smart-iss-posts/iss/models/SimpleConvAutoEncoder.py

# -*- coding: utf-8 -*-

from iss.models import AbstractAutoEncoderModel
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Reshape, Flatten, BatchNormalization, Activation
from keras.optimizers import Adadelta, Adam
from keras.models import Model
import numpy as np

class SimpleConvAutoEncoder(AbstractAutoEncoderModel):

		def __init__(self, config):

			save_directory = config['save_directory']
			model_name = config['model_name']

			super().__init__(save_directory, model_name)

			self.activation = config['activation']
			self.input_shape = (config['input_height'], config['input_width'], config['input_channel'])
			self.lr = config['learning_rate']
			self.build_model()

		def build_model(self):
			input_shape = self.input_shape

			picture = Input(shape = input_shape)

			# encoded network
			x = Conv2D(64, (3, 3), padding = 'same', name = 'enc_conv_1')(picture)
			x = BatchNormalization()(x)
			x = Activation('relu')(x)
			x = MaxPooling2D((2, 2))(x)

			x = Conv2D(32, (3, 3), padding = 'same', name = 'enc_conv_2')(x)
			x = BatchNormalization()(x)
			x = Activation('relu')(x)
			x = MaxPooling2D((2, 2))(x)

			x = Conv2D(16, (3, 3), padding = 'same', name = 'enc_conv_3')(x)
			x = BatchNormalization()(x)
			x = Activation('relu')(x)
			encoded = MaxPooling2D((2, 2))(x)

			# decoded network
			x = Conv2D(16, (3, 3), padding = 'same', name = 'dec_conv_1')(encoded)
			x = BatchNormalization()(x)
			x = Activation('relu')(x)
			x = UpSampling2D((2, 2))(x)

			x = Conv2D(32, (3, 3), padding = 'same', name = 'dec_conv_2')(x)
			x = BatchNormalization()(x)
			x = Activation('relu')(x)
			x = UpSampling2D((2, 2))(x)

			x = Conv2D(64, (3, 3), padding = 'same', name = 'dec_conv_3')(x)
			x = BatchNormalization()(x)
			x = Activation('relu')(x)
			x = UpSampling2D((2, 2))(x)

			x = Conv2D(3, (3, 3), padding = 'same', name = 'dec_conv_4')(x)
			x = BatchNormalization()(x)
			x = Flatten()(x)
			x = Dense(np.prod(input_shape), activation = self.activation)(x)
			decoded = Reshape((input_shape))(x)

			self.model = Model(picture, decoded)
			
			# optimizer = Adadelta(lr = self.lr, rho = 0.95, epsilon = None, decay = 0.0)
			optimizer = Adam(lr = 0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
			
			self.model.compile(optimizer = optimizer, loss = 'binary_crossentropy')