Validation Accuracy remains constant while training VGG? I posted this question on stackoverflow and got downvoted for unmentioned reason, so I'll repost it here, hoping to get some insights
This is the plot

This is the code:
with strategy.scope():

  model2 = tf.keras.applications.VGG16(
    include_top=True,
    weights=None,
    input_tensor=None,
    input_shape=(32, 32, 3),
    pooling=None,
    classes=10,
    classifier_activation=&quot;relu&quot;,
  )

  model2.compile(optimizer='adam',
                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])
  
  history = model2.fit(
            train_images, train_labels,epochs=10, 
            validation_data=(test_images, test_labels)
            )

I'm trying to train VGG16 from scratch, hence not importing their weights I also tried a model which I created myself, with same hyperparameters, and that worked fine
Any help is highly appreciated
Heres the full code
Ok, I solved this problem
The simple thing was that learning rate was too big
I changed the code to this
LR = batch_size/((z+1)*100000)
LR=LR/3

instead of
LR = batch_size/((z+1)*1000)
LR=LR/3

and it seems to work well