-
Notifications
You must be signed in to change notification settings - Fork 19.4k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Getting Wrong output even though vgg16 model showing 95% val_accuracy #19612
Comments
Hi @Avataryug-hs , Since you are using same dataset for training and testing and getting good val_accuracy also I would except the model to perform well. If you are using a new dataset for predictions and not getting good accuracy that means either the new dataset is having different probability distribution or trained model might be overfitting. |
Hi
I am not using same dataset for training and testing, both contain different images, also trained model is not overfitting by the graph i got , loss functions are seeming to decrease .. attaching the screenshot .
[cid:5480370a-567d-47b4-acd3-d894c58cbced]
…________________________________
From: Surya ***@***.***>
Sent: 25 April 2024 09:52 AM
To: keras-team/keras ***@***.***>
Cc: Harsh Shinde ***@***.***>; Mention ***@***.***>
Subject: Re: [keras-team/keras] Getting Wrong output even though vgg16 model showing 95% val_accuracy (Issue #19612)
Hi @Avataryug-hs<https://github.com/Avataryug-hs> ,
Since you are using same dataset for training and testing and getting good val_accuracy also I would except the model to perform well. If you are using a new dataset for predictions and not getting good accuracy that means either the new dataset is having different probability distribution or trained model might be overfitting.
—
Reply to this email directly, view it on GitHub<#19612 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BG7XZS44RUKSAK5VPAKCZY3Y7CAJHAVCNFSM6AAAAABGYCGWKCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDANZWGMZTEMRVGU>.
You are receiving this because you were mentioned.Message ID: ***@***.***>
|
Could you provide code for loading and creating datasets and for model's predictions? |
Could you please provide some dummy dataset along with reproducible code snippet? Thanks! |
In this project i have been trying to classify 3 hairstyles - Braided , curly and straight …i am using transfer learning to classify the images … vvg16 model is giving roughly same training and testing accuracy … but when trying to predict actual image , its giving wrong result ( predicting different class)…
My Dataset is roughly 400 images for training and 100 for testing ( all are evenly divided )
Please suggest something , and help me out
Attaching model code -
`from keras.applications import VGG16
Load the pre-trained VGG16 model without the top (fully connected) layers
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
Freeze the base model layers so they are not trainable
for layer in base_model.layers:
layer.trainable = False
Create a new model by adding custom top layers on top of the base model
model = Sequential()
model.add(base_model)
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.20))
model.add(Dense(3, activation='softmax')) # Assuming 3 classes
Compile the model
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])
Print the model summary
model.summary()`
The text was updated successfully, but these errors were encountered: