Merge branch 'master' of https://github.com/friggog/Affectone

.gitignore

@@ -1,2 +1,2 @@
 __pycache__
-
+.DS_Store

net.py

@@ -16,7 +16,7 @@ CLASSIFY = 0
 REGRESS = 1
 
 # OTPIONS #
-BATCH_SIZE = 256 # TODO whatever GPU can handle
+BATCH_SIZE = 400
 EPOCHS = 24
 
 def load_images(C_or_R, paths, labels, batch_size=32, eval=False):
@@ -130,22 +130,22 @@ def base_model(C_or_R):
     model.add(Activation('relu'))
     model.add(MaxPooling2D(pool_size=(2, 2)))
     # CONV BLOCK 4
-    # model.add(Conv2D(256, (3, 3)))
-    # model.add(BatchNormalization(axis=-1))
-    # model.add(Activation('relu'))
-    # model.add(Conv2D(256, (3, 3)))
-    # model.add(BatchNormalization(axis=-1))
-    # model.add(Activation('relu'))
-    # model.add(MaxPooling2D(pool_size=(2,2)))
+    model.add(Conv2D(256, (3, 3)))
+    model.add(BatchNormalization(axis=-1))
+    model.add(Activation('relu'))
+    model.add(Conv2D(256, (3, 3)))
+    model.add(BatchNormalization(axis=-1))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2,2)))
     # FLATTEN
     model.add(Flatten())
     # DENSE 1
-    model.add(Dense(256))
+    model.add(Dense(1024))
     model.add(BatchNormalization())
     model.add(Activation('relu'))
     model.add(Dropout(0.5))
     # DENSE 2
-    model.add(Dense(256))
+    model.add(Dense(1024))
     model.add(BatchNormalization())
     model.add(Activation('relu'))
     model.add(Dropout(0.5))
@@ -243,7 +243,7 @@ def train(C_or_R, mode=0):
             steps_per_epoch=len(t_labels) // BATCH_SIZE,
             class_weight=t_weights,
             epochs=EPOCHS,
-            validation_data=load_images(C_or_R, v_paths, v_labels, BATCH_SIZE),
+            validation_data=load_images(C_or_R, v_paths, v_labels, BATCH_SIZE, eval=True),
             validation_steps=len(v_labels) // BATCH_SIZE,
             callbacks=[ModelCheckpoint('AFF_NET_C_WIP.h5', monitor='val_acc', save_best_only=True)])
     else:
@@ -252,7 +252,7 @@ def train(C_or_R, mode=0):
             load_images(C_or_R, t_paths, t_labels, BATCH_SIZE, eval=True),
             steps_per_epoch=len(t_labels) // BATCH_SIZE,
             epochs=EPOCHS,
-            validation_data=load_images(C_or_R, v_paths, v_labels, BATCH_SIZE),
+            validation_data=load_images(C_or_R, v_paths, v_labels, BATCH_SIZE, eval=True),
             validation_steps=len(v_labels) // BATCH_SIZE,
             callbacks=[ModelCheckpoint('AFF_NET_R_WIP.h5', save_best_only=True)])
 
@@ -267,7 +267,10 @@ def train(C_or_R, mode=0):
     model.save('AFF_NET_' + ns + '_FULL.h5')
 
 # eval(CLASSIFY)
-# train(CLASSIFY, mode=0)
+# 
 # train(REGRESS, mode=1)
 # load_and_save()
-# eval(CLASSIFY)
+# eval(CLASSIFY)
+
+if __name__ == '__main__':
+    train(CLASSIFY, mode=0)