OneEye Changeset - dd45e200a0dc

Changeset - dd45e200a0dc

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Laman - 6 years ago 2019-05-04 20:10:46

convolution neural network model

2 files changed with 56 insertions and 15 deletions:

exp/kerokero/test.py

exp/kerokero/train.py

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exp/kerokero/test.py

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@@ @@ -20,7 +20,7 @@ print("loading data...") @@
 print("done")
 for img in testImages:
 	label=model.predict(np.reshape(img,(1,224,224)))
+	label=model.predict(np.reshape(img,(1,224,224,1)))
 	print(label)
 	points=[]
 	for i in range(4):

exp/kerokero/train.py

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 import argparse
 from keras.layers import Conv2D,Dropout,Dense,Flatten
+from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization
 from keras.models import Sequential,load_model
 from prepare_data import loadDataset
@@ @@ -14,19 +14,60 @@ parser.add_argument("--epochs",type=int, @@
 parser.add_argument("--initial_epoch",type=int,default=0)
 args=parser.parse_args()
 model=Sequential([
 	Flatten(input_shape=(224,224)),
 	Dense(128, activation="relu"),
 	Dropout(0.1),
 	Dense(64, activation="relu"),
 	Dense(8)
 ])
 def createFullyConnected():
 	model=Sequential([
 		Flatten(input_shape=(224,224)),
 		Dense(128, activation="relu"),
 		Dropout(0.1),
 		Dense(64, activation="relu"),
 		Dense(8)
 	])
 	model.compile(
 		optimizer='adam',
 		loss='mse',
 		metrics=['mae','accuracy']
+	)
 	return model
 model.compile(
 	optimizer='adam',
 	loss='mse',
 	metrics=['mae','accuracy']
+)
 def createCNN():
 	model=Sequential()
 	model.add(Conv2D(filters=16,kernel_size=2,padding="same",activation="relu",input_shape=(224,224,1)))
 	model.add(Dropout(0.1))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 	model.add(BatchNormalization())
 	model.add(Conv2D(32,(5,5),activation="relu"))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 	model.add(Dropout(0.2))
 	model.add(BatchNormalization())
 	model.add(Conv2D(64,(5,5),activation="relu"))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 	model.add(BatchNormalization())
 	model.add(Conv2D(128,(3,3),activation="relu"))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 	model.add(Dropout(0.4))
 	model.add(BatchNormalization())
 	model.add(Flatten())
 	model.add(Dense(500, activation="relu"))
 	model.add(Dropout(0.1))
 	model.add(Dense(128, activation="relu"))
 	model.add(Dropout(0.1))
 	model.add(Dense(8))
 	model.compile(optimizer='adam',loss='mse',metrics=['mae','accuracy'])
 	return model
 model=createCNN()
 if args.load_model:
 	model=load_model(args.load_model)
@@ @@ -35,6 +76,6 @@ print("loading data...") @@
 print("done")
 for i in range(args.initial_epoch,args.epochs//10):
-	model.fit(trainImages,trainLabels,epochs=(i+1)*10,initial_epoch=i*10,batch_size=128,validation_split=1/9)
+	model.fit(trainImages.reshape((-1,224,224,1)),trainLabels,epochs=(i+1)*10,initial_epoch=i*10,batch_size=128,validation_split=0.2)
 	model.save(args.save_model.format(i+1))
 print(model.evaluate(testImages,testLabels))

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