='tanh')(inputs)
out = Dense(60, activation='tanh')(x)
Encoder = Model(inputs, out)
i = Input(shape=(60, ))
x = Dense(125, activation='tanh')(i)
y = Dense(274, name='decoder_out', activation=None)(x)
Decoder = Model(i, y)
tool = toolkit()
#set options
options = tool.setAlgorthem("deepsvdd")
options.datasetname = "arrhythmia"
options.class_num = 0
options.pre_train_epochs = 10
options.train_epochs = 50
options.encoder_model = Encoder
options.decoder_model = Decoder
options.inputs = inputs
options.x_train, options.train_label, options.x_test, options.test_label = gen_arrhythmia_train_valid_data(
)
scaler = StandardScaler()
options.x_train = scaler.fit_transform(options.x_train)
options.x_test = scaler.transform(options.x_test)
tool.train(options)
tool.test()
def test_SVDD():
i = Input(shape=(28, 28, 1))
x = Conv2D(16, (3, 3), activation='relu', padding='same')(i)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
Encoder = Model(i, x)
i = Input(shape=(4, 4, 8)) # 8 conv2d features
x = Conv2D(8, (3, 3), activation='relu', padding='same')(i)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
Decoder = Model(i, x)
tool = toolkit()
#set options
options = tool.setAlgorthem("deepsvdd")
options.datasetname = "mnist"
options.class_num = 0
options.pre_train_epochs = 10
options.train_epochs = 20
options.encoder_model = Encoder
options.decoder_model = Decoder
tool.train(options)
tool.test()
return
def test_KDD_on_Dagmm():
tool = toolkit()
#set options
options = tool.setAlgorthem("DAGMM")
options.datasetname = "kdd"
X_train, X_test, y_train, y_test = gen_KDD_train_valid_data()
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
minmaxscaler = sklearn.preprocessing.MinMaxScaler().fit(X_train)
X_train = minmaxscaler.transform(X_train)
minmaxscaler = sklearn.preprocessing.MinMaxScaler().fit(X_test)
X_test = minmaxscaler.transform(X_test)
options.X_test = X_test
options.X_train = X_train
options.y_train = y_train
options.y_test = y_test
options.comp_hidden = [60, 30, 10, 1, 10, 30, 60]
options.mid_layer = 3
options.epoch = 80
options.batch_size = 4096
tool.train(options)
tool.test()
def test_REPEN():
tool = toolkit()
#set options
options = tool.setAlgorthem("repen")
options.datasetname = "kdd"
options.num_runs = 3
tool.train(options)
tool.test()
return
def test_GAN():
tool = toolkit()
#set options
options = tool.setAlgorthem("GAN")
options.datasetname == "kdd"
options.class_num = 0
options.epochs = 1
options.generator_model= None
options.discriminator_model = None
tool.train(options)
tool.test()
return
def test_Thyroid_on_Dagmm_with_customed_net_structure():
tool = toolkit()
#set options
options = tool.setAlgorthem("DAGMM")
options.datasetname = "Thyroid"
X_train, X_test, y_train, y_test = gen_Thyroid_train_valid_data()
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
options.X_test = X_test
options.X_train = X_train
options.y_train = y_train
options.y_test = y_test
# set compression model
input_shape = (6,)
inputs = Input(shape=input_shape, name='encoder_input')
x = Dense(14, activation=None)(inputs)
x = Activation('tanh')(x)
mid_presentation = Dense(10, activation=None)(x)
x = Dense(14, activation=None)(mid_presentation)
x = Activation('tanh')(x)
y = Dense(6, name='compression_out_layer',activation=None)(x)
options.compress_net_inputs = inputs
options.compress_net_mid_presentation = mid_presentation
options.compress_net_outputs = y
# set estimated model
z = options.create_estimate_net_z_layer(inputs,mid_presentation,y)
layer = Dense(10, activation=None)(z)
layer = Activation('tanh')(layer)
layer = Dropout(0.5, noise_shape=None, seed=None)(layer)
gamma = Dense(2, activation='softmax', name='gamma')(layer)
options.z_layer = z
options.gamma_layer = gamma
options.epoch = 100
options.normal_portion = 97.5
options.batch_size = 1024
tool.train(options)
tool.test()
def test_arrhythmia_on_Dagmm():
tool = toolkit()
#set options
options = tool.setAlgorthem("DAGMM")
options.datasetname = "Thyroid"
X_train, X_test, y_train, y_test = gen_arrhythmia_train_valid_data()
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
options.X_test = X_test
options.X_train = X_train
options.y_train = y_train
options.y_test = y_test
options.comp_hidden = [10,2,10]
options.mid_layer = 1
options.mix_components = 2
options.epoch = 15000
options.batch_size = 500
options.normal_portion = 85
tool.train(options)
tool.test()
def test_B(n_class, epcho):
#change kernal size to 3*3
kernel_size = 3
latent_dim = 128 #output features number
inputs = Input(shape=(32, 32, 3), name='encoder_input')
x = inputs
x = Conv2D(filters=32,
kernel_size=kernel_size,
use_bias=False,
padding="same",
kernel_initializer="glorot_normal")(x)
x = BatchNormalization(epsilon=1e-04, scale=False)(x)
x = LeakyReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Conv2D(filters=64,
kernel_size=kernel_size,
use_bias=False,
padding="same",
kernel_initializer="glorot_normal")(x)
x = BatchNormalization(epsilon=1e-04, scale=False)(x)
x = LeakyReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Conv2D(filters=128,
kernel_size=kernel_size,
use_bias=False,
padding="same",
kernel_initializer="glorot_normal")(x)
x = BatchNormalization(epsilon=1e-04, scale=False)(x)
x = LeakyReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# Generate the latent vector
x = Flatten()(x)
latent = Dense(latent_dim, name='latent_vector', use_bias=False)(x)
#Instantiate Encoder Model
encoder = Model(inputs, latent, name='encoder')
latent_inputs = Input(shape=(128, ), name='decoder_input')
x = BatchNormalization(epsilon=1e-04, scale=False)(latent_inputs)
x = Reshape((4, 4, 8))(x)
x = LeakyReLU()(x)
x = Conv2DTranspose(filters=128,
kernel_size=kernel_size,
padding='same',
use_bias=False,
kernel_initializer="glorot_normal")(x)
x = BatchNormalization(epsilon=1e-04, scale=False)(x)
x = LeakyReLU()(x)
x = UpSampling2D(size=(2, 2), interpolation='nearest')(x)
x = Conv2DTranspose(filters=64,
kernel_size=kernel_size,
padding='same',
use_bias=False,
kernel_initializer="glorot_normal")(x)
x = BatchNormalization(epsilon=1e-04, scale=False)(x)
x = LeakyReLU()(x)
x = UpSampling2D(size=(2, 2), interpolation='nearest')(x)
x = Conv2DTranspose(filters=32,
kernel_size=kernel_size,
padding='same',
use_bias=False,
kernel_initializer="glorot_normal")(x)
x = BatchNormalization(epsilon=1e-04, scale=False)(x)
x = LeakyReLU()(x)
x = UpSampling2D(size=(2, 2), interpolation='nearest')(x)
x = Conv2DTranspose(filters=3,
kernel_size=kernel_size,
padding='same',
use_bias=False,
kernel_initializer="glorot_normal")(x)
outputs = Activation('sigmoid', name='decoder_output')(x)
# Instantiate Decoder Model
decoder = Model(latent_inputs, outputs, name='decoder')
tool = toolkit()
#set options
options = tool.setAlgorthem("deepsvdd")
options.datasetname = "cifar10"
options.class_num = n_class
options.pre_train_epochs = 10
options.train_epochs = epcho
options.encoder_model = encoder
options.decoder_model = decoder
tool.train(options)
tool.test()
print('change kernal size to 3*3')
return
def test_B_C_D_E(n_class, epcho):
#remove one Conv layer+ remove one Dense layer+ set filter =32
#set kernal 3*3
kernel_size = 3
inputs = Input(shape=(32, 32, 3), name='encoder_input')
x = inputs
x = Conv2D(filters=32,
kernel_size=kernel_size,
use_bias=False,
padding="same",
kernel_initializer="glorot_normal")(x)
x = BatchNormalization(epsilon=1e-04, scale=False)(x)
x = LeakyReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Conv2D(filters=32,
kernel_size=kernel_size,
use_bias=False,
padding="same",
kernel_initializer="glorot_normal")(x)
x = BatchNormalization(epsilon=1e-04, scale=False)(x)
x = LeakyReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
'''
x = Conv2D(filters=128,kernel_size=kernel_size,use_bias= False
,padding="same",kernel_initializer="glorot_normal")(x)
x = BatchNormalization( epsilon=1e-04, scale=False)(x)
x = LeakyReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
'''
# Generate the latent vector
latent = Flatten()(x)
#latent = Dense(latent_dim, name='latent_vector', use_bias=False)(x)
#Instantiate Encoder Model
encoder = Model(inputs, latent, name='encoder')
encoder.summary()
latent_inputs = Input(shape=(8 * 8 * 32, ), name='decoder_input')
x = BatchNormalization(epsilon=1e-04, scale=False)(latent_inputs)
x = Reshape((8, 8, 32))(latent_inputs)
x = LeakyReLU()(x)
x = Conv2DTranspose(filters=32,
kernel_size=kernel_size,
padding='same',
use_bias=False,
kernel_initializer="glorot_normal")(x)
x = BatchNormalization(epsilon=1e-04, scale=False)(x)
x = LeakyReLU()(x)
x = UpSampling2D(size=(2, 2), interpolation='nearest')(x)
x = Conv2DTranspose(filters=32,
kernel_size=kernel_size,
padding='same',
use_bias=False,
kernel_initializer="glorot_normal")(x)
x = BatchNormalization(epsilon=1e-04, scale=False)(x)
x = LeakyReLU()(x)
x = UpSampling2D(size=(2, 2), interpolation='nearest')(x)
x = Conv2DTranspose(filters=3,
kernel_size=kernel_size,
padding='same',
use_bias=False,
kernel_initializer="glorot_normal")(x)
outputs = Activation('sigmoid', name='decoder_output')(x)
# Instantiate Decoder Model
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
tool = toolkit()
#set options
options = tool.setAlgorthem("deepsvdd")
options.datasetname = "cifar10"
options.class_num = n_class
options.pre_train_epochs = 10
options.train_epochs = epcho
options.encoder_model = encoder
options.decoder_model = decoder
tool.train(options)
tool.test()
print(
'remove one Conv layer+ remove one Dense layer+set filters = 32+set kernal 3*3'
)
return