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model.py
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from keras.layers import Input, MaxPooling1D, UpSampling1D, Convolution1D, Cropping1D
from keras.models import Model
from keras.optimizers import Adam
from keras import regularizers
class DeepAutoEncoder(object):
def __init__(self, data):
input_img = Input(shape=(data.shape[1], data.shape[2])) # 0
conv1 = Convolution1D(16, 7, activation='relu', border_mode='same', init='glorot_normal',
W_regularizer=regularizers.l2(0.0005))(input_img) # 1
pool1 = MaxPooling1D(2, border_mode='same')(conv1) # 2
conv2 = Convolution1D(32, 5, activation='relu', border_mode='same', init='glorot_normal',
W_regularizer=regularizers.l2(0.0005))(pool1) # 3
pool2 = MaxPooling1D(2, border_mode='same')(conv2) # 4
conv3 = Convolution1D(64, 3, activation='relu', border_mode='same', init='glorot_normal',
W_regularizer=regularizers.l2(0.0005))(pool2) # 5
pool3 = MaxPooling1D(2, border_mode='same')(conv3) # 6
conv4 = Convolution1D(128, 3, activation='relu', border_mode='same', init='glorot_normal',
W_regularizer=regularizers.l2(0.0005))(pool3) # 7
pool4 = MaxPooling1D(2, border_mode='same')(conv4) # 8
encoded = pool4
unpool4 = UpSampling1D(2)(pool4) # 9
deconv3 = Convolution1D(64, 3, activation='relu', border_mode='same', init='glorot_normal',
W_regularizer=regularizers.l2(0.0005))(unpool4) # 10
unpool3 = UpSampling1D(2)(deconv3) # 11
deconv2 = Convolution1D(32, 3, activation='relu', border_mode='same', init='glorot_normal',
W_regularizer=regularizers.l2(0.0005))(unpool3) # 12
unpool2 = UpSampling1D(2)(deconv2) # 13
deconv1 = Convolution1D(16, 5, activation='relu', border_mode='same', init='glorot_normal',
W_regularizer=regularizers.l2(0.0005))(unpool2) # 14
unpool1 = UpSampling1D(2)(deconv1) # 15
crop = Cropping1D(cropping=(0, 7))(unpool1)
decoded = Convolution1D(1, 7, activation='sigmoid', border_mode='same', init='glorot_normal',
W_regularizer=regularizers.l2(0.0005))(crop) # 16
self.encoder = Model(input=input_img, output=encoded)
self.autoencoder = Model(input=input_img, output=decoded)
def compile(self, optimizer='adam', loss='binary_crossentropy'):
adam = Adam(lr=0.001, decay=0.005)
self.autoencoder.compile(optimizer=adam, loss=loss)
print(self.autoencoder.summary())
def train(self, x_train=None, x_test=None, nb_epoch=1, batch_size=128, shuffle=True):
self.autoencoder.fit(x_train, x_train,
nb_epoch=nb_epoch,
batch_size=batch_size,
shuffle=shuffle,
validation_data=(x_test, x_test))
self.encoder.save('./save_data/full_model_encoder.h5')
self.autoencoder.save('./save_data/full_model_autoencoder.h5')
def load_weights(self, ae01, ae02, ae03, ae04):
self.autoencoder.layers[1].set_weights(ae01.layers[1].get_weights())
self.autoencoder.layers[3].set_weights(ae02.layers[1].get_weights())
self.autoencoder.layers[5].set_weights(ae03.layers[1].get_weights())
self.autoencoder.layers[7].set_weights(ae04.layers[1].get_weights())
self.autoencoder.layers[10].set_weights(ae04.layers[4].get_weights())
self.autoencoder.layers[12].set_weights(ae03.layers[4].get_weights())
self.autoencoder.layers[14].set_weights(ae02.layers[4].get_weights())
self.autoencoder.layers[16].set_weights(ae01.layers[4].get_weights())
class AutoEncoderStack01(object):
def __init__(self, data):
input_img = Input(shape=(data.shape[1], data.shape[2])) # 0
conv1 = Convolution1D(16, 7, activation='relu',
border_mode='same')(input_img) # 1
pool1 = MaxPooling1D(2, border_mode='same')(conv1) # 2
encoded = pool1
unpool1 = UpSampling1D(2)(pool1) # 3
decoded = Convolution1D(
1, 7, activation='sigmoid', border_mode='same')(unpool1) # 4
crop = Cropping1D(cropping=(0, 1))(decoded)
self.encoder = Model(input=input_img, output=encoded)
self.autoencoder = Model(input=input_img, output=crop)
def compile(self, optimizer='adam', loss='binary_crossentropy'):
adam = Adam(lr=0.001, decay=0.005)
self.autoencoder.compile(optimizer=adam, loss=loss)
print(self.autoencoder.summary())
def train(self, x_train=None, x_test=None, nb_epoch=1, batch_size=128, shuffle=True):
self.autoencoder.fit(x_train, x_train,
nb_epoch=nb_epoch,
batch_size=batch_size,
shuffle=shuffle,
validation_data=(x_test, x_test))
self.encoder.save('./save_data/stack01_encoder.h5')
self.autoencoder.save('./save_data/stack01_autoencoder.h5')
class AutoEncoderStack02(object):
def __init__(self, data):
input_img = Input(shape=(data.shape[1], data.shape[2])) # 0
conv2 = Convolution1D(32, 5, activation='relu',
border_mode='same')(input_img) # 1
pool2 = MaxPooling1D(2, border_mode='same')(conv2) # 2
encoded = pool2
unpool2 = UpSampling1D(2)(pool2) # 3
decoded = Convolution1D(
16, 5, activation='linear', border_mode='same')(unpool2) # 4
self.encoder = Model(input=input_img, output=encoded)
self.autoencoder = Model(input=input_img, output=decoded)
def compile(self, optimizer='adam', loss='mean_squared_error'):
adam = Adam(lr=0.0005, decay=0.005)
self.autoencoder.compile(optimizer=adam, loss=loss)
print(self.autoencoder.summary())
def train(self, x_train=None, x_test=None, nb_epoch=1, batch_size=128, shuffle=True):
self.autoencoder.fit(x_train, x_train,
nb_epoch=nb_epoch,
batch_size=batch_size,
shuffle=shuffle,
validation_data=(x_test, x_test))
self.encoder.save('./save_data/stack02_encoder.h5')
self.autoencoder.save('./save_data/stack02_autoencoder.h5')
class AutoEncoderStack03(object):
def __init__(self, data):
input_img = Input(shape=(data.shape[1], data.shape[2])) # 0
conv3 = Convolution1D(64, 3, activation='relu',
border_mode='same')(input_img) # 1
pool3 = MaxPooling1D(2, border_mode='same')(conv3) # 2
encoded = pool3
unpool3 = UpSampling1D(2)(pool3) # 4
decoded = Convolution1D(
32, 3, activation='linear', border_mode='same')(unpool3) # 13
self.encoder = Model(input=input_img, output=encoded)
self.autoencoder = Model(input=input_img, output=decoded)
def compile(self, optimizer='adam', loss='mean_squared_error'):
adam = Adam(lr=0.0005, decay=0.005)
self.autoencoder.compile(optimizer=adam, loss=loss)
print(self.autoencoder.summary())
def train(self, x_train=None, x_test=None, nb_epoch=1, batch_size=128, shuffle=True):
self.autoencoder.fit(x_train, x_train,
nb_epoch=nb_epoch,
batch_size=batch_size,
shuffle=shuffle,
validation_data=(x_test, x_test))
self.encoder.save('./save_data/stack03_encoder.h5')
self.autoencoder.save('./save_data/stack03_autoencoder.h5')
class AutoEncoderStack04(object):
def __init__(self, data):
input_img = Input(shape=(data.shape[1], data.shape[2])) # 0
conv4 = Convolution1D(128, 3, activation='relu',
border_mode='same')(input_img) # 1
pool4 = MaxPooling1D(2, border_mode='same')(conv4) # 2
encoded = pool4
unpool4 = UpSampling1D(2)(pool4) # 3
decoded = Convolution1D(
64, 3, activation='linear', border_mode='same')(unpool4) # 4
crop = Cropping1D(cropping=(0, 1))(decoded)
self.encoder = Model(input=input_img, output=encoded)
self.autoencoder = Model(input=input_img, output=crop)
print(self.autoencoder.summary())
def compile(self, optimizer='adam', loss='mean_squared_error'):
adam = Adam(lr=0.0005, decay=0.005)
self.autoencoder.compile(optimizer=adam, loss=loss)
def train(self, x_train=None, x_test=None, nb_epoch=1, batch_size=128, shuffle=True):
self.autoencoder.fit(x_train, x_train,
nb_epoch=nb_epoch,
batch_size=batch_size,
shuffle=shuffle,
validation_data=(x_test, x_test))
self.encoder.save('./save_data/stack04_encoder.h5')
self.autoencoder.save('./save_data/stack04_autoencoder.h5')