mirror of
https://github.com/prise6/smart-iss-posts
synced 2024-05-03 06:03:10 +02:00
72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
# -*- 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')
|