AnswerBun.com

Why does my model sometimes not learn well from same data?

Data Science Asked by ManInMoon on November 30, 2020

I have a dataset of 2 classes, both containing 2K images. I have split that into 1500 images for training and 500 images for validation.

This is a simple structure for testing purposes, and each image is classified depending on the colour of a particular pixel. Either Green or Red.

I have run this model many times and I find that sometimes the models gets low loss/ high accuracy within a few epochs, but other times it gets stuck at accuracy 50%.

The datasets are exactly the same each time with only difference coming from model.fit “shuffle” option.

I tested the LR Range first:

enter image description here

and I “cycle” the learning rate through an appropriate range.

model = keras.Sequential([
        keras.layers.Dense(112, activation=tf.nn.relu, input_shape=(224, 224, 3)),
        keras.layers.Dropout(0.4),
        keras.layers.Flatten(),
        keras.layers.Dense(3, activation=tf.nn.softmax)
    ])


LRS = CyclicLR(base_lr=0.000005, max_lr=0.0003, step_size=200.)

model.fit(train_images, train_labels, shuffle=True, epochs=10,
              callbacks=[checkpoint,
                         LRS],
              validation_data = (test_images, test_labels)
              )

Why does the model sometimes NOT get a good fit?

EDIT 1

Re Serali’s suggestion:

myOpt = keras.optimizers.Adam(lr=0.001,decay=0.01)
model.compile(optimizer=myOpt, loss='categorical_crossentropy',  metrics=['accuracy'])

reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,verbose=1,patience=5, min_lr=0.00001)

2 Answers

Have you tried without the "CycleLR" part? It is obviously a good idea but as far as I know, not a standard Keras function - needs to be implemented separately. Any small error in this implementation might cause problems.

Answered by serali on November 30, 2020

Shuffle = True should give better results particularly when you are running for more epochs. But I am not clear why there is so huge difference in the accuracy when shuffle changes . One thing you can try is , to increase the number of epochs and see if the accuracy improves. For shuffle = False

Shuffle set to false, allows you to use the previously trained data. Setting this to true means that you either want to retrain or set the epoch to some value greater than 10. To learn, but this increases the chances of memorization (over fitting)

Answered by Bala_Science on November 30, 2020

Add your own answers!

Related Questions

Decomposing R squared or VIF

1  Asked on December 17, 2020 by jun-jang

   

Missing population values in census data

1  Asked on December 17, 2020 by threadid

     

Sample Space of Longest Run

1  Asked on December 16, 2020 by a-may

   

When to choose character instead of factor in R?

2  Asked on December 16, 2020 by lupi5

   

Data storage for Intrusion Detection System

0  Asked on December 16, 2020 by user2071938

     

Demand Forecasting – Error in predictions

0  Asked on December 16, 2020 by gopik-anand

         

How does attention mechanism learn?

3  Asked on December 16, 2020 by user2790103

       

Representing geospatial information

1  Asked on December 15, 2020 by exi_le

   

Ask a Question

Get help from others!

© 2022 AnswerBun.com. All rights reserved. Sites we Love: PCI Database, MenuIva, UKBizDB, Menu Kuliner, Sharing RPP, SolveDir