Validation Accuracy Stuck, Accuracy low

Question

I want to create a machine learning model with Tensorflow which detects flowers. I went in the nature and took pictures of 4 different species (~600 per class, one class got 700).

I load these images with Tensorflow Train Generator:

 train_datagen = ImageDataGenerator(rescale=1./255,
        shear_range=0.2,
        zoom_range=0.15, 
        brightness_range=[0.7, 1.4], 
        fill_mode='nearest', 
        vertical_flip=True,  
        horizontal_flip=True,
        rotation_range=15, 
        
        
        width_shift_range=0.1, 
        height_shift_range=0.1, 
    
        validation_split=0.2) 

    train_generator = train_datagen.flow_from_directory(
        pfad,
        target_size=(imageShape[0],imageShape[1]),
        batch_size=batchSize,
        class_mode='categorical',
        subset='training',
        seed=1,
        shuffle=False,
        #save_to_dir=r'G:\test'
        ) 
    
    validation_generator = train_datagen.flow_from_directory(
        pfad, 
        target_size=(imageShape[0],imageShape[1]),
        batch_size=batchSize,
        shuffle=False,
        seed=1,
        class_mode='categorical',
        subset='validation')

Then I am creating a simple model looking like this:

model = tf.keras.Sequential([
      
     
      
      keras.layers.Conv2D(128, (3,3), activation='relu', input_shape=(imageShape[0], imageShape[1],3)),
      keras.layers.MaxPooling2D(2,2),
      keras.layers.Dropout(0.5),
      
      keras.layers.Conv2D(256, (3,3), activation='relu'),
      
      keras.layers.MaxPooling2D(2,2), 
     
      keras.layers.Conv2D(512, (3,3), activation='relu'),
      
      keras.layers.MaxPooling2D(2,2),
     
      keras.layers.Flatten(),

      
      
      
      
      keras.layers.Dense(280, activation='relu'),
      
      keras.layers.Dense(4, activation='softmax')
    ])
    
    
    opt = tf.keras.optimizers.SGD(learning_rate=0.001,decay=1e-5)
    model.compile(loss='categorical_crossentropy',
                  optimizer= opt,
                  metrics=['accuracy'])

And want to start the training process (CPU):

history=model.fit(
        train_generator,
        steps_per_epoch = train_generator.samples // batchSize,
        validation_data = validation_generator, 
        validation_steps = validation_generator.samples // batchSize,
        epochs = 200,callbacks=[checkpoint,early,tensorboard],workers=-1)

The result should be that my validation Accuracy improves, but it starts with 0.3375 and stays at this level the whole training process. Validation loss (1.3737) decreases by 0.001. Accuracy start with 0.15 but increases.

Why is my validation accuracy stuck? Am I using the right loss? Or do I build my model wrong? Is my Tensorflow Train Generator hot encoding the labels?

Thanks

Answer 1

I solved the problem by using RMSprop() without any parameters.

So I changed from:

opt = tf.keras.optimizers.SGD(learning_rate=0.001,decay=1e-5)
model.compile(loss='categorical_crossentropy',optimizer= opt, metrics=['accuracy'])

to:

    opt = tf.keras.optimizers.RMSprop()
    model.compile(loss='categorical_crossentropy',
                  optimizer= opt,
                  metrics=['accuracy'])