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autoencoder.py
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import util
import keras
from keras.models import Model
from keras.layers import Layer, Flatten, LeakyReLU
from keras.layers import Input, Reshape, Dense, Lambda
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D
from keras import backend as K
from keras.engine.base_layer import InputSpec
from keras.optimizers import Adam, SGD, RMSprop
from keras.layers.normalization import BatchNormalization
from keras.losses import mse, binary_crossentropy
from keras import regularizers, activations, initializers, constraints
from keras.constraints import Constraint
from keras.callbacks import History
from keras.utils import plot_model
from keras.models import load_model
from keras.utils.generic_utils import get_custom_objects
def sampling(args):
epsilon_std = 1.0
z_mean, z_log_sigma = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
epsilon = K.random_normal(shape=(batch, dim),
mean=0., stddev=epsilon_std)
return z_mean + K.exp(z_log_sigma) * epsilon
class Autoencoder:
def __init__(self, x_sz, y_sz=1, z_dim= 64, variational = False, name=[]):
self.name = name
self.x_sz = x_sz
self.y_sz = y_sz
self.m2m = []
self.m2zm = []
self.zm2m = []
self.variational = variational
self.z_dim = z_dim
def encoder2D(self):
#define the simple autoencoder
input_image = Input(shape=(self.x_sz,))
#image encoder
_ = Dense(512)(input_image)
_ = LeakyReLU(alpha=0.3)(_)
_ = Dense(256)(_)
_ = LeakyReLU(alpha=0.3)(_)
_ = Dense(128)(_)
_ = LeakyReLU(alpha=0.3)(_)
_ = Dense(64)(_)
if not self.variational:
encoded_image = Dense(self.z_dim)(_)
else:
_ = Dense(self.z_dim)(_)
z_mean_m = Dense(self.z_dim)(_)
z_log_var_m = Dense(self.z_dim)(_)
encoded_image = Lambda(sampling)([z_mean_m, z_log_var_m])
return input_image, encoded_image, z_mean_m, z_log_var_m
return input_image, encoded_image
def decoder2D(self, encoded_image):
#image decoder
_ = Dense(64)(encoded_image)
_ = LeakyReLU(alpha=0.3)(_)
_ = Dense(128)(_)
_ = LeakyReLU(alpha=0.3)(_)
_ = Dense(256)(_)
_ = LeakyReLU(alpha=0.3)(_)
_ = Dense(512)(_)
_ = LeakyReLU(alpha=0.3)(_)
decoded_image = Dense(self.x_sz)(_)
return decoded_image
def train_autoencoder2D(self, x_train, load = False):
#set loss function, optimizer and compile
input_image, encoded_image = self.encoder2D()
decoded_image = self.decoder2D(encoded_image)
self.m2m = Model(input_image, decoded_image)
opt = keras.optimizers.Adam(lr=1e-3)
self.m2m.compile(optimizer=opt,
loss="mse",
metrics=['mse'])
#get summary of architecture parameters and plot arch. diagram
self.m2m.summary()
plot_model(self.m2m, to_file='AE_m2m.png')
#train the neural network
if not load:
plot_losses = util.PlotLosses()
self.m2m.fit(x_train, x_train,
epochs=100,
batch_size=32,
shuffle=True,
validation_split=0.3,
callbacks=[plot_losses])
#save trained model
self.m2m.save('AE_m2m.h5')
else:
#load an already trained model
print("Trained model loaded")
self.m2m = load_model('AE_m2m.h5')
#set the encoder model
input_image_f = Input(shape=(self.x_sz,))
_ = self.m2m.layers[1](input_image_f)
for i in range(2, 8):
_ = self.m2m.layers[i](_)
encoded_image_f = self.m2m.layers[8](_)
self.m2zm = Model(input_image_f, encoded_image_f)
#set the decoder model
zm_dec = Input(shape=(self.z_dim, ))
_ = self.m2m.layers[9](zm_dec)
for i in range(10, 17):
_ = self.m2m.layers[i](_)
decoded_image_ = self.m2m.layers[17](_)
self.zm2m = Model(zm_dec, decoded_image_)
def train_var_autoencoder2D(self, x_train, load = False):
#set loss function, optimizer and compile
input_image, encoded_image, z_mean, z_log_var = self.encoder2D()
decoded_image = self.decoder2D(encoded_image)
#define the variational loss and mse loss (equal weighting)
def vae_loss(input_image, decoded_image):
recons_loss = K.sum(mse(input_image, decoded_image))
kl_loss = (- 0.5) * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
return K.mean(recons_loss + 0.1*kl_loss)
#add custom loss
get_custom_objects().update({"vae_loss": vae_loss})
self.m2m = Model(input_image, decoded_image)
opt = keras.optimizers.Adam(lr=1e-6)
self.m2m.compile(optimizer=opt,
loss=vae_loss)
#get summary of architecture parameters and plot arch. diagram
self.m2m.summary()
plot_model(self.m2m, to_file='AE_m2m_var.png')
#train the neural network
if not load:
plot_losses = util.PlotLosses()
self.m2m.fit(x_train, x_train,
epochs=10,
batch_size=32,
shuffle=True,
validation_split=0.3,
callbacks=[plot_losses])
#save trained model
self.m2m.save('AE_m2m_var.h5')
else:
#load an already trained model
print("Trained model loaded")
self.m2m = load_model('AE_m2m_var.h5')
#set the encoder model
self.m2zm = Model(input_image, encoded_image)
#set the decoder model
zm_dec = Input(shape=(self.z_dim, ))
_ = self.m2m.layers[12](zm_dec)
for i in range(13, 20):
_ = self.m2m.layers[i](_)
decoded_image_ = self.m2m.layers[20](_)
self.zm2m = Model(zm_dec, decoded_image_)