nouveau modele de convolution

iss/models/AbstractModel.py

@@ -26,3 +26,10 @@ class AbstractModel:
 	def predict_one(self, x, batch_size = 1, verbose = 0, steps = None):
 		x = np.expand_dims(x, axis = 0)
 		return self.predict(x, batch_size, verbose, steps)
+
+
+class AbstractAutoEncoderModel(AbstractModel):
+
+	def __init__(self, save_directory, model_name):
+		super().__init__(save_directory, model_name)
+		self.encoded_layer = None

iss/models/DataLoader.py

@@ -4,9 +4,10 @@ from keras.preprocessing.image import ImageDataGenerator
 
 class ImageDataGeneratorWrapper:
 
-	def __init__(self, config):
+	def __init__(self, config, model):
 		
 		self.config = config
+		self.model = model
 		self.datagen = None
 		self.train_generator = None
 		self.test_generator = None
@@ -26,11 +27,11 @@ class ImageDataGeneratorWrapper:
 		# voir plus tars si besoin de parametrer
 		return self.datagen.flow_from_directory(
 			directory,
-			target_size = (self.config.get('models')['simple']['input_height'], self.config.get('models')['simple']['input_width']),
+			target_size = (self.config.get('models')[self.model]['input_height'], self.config.get('models')[self.model]['input_width']),
 			color_mode = 'rgb',
 			classes = None,
 			class_mode = 'input',
-			batch_size = self.config.get('models')['simple']['batch_size'], 
+			batch_size = self.config.get('models')[self.model]['batch_size'], 
 		)
 
 	def set_train_generator(self):

iss/models/SimpleAutoEncoder.py

@@ -1,12 +1,12 @@
 # -*- coding: utf-8 -*-
 
-from iss.models.AbstractModel import AbstractModel
+from iss.models import AbstractAutoEncoderModel
 from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Reshape, Flatten
 from keras.optimizers import Adadelta, Adam
 from keras.models import Model
 import numpy as np
 
-class SimpleAutoEncoder(AbstractModel):
+class SimpleAutoEncoder(AbstractAutoEncoderModel):
 
 		def __init__(self, config):
 

iss/models/SimpleConvAutoEncoder.py

@@ -1,12 +1,12 @@
 # -*- coding: utf-8 -*-
 
-from iss.models.AbstractModel import AbstractModel
-from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Reshape, Flatten
+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(AbstractModel):
+class SimpleConvAutoEncoder(AbstractAutoEncoderModel):
 
 		def __init__(self, config):
 
@@ -26,16 +26,39 @@ class SimpleConvAutoEncoder(AbstractModel):
 			picture = Input(shape = input_shape)
 
 			# encoded network
-			x = Conv2D(4, (3, 3), activation = 'relu', padding = 'same', name = 'enc_conv_1')(picture)
+			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(8, (3, 3), activation = 'relu', padding = 'same', name = 'enc_conv_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(8, (3, 3), activation = 'relu', padding = 'same', name = 'dec_conv_1')(encoded)
+			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(4, (3, 3), activation = 'relu', padding = 'same', name = 'dec_conv_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)

iss/models/__init__.py

@@ -1,3 +1,5 @@
 
+from .AbstractModel import AbstractModel
+from .AbstractModel import AbstractAutoEncoderModel
 from .SimpleConvAutoEncoder import SimpleConvAutoEncoder
-from .SimpleAutoEncoder import SimpleAutoEncoder
+from .SimpleAutoEncoder import SimpleAutoEncoder