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Code Issues Releases Wiki Activity

re write abstract class

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Francois Vieille 2019-03-13 22:55:32 +01:00
parent 0efb5bf975
commit 9c76c82920
2 changed files with 14 additions and 4 deletions
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3
iss/models/AbstractModel.py
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@ -32,4 +32,5 @@ class AbstractAutoEncoderModel(AbstractModel):
def __init__(self, save_directory, model_name):
super().__init__(save_directory, model_name)
self.encoded_layer = None
self.encoder_model = None
self.decoder_model = None

15
iss/models/SimpleConvAutoEncoder.py
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@ -4,6 +4,7 @@ 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
from keras import backend as K
import numpy as np
class SimpleConvAutoEncoder(AbstractAutoEncoderModel):
@ -17,11 +18,13 @@ class SimpleConvAutoEncoder(AbstractAutoEncoderModel):
self.activation = config['activation']
self.input_shape = (config['input_height'], config['input_width'], config['input_channel'])
self.latent_shape = (config['latent_height'], config['latent_width'], config['latent_channel'])
self.lr = config['learning_rate']
self.build_model()
def build_model(self):
input_shape = self.input_shape
latent_shape = self.latent_shape
picture = Input(shape = input_shape)
@ -41,8 +44,12 @@ class SimpleConvAutoEncoder(AbstractAutoEncoderModel):
x = Activation('relu')(x)
encoded = MaxPooling2D((2, 2))(x)
self.encoder_model = Model(picture, encoded, name = "encoder")
# decoded network
x = Conv2D(16, (3, 3), padding = 'same', name = 'dec_conv_1')(encoded)
latent_input = Input(shape = latent_shape)
x = Conv2D(16, (3, 3), padding = 'same', name = 'dec_conv_1')(latent_input)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = UpSampling2D((2, 2))(x)
@ -63,9 +70,11 @@ class SimpleConvAutoEncoder(AbstractAutoEncoderModel):
x = Dense(np.prod(input_shape), activation = self.activation)(x)
decoded = Reshape((input_shape))(x)
self.model = Model(picture, decoded)
self.decoder_model = Model(latent_input, decoded, name = "decoder")
picture_dec = self.decoder_model(self.encoder_model(picture))
self.model = Model(picture, picture_dec)
# 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')