Data Science Asked on January 9, 2021
My model is based on Shallow Net.
When I am training my model, the results are:
loss: 1.1398 - accuracy: 0.6093 - val_loss: 1.2309 - val_accuracy: 0.5657
Then I downloaded 20 images (2 for each class) from the net to check the performance.
Labels corresponding to this dataset should be:0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9].
But my model’s prediction is: [0,0,0,1,0,1,5,0,0,5,2,2,0,0,5,2,0,0,1,9].
The accuracy is: 0.2 which is quite low as compared to 0.5657.
My code to load these datasets:
for file in os.listdir("C:/Users/....."):
img_arr=cv2.imread(os.path.join(os.getcwd(),"Dataset",file))
img_arr=cv2.resize(img_arr,(32,32))/255
img_arrs.append(img_arr)
img_arrs=np.array(img_arrs)
img_arrs=img_arrs.reshape(20,32,32,3)
model=load_model("weights.hdf5")
pred=model.predict(img_arrs).argmax(axis=1)
What could be the reason behind this? Can someone give me an insight?
(x_train,y_train),(x_test,y_test)=cifar10.load_data()
x_train=x_train.astype(float)/255
x_test=x_test.astype(float)/255
lb=LabelBinarizer()
y_train=lb.fit_transform(y_train)
y_test=lb.transform(y_test)
labelNames = ["airplane", "automobile", "bird", "cat", "deer","dog", "frog", "horse", "ship", "truck"]
model=ShallowNet.ShallowNet.build(width=32, height=32, depth=3, classes=10)
sgd=SGD(0.001)
model.compile(optimizer=sgd,loss="categorical_crossentropy",metrics=["accuracy"])
H=model.fit(x_train,y_train,validation_data=(x_test,y_test),batch_size=32,epochs=50,verbose=1)
Here are some possibilities that come to mind:
Answered by James on January 9, 2021
I think ~50% accuracy might not be so good to match it to new images on the basis of accuracy only.
Similarly 2 images per class is also a bit small to check a model unless a model is built on millions of images which has seen almost every type of variances across pixels
What you may try -
Check the Loss of Individual classes on training
Try to match the Loss of these images i.e. 2 per class
Do the same thing for Accuracy i.e. per class accuracy
Try to check the activation of 2nd last layer i.e. layer prior to Softmax
Try to gather 80-90 images and repeat if your Model score ~80%
I meant you should try to look inside the Model instead of a Black-box approach
Answered by 10xAI on January 9, 2021
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