Initial commit

Tensorflow/concours_foetus/model.py

@@ -0,0 +1,92 @@
+import tensorflow as tf
+from tensorflow.keras import layers, models
+import config
+
+def block_resnet(input, filters, kernel_size, reduce, dropout=0.):
+    result=layers.Conv2D(filters, kernel_size, strides=1, padding='SAME', activation='relu')(input)
+    if dropout is not 0.:
+        result=layers.Dropout(dropout)(result)
+    if reduce is True:
+        result=layers.Conv2D(filters, kernel_size, strides=2, padding='SAME')(result)
+    else:
+        result=layers.Conv2D(filters, kernel_size, strides=1, padding='SAME')(result)
+        
+    if input.shape[-1]==filters:
+        if reduce is True:
+            shortcut=layers.Conv2D(filters, 1, strides=2, padding='SAME')(input)
+        else:
+            shortcut=input
+    else:
+        if reduce is True:
+            shortcut=layers.Conv2D(filters, 1, strides=2, padding='SAME')(input)
+        else:
+            shortcut=layers.Conv2D(filters, 1, strides=1, padding='SAME')(input)    
+    result=layers.add([result, shortcut])
+    if dropout is not 0.:
+        result=layers.Dropout(dropout)(result)
+    result=layers.Activation('relu')(result)
+    result=layers.BatchNormalization()(result)
+    return result
+
+def model(nbr):
+    entree=layers.Input(shape=(config.largeur, config.hauteur, 1), dtype='float32')
+
+    result=block_resnet(entree, 2*nbr, 3, False, 0.3)
+    result=block_resnet(result, 2*nbr, 3, False, 0.3)
+    result=block_resnet(result, 2*nbr, 3, False, 0.3)
+    result=block_resnet(result, 2*nbr, 3, True,  0.3)
+
+    result=block_resnet(result, 4*nbr, 3, False, 0.4)
+    result=block_resnet(result, 4*nbr, 3, False, 0.4)
+    result=block_resnet(result, 4*nbr, 3, False, 0.4)
+    result=block_resnet(result, 4*nbr, 3, False, 0.4)
+    result=block_resnet(result, 4*nbr, 3, False, 0.4)
+    result=block_resnet(result, 4*nbr, 3, False, 0.4)
+    result=block_resnet(result, 4*nbr, 3, True,  0.4)
+
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, False, 0.4)
+    result=block_resnet(result, 8*nbr, 3, True,  0.4)
+
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    result=block_resnet(result, 16*nbr, 3, False, 0.5)
+    
+    result=layers.AveragePooling2D()(result)    
+    result=layers.Flatten()(result)
+    sortie=layers.Dense(5, activation='sigmoid')(result)
+    
+    model=models.Model(inputs=entree, outputs=sortie)
+    return model
+