/
sae.py
504 lines (356 loc) · 17.1 KB
/
sae.py
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from common import *
from library import *
# SAE
from keras import regularizers
def train_sae_210(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.9), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.8), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.9), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded
def train_sae_220(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.8), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.6), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.8), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded
def train_sae_230(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.7), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.4), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.7), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded
def train_sae_240(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.6), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.2), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.6), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded
def train_sae_h310(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.9), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.8), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.7), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.8), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.9), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded
def train_sae_h320(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.8), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.6), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.4), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.6), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.8), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded
def train_sae_h330(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.7), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.4), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.1), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.4), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.7), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded
def train_sae_h410(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.9), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.8), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.7), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.6), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.7), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.8), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.9), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded
def train_sae_420(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.8), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.6), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.4), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.2), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.4), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.6), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.8), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded
def train_sae_510(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.9), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.8), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.7), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.6), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.5), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.6), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.7), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.8), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.9), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded
def train_sae_610(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.9), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.8), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.7), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.6), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.5), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.4), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.5), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.6), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.7), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.8), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.9), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded
def train_sae_710(x_train, x_test):
input_dim = x_train.shape[1]
input_layer = Input(shape = (input_dim, ))
encoded = BatchNormalization()(input_layer)
encoded = Dense(int(input_dim*0.9), activation='relu',activity_regularizer=regularizers.l1(10e-5))(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.8), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.7), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.6), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.5), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded = Dense(int(input_dim*0.4), activation='relu')(encoded)
encoded = BatchNormalization()(encoded)
encoded_end = Dense(int(input_dim*0.3), activation='relu')(encoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.4), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.5), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.6), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.7), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.8), activation='relu')(decoded)
decoded = BatchNormalization()(encoded_end)
decoded = Dense(int(input_dim*0.9), activation='relu')(decoded)
decoded = BatchNormalization()(decoded)
output_layer = Dense(input_dim, activation='sigmoid')(decoded)
# 訓練
autoencoder = Model(input_layer, output_layer)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=16,
shuffle=True,
validation_data=(x_test, x_test))
encoder_model = Model(inputs=autoencoder.input, outputs=encoded_end)
x_train_encoded = encoder_model.predict(x_train)
x_test_encoded = encoder_model.predict(x_test)
return x_train_encoded,x_test_encoded