From 70fe2963600a535d3b5170add27e4551ccef6e1b Mon Sep 17 00:00:00 2001
From: Kelvin Davis <273degreeskelvin@gmail.com>
Date: Wed, 23 May 2018 23:16:51 +1000
Subject: [PATCH] replaced np with K

---
 mini_proj/waldo_model.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/mini_proj/waldo_model.py b/mini_proj/waldo_model.py
index 4241d80..929e1e9 100644
--- a/mini_proj/waldo_model.py
+++ b/mini_proj/waldo_model.py
@@ -48,21 +48,21 @@ def FCN():
     ## Define the model structure
     model = Model(inputs=inputs, outputs=classif)
     # Optimizer recommended Adadelta values (lr=0.01)
-    model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])  
+    model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])
 
     return model
 
 def precision(y_true, y_pred):
-    y_pred = np.round(y_pred)
-    num = np.sum(np.logical_and(y_true, y_pred))
-    den = np.sum(y_pred)
-    return np.divide(num, den)
+    y_pred = K.round(y_pred)
+    num = K.sum(K.logical_and(y_true, y_pred))
+    den = K.sum(y_pred)
+    return K.divide(num, den)
 
 def recall(y_true, y_pred):
-    y_pred = np.round(y_pred)
-    num = np.sum(np.logical_and(y_true, y_pred))
-    den = np.sum(y_true)
-    return np.divide(num, den)
+    y_pred = K.round(y_pred)
+    num = K.sum(K.logical_and(y_true, y_pred))
+    den = K.sum(y_true)
+    return K.divide(num, den)
 
 def f_measure(y_true, y_pred):
     p = precision(y_true, y_pred)