Update app.py

app.py

@@ -343,141 +343,6 @@ def decoder(inputs, input_tensor):
 
 
 
-
-def autoencoder(n_classes=2, height=size, width=size, channels=3):
-    inputs = Input((height, width, channels))
-    #Contraction path
-    conv_1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
-    conv_1 = BatchNormalization()(conv_1)
-    conv_1 = Dropout(0.2)(conv_1)  
-    conv_1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv_1)
-    conv_1 = BatchNormalization()(conv_1)
-    pool_1 = MaxPooling2D((2, 2))(conv_1)
-    
-    conv_2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool_1)
-    conv_2 = BatchNormalization()(conv_2)
-    conv_2 = Dropout(0.2)(conv_2)  
-    conv_2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_2)
-    conv_2 = BatchNormalization()(conv_2)
-    pool_2 = MaxPooling2D((2, 2))(conv_2)
-     
-    conv_3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool_2)
-    conv_3 = BatchNormalization()(conv_3)
-    conv_3 = Dropout(0.2)(conv_3)
-    conv_3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv_3)
-    conv_3 = BatchNormalization()(conv_3)
-    pool_3 = MaxPooling2D((2, 2))(conv_3)
-     
-    conv_4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool_3)
-    conv_4 = BatchNormalization()(conv_4)
-    conv_4 = Dropout(0.2)(conv_4)
-    conv_4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv_4)
-    conv_4 = BatchNormalization()(conv_4)
-    pool_4 = MaxPooling2D(pool_size=(2, 2))(conv_4)
-     
-         
-    #conv_5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool_4)    
-    #conv_5 = BatchNormalization()(conv_5)
-    conv_5 = Dropout(0.1)(pool_4)    
-    
-    #Expansive path
-    
-    u6 = UpSampling2D((2, 2))(conv_5)
-    #u6 = concatenate([att_5, u6])
-    conv_6 = Conv2D(256, (3, 3), activation='relu', padding='same')(u6)
-    conv_6 = BatchNormalization()(conv_6)
-    conv_6 = Dropout(0.2)(conv_6)  
-    #conv_6 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv_6)
-    #conv_6 = Dropout(0.2)(conv_6)  
-    #conv_6 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv_6)
-    #conv_6 = BatchNormalization()(conv_6)
-    #conv_6 = Dropout(0.2)(conv_6)    
-    conv_6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv_6)
-    conv_6 = BatchNormalization()(conv_6)
-    
-    """
-    u66 = UpSampling2D((2, 2))(conv_6)    
-    conv_66 = Conv2D(128, (3, 3), activation='relu', padding='same')(u66)
-    conv_66 = BatchNormalization()(conv_66)
-    conv_66 = Conv2D(128, (3, 3), activation='relu', padding='same')(u66)
-    conv_66 = Conv2D(128, (3, 3), activation='relu', padding='same')(u66)
-    conv_66 = BatchNormalization()(conv_66)
-    conv_66 = Dropout(0.2)(conv_66)
-    conv_66 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv_66)
-    """
-     
-    u7 = UpSampling2D((2, 2))(conv_6)    
-    conv_7 = Conv2D(128, (3, 3), activation='relu', padding='same')(u7)
-    conv_7 = BatchNormalization()(conv_7)
-    conv_7 = Dropout(0.2)(conv_7)
-    #conv_7 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv_7)
-    #conv_7 = Dropout(0.1)(conv_7)
-    #conv_7 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv_7)
-    #conv_7 = BatchNormalization()(conv_7)
-    #conv_7 = Dropout(0.1)(conv_7)
-    conv_7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv_7)
-    conv_7 = BatchNormalization()(conv_7)
-     
-    u8 = UpSampling2D((2, 2))(conv_7)    
-    conv_8 = Conv2D(64, (3, 3), activation='relu', padding='same')(u8)
-    conv_8 = BatchNormalization()(conv_8)
-    conv_8 = Dropout(0.2)(conv_8) 
-    #conv_8 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv_8)
-    #conv_8 = Dropout(0.2)(conv_8) 
-    #conv_8 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv_8)
-    #conv_8 = BatchNormalization()(conv_8)
-    #conv_8 = Dropout(0.2)(conv_8)  
-    conv_8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_8)
-    conv_8 = BatchNormalization()(conv_8)
-     
-    u9 = UpSampling2D((2, 2))(conv_8)    
-    conv_9 = Conv2D(32, (3, 3), activation='relu', padding='same')(u9)
-    conv_9 = BatchNormalization()(conv_9)
-    conv_9 = Dropout(0.2)(conv_9)
-    #conv_9 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_9)
-    #conv_9 = Dropout(0.1)(conv_9)
-    #conv_9 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv_9)
-    #conv_9 = BatchNormalization()(conv_9)
-    #conv_9 = Dropout(0.1)(conv_9)
-    conv_9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv_9)
-    conv_9 = BatchNormalization()(conv_9)
-     
-    outputs = Conv2D(n_classes, (1, 1), activation='softmax')(conv_9)
-    
-    model = Model(inputs=[inputs], outputs=[outputs])
-    return model
-    
-"""
-    gating_16 = gating_signal(stage_5, 8*FILTER_NUM, True)
-    att_16 = attention_block(stage_4, stage_5, 8*FILTER_NUM)
-    up_stage_1 = upsample(stage_5,stage_4)
-    up_16 = layers.concatenate([up_stage_1, att_16], axis=axis)
-    
-    
-    gating_32 = gating_signal(up_repeat_elem1, 4*FILTER_NUM, True)
-    att_32 = attention_block(stage_3, gating_32, 4*FILTER_NUM)
-    up_stage_2 = upsample(up_repeat_elem1,stage_3)
-    up_32 = layers.concatenate([up_stage_2, att_32], axis=axis)
-    
-    
-    gating_64 = gating_signal(up_repeat_elem2, 2*FILTER_NUM, True)
-    att_64 = attention_block(stage_2, gating_64, 2*FILTER_NUM)
-    up_stage_3 = upsample(up_repeat_elem2,stage_2)
-    up_64 = layers.concatenate([up_stage_3, att_64], axis=axis)
-    
-    
-    gating_128 = gating_signal(up_repeat_elem3, FILTER_NUM, True)
-    att_128 = attention_block(stage_1, gating_128, FILTER_NUM)
-    up_stage_4 = upsample(up_repeat_elem3,stage_1)
-    up_128 = layers.concatenate([up_stage_4, att_128], axis=axis)
-    
-    
-    gating_256 = gating_signal(up_repeat_elem4, FILTER_NUM, True)
-    att_256 = attention_block(conv_1, gating_256, FILTER_NUM)
-    up_stage_5 = upsample(up_repeat_elem4,conv_1)
-    up_256 = layers.concatenate([up_stage_5, att_256], axis=axis)
-"""
-
 def unet_2( n_classes=2, height=size, width=size, channels=3, metrics = ['accuracy']):
     inputs = Input((height, width, channels))