Validation accuracy constant in Keras CNN for multiclass image classification

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I'm performing a multiclass image classification task. While training my CNN the validation accuracy remains constant across all epochs. I've tried different model architectures and different hyperparameter values but no change. Any ideas would be greatly appreciated. Here are my current results: Train and Validation Loss and Accuracy

Here is my CNN:

model = models.Sequential()

model.add(Conv2D(32, (3, 3), activation = 'relu', input_shape= . 
(img_width, img_height, 3)))

model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(64, (3, 3), activation = 'relu'))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(128, (3, 3), activation = 'relu'))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Flatten())
model.add(layers.Dropout(0.2))
model.add(layers.Dense(64, activation = 'relu'))

model.add(layers.Dropout(0.2))
model.add(layers.Dense(8, activation = 'softmax'))

model.compile(loss='categorical_crossentropy',
optimizer=optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, 
epsilon=1e-08, decay=0.0001),metrics = ['acc'])

model.summary()
conv-neural-network keras tensorflow python
1 Answers

There are a variety of possible underlying factors that can potentially cause this phenomenon - below is a list, by no means exhaustive, of some preliminary fixes you could try:

  • If you're using the Adam optimizer(or any other adaptive learning rate optimizer such as RMSprop or Adadelta), try a significantly smaller initial learning rate than the default, somewhere on the order of 10E-6. Alternatively, try Stochastic Gradient Descent with an initial learning rate somewhere in the regime of 10E-2 to 10E-3. You could also set a large initial learning rate and anneal it over the course of several training epochs by employing Keras's LearningRateScheduler callback and defining a custom learning rate schedule(for SGD).

  • If the above doesn't work, try decreasing the complexity of your network (e.g. the number of layers) and increasing the size of the training set. Also, while you're inspecting your training dataset, ensure that your training set doesn't suffer from class imbalance - if it does, you can artificially weight the losses associated with the underrepresented class's training examples using the class_weights parameter that can be passed to the model's fit() method.

  • If the issue still persists, you may have to confront the possibility that a constant validation loss is possibly an artifact of essentially fitting on noise and any (even somewhat plausible) predictions the model emits may be spurious. You may want to try extracting more informative features, a larger variety of features or perform extensive data augmentation on your training set at this point.

Have a look at this GitHub issue for further suggestions that may help resolve your problem:

https://github.com/keras-team/keras/issues/1597

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