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AE.py
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AE.py
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from keras.layers import Input, Conv2D, Flatten, Dense, Conv2DTranspose, Reshape, Lambda, Activation, BatchNormalization, LeakyReLU, Dropout
from keras.models import Model
from keras import backend as K
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from keras.utils import plot_model
from utils.callbacks import CustomCallback, step_decay_schedule
import numpy as np
import json
import os
import pickle
class Autoencoder():
def __init__(self
, input_dim
, encoder_conv_filters
, encoder_conv_kernel_size
, encoder_conv_strides
, decoder_conv_t_filters
, decoder_conv_t_kernel_size
, decoder_conv_t_strides
, z_dim
, use_batch_norm = False
, use_dropout = False
):
self.name = 'autoencoder'
self.input_dim = input_dim
self.encoder_conv_filters = encoder_conv_filters
self.encoder_conv_kernel_size = encoder_conv_kernel_size
self.encoder_conv_strides = encoder_conv_strides
self.decoder_conv_t_filters = decoder_conv_t_filters
self.decoder_conv_t_kernel_size = decoder_conv_t_kernel_size
self.decoder_conv_t_strides = decoder_conv_t_strides
self.z_dim = z_dim
self.use_batch_norm = use_batch_norm
self.use_dropout = use_dropout
self.n_layers_encoder = len(encoder_conv_filters)
self.n_layers_decoder = len(decoder_conv_t_filters)
self._build()
def _build(self):
### THE ENCODER
encoder_input = Input(shape=self.input_dim, name='encoder_input')
x = encoder_input
for i in range(self.n_layers_encoder):
conv_layer = Conv2D(
filters = self.encoder_conv_filters[i]
, kernel_size = self.encoder_conv_kernel_size[i]
, strides = self.encoder_conv_strides[i]
, padding = 'same'
, name = 'encoder_conv_' + str(i)
)
x = conv_layer(x)
x = LeakyReLU()(x)
if self.use_batch_norm:
x = BatchNormalization()(x)
if self.use_dropout:
x = Dropout(rate = 0.25)(x)
shape_before_flattening = K.int_shape(x)[1:]
x = Flatten()(x)
encoder_output= Dense(self.z_dim, name='encoder_output')(x)
self.encoder = Model(encoder_input, encoder_output)
### THE DECODER
decoder_input = Input(shape=(self.z_dim,), name='decoder_input')
x = Dense(np.prod(shape_before_flattening))(decoder_input)
x = Reshape(shape_before_flattening)(x)
for i in range(self.n_layers_decoder):
conv_t_layer = Conv2DTranspose(
filters = self.decoder_conv_t_filters[i]
, kernel_size = self.decoder_conv_t_kernel_size[i]
, strides = self.decoder_conv_t_strides[i]
, padding = 'same'
, name = 'decoder_conv_t_' + str(i)
)
x = conv_t_layer(x)
if i < self.n_layers_decoder - 1:
x = LeakyReLU()(x)
if self.use_batch_norm:
x = BatchNormalization()(x)
if self.use_dropout:
x = Dropout(rate = 0.25)(x)
else:
x = Activation('sigmoid')(x)
decoder_output = x
self.decoder = Model(decoder_input, decoder_output)
### THE FULL AUTOENCODER
model_input = encoder_input
model_output = self.decoder(encoder_output)
self.model = Model(model_input, model_output)
def compile(self, learning_rate):
self.learning_rate = learning_rate
optimizer = Adam(lr=learning_rate)
def r_loss(y_true, y_pred):
return K.mean(K.square(y_true - y_pred), axis = [1,2,3])
self.model.compile(optimizer=optimizer, loss = r_loss)
def save(self, folder):
if not os.path.exists(folder):
os.makedirs(folder)
os.makedirs(os.path.join(folder, 'viz'))
os.makedirs(os.path.join(folder, 'weights'))
os.makedirs(os.path.join(folder, 'images'))
with open(os.path.join(folder, 'params.pkl'), 'wb') as f:
pickle.dump([
self.input_dim
, self.encoder_conv_filters
, self.encoder_conv_kernel_size
, self.encoder_conv_strides
, self.decoder_conv_t_filters
, self.decoder_conv_t_kernel_size
, self.decoder_conv_t_strides
, self.z_dim
, self.use_batch_norm
, self.use_dropout
], f)
self.plot_model(folder)
def load_weights(self, filepath):
self.model.load_weights(filepath)
def train(self, x_train, batch_size, epochs, run_folder, print_every_n_batches = 100, initial_epoch = 0, lr_decay = 1):
custom_callback = CustomCallback(run_folder, print_every_n_batches, initial_epoch, self)
lr_sched = step_decay_schedule(initial_lr=self.learning_rate, decay_factor=lr_decay, step_size=1)
checkpoint2 = ModelCheckpoint(os.path.join(run_folder, 'weights/weights.h5'), save_weights_only = True, verbose=1)
callbacks_list = [checkpoint2, custom_callback, lr_sched]
self.model.fit(
x_train
, x_train
, batch_size = batch_size
, shuffle = True
, epochs = epochs
, initial_epoch = initial_epoch
, callbacks = callbacks_list
)
def plot_model(self, run_folder):
plot_model(self.model, to_file=os.path.join(run_folder ,'viz/model.png'), show_shapes = True, show_layer_names = True)
plot_model(self.encoder, to_file=os.path.join(run_folder ,'viz/encoder.png'), show_shapes = True, show_layer_names = True)
plot_model(self.decoder, to_file=os.path.join(run_folder ,'viz/decoder.png'), show_shapes = True, show_layer_names = True)