[FIXED] Facial expression val_accuracy does not improve

Issue

I am running te following code:

basemodel.fit(X_train,y_train,epochs=25,validation_split=.1,callbacks=call_back) 

But I get a result Epoch 00014: val_accuracy did not improve from 0.57709. I am not sure what is the issue there because I clearly see that my loss has decreased and my accuracy has increased.

This is the result

Epoch 1/25
909/909 [==============================] - 13s 6ms/step - loss: 1.6465 - accuracy: 0.3396 - val_loss: 1.4830 - val_accuracy: 0.4334

Epoch 00001: val_accuracy improved from -inf to 0.43344, saving model to checkpoint/best_model.h5
Epoch 2/25
909/909 [==============================] - 5s 5ms/step - loss: 1.3402 - accuracy: 0.4860 - val_loss: 1.3291 - val_accuracy: 0.4926

Epoch 00002: val_accuracy improved from 0.43344 to 0.49257, saving model to checkpoint/best_model.h5
Epoch 3/25
909/909 [==============================] - 5s 5ms/step - loss: 1.2050 - accuracy: 0.5418 - val_loss: 1.2769 - val_accuracy: 0.5025

Epoch 00003: val_accuracy improved from 0.49257 to 0.50248, saving model to checkpoint/best_model.h5
Epoch 4/25
909/909 [==============================] - 5s 5ms/step - loss: 1.1054 - accuracy: 0.5806 - val_loss: 1.1936 - val_accuracy: 0.5495

Epoch 00004: val_accuracy improved from 0.50248 to 0.54954, saving model to checkpoint/best_model.h5
Epoch 5/25
909/909 [==============================] - 5s 5ms/step - loss: 1.0190 - accuracy: 0.6159 - val_loss: 1.1535 - val_accuracy: 0.5551

Epoch 00005: val_accuracy improved from 0.54954 to 0.55511, saving model to checkpoint/best_model.h5
Epoch 6/25
909/909 [==============================] - 5s 5ms/step - loss: 0.9329 - accuracy: 0.6502 - val_loss: 1.1962 - val_accuracy: 0.5641

Epoch 00006: val_accuracy improved from 0.55511 to 0.56409, saving model to checkpoint/best_model.h5
Epoch 7/25
909/909 [==============================] - 5s 5ms/step - loss: 0.8435 - accuracy: 0.6846 - val_loss: 1.1707 - val_accuracy: 0.5771

Epoch 00007: val_accuracy improved from 0.56409 to 0.57709, saving model to checkpoint/best_model.h5
Epoch 8/25
909/909 [==============================] - 5s 5ms/step - loss: 0.7527 - accuracy: 0.7201 - val_loss: 1.3817 - val_accuracy: 0.5545

Epoch 00008: val_accuracy did not improve from 0.57709
Epoch 9/25
909/909 [==============================] - 5s 5ms/step - loss: 0.6633 - accuracy: 0.7576 - val_loss: 1.5021 - val_accuracy: 0.5207

Epoch 00009: val_accuracy did not improve from 0.57709
Epoch 10/25
909/909 [==============================] - 5s 5ms/step - loss: 0.5865 - accuracy: 0.7874 - val_loss: 1.5610 - val_accuracy: 0.5721

Epoch 00010: val_accuracy did not improve from 0.57709
Epoch 11/25
909/909 [==============================] - 5s 5ms/step - loss: 0.5154 - accuracy: 0.8097 - val_loss: 1.5723 - val_accuracy: 0.5430

Epoch 00011: val_accuracy did not improve from 0.57709
Epoch 12/25
909/909 [==============================] - 5s 6ms/step - loss: 0.4540 - accuracy: 0.8333 - val_loss: 2.1641 - val_accuracy: 0.5650

Epoch 00012: val_accuracy did not improve from 0.57709
Epoch 13/25
909/909 [==============================] - 5s 5ms/step - loss: 0.4106 - accuracy: 0.8511 - val_loss: 2.3236 - val_accuracy: 0.5322

Epoch 00013: val_accuracy did not improve from 0.57709
Epoch 14/25
909/909 [==============================] - 5s 5ms/step - loss: 0.3747 - accuracy: 0.8682 - val_loss: 1.8985 - val_accuracy: 0.5567

Epoch 00014: val_accuracy did not improve from 0.57709
Epoch 15/25
909/909 [==============================] - 5s 5ms/step - loss: 0.3480 - accuracy: 0.8768 - val_loss: 2.1689 - val_accuracy: 0.5505

Epoch 00015: val_accuracy did not improve from 0.57709
Epoch 16/25
909/909 [==============================] - 5s 5ms/step - loss: 0.3224 - accuracy: 0.8878 - val_loss: 2.0880 - val_accuracy: 0.5269

Epoch 00016: val_accuracy did not improve from 0.57709
Epoch 17/25
909/909 [==============================] - 5s 5ms/step - loss: 0.3157 - accuracy: 0.8912 - val_loss: 2.2746 - val_accuracy: 0.5328

Epoch 00017: val_accuracy did not improve from 0.57709
Epoch 18/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2960 - accuracy: 0.8992 - val_loss: 2.3014 - val_accuracy: 0.5582

Epoch 00018: val_accuracy did not improve from 0.57709
Epoch 19/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2961 - accuracy: 0.8998 - val_loss: 2.8190 - val_accuracy: 0.5399

Epoch 00019: val_accuracy did not improve from 0.57709
Epoch 20/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2945 - accuracy: 0.9016 - val_loss: 2.5621 - val_accuracy: 0.5495

Epoch 00020: val_accuracy did not improve from 0.57709
Epoch 21/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2772 - accuracy: 0.9075 - val_loss: 2.6602 - val_accuracy: 0.5402

Epoch 00021: val_accuracy did not improve from 0.57709
Epoch 22/25
909/909 [==============================] - 5s 6ms/step - loss: 0.2857 - accuracy: 0.9070 - val_loss: 2.7156 - val_accuracy: 0.5381

Epoch 00022: val_accuracy did not improve from 0.57709
Epoch 23/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2767 - accuracy: 0.9098 - val_loss: 3.4705 - val_accuracy: 0.5291

Epoch 00023: val_accuracy did not improve from 0.57709
Epoch 24/25
909/909 [==============================] - 5s 6ms/step - loss: 0.2725 - accuracy: 0.9100 - val_loss: 3.5462 - val_accuracy: 0.5706

Epoch 00024: val_accuracy did not improve from 0.57709
Epoch 25/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2675 - accuracy: 0.9134 - val_loss: 2.3214 - val_accuracy: 0.5254

Epoch 00025: val_accuracy did not improve from 0.57709
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Below is a screenshot of my code:

My learning rate is .01.

basemodel.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=.01), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Solution

This is the case of overfitting/memorization of the training data by the model.

Change the Validation data and set it to train data, you will see validation loss will also go down.

With the discussion That I had with you!! You had just 1000 data points and the model that you are building have 403,463 trainable parameters.

Choices that you have

1. Get more data
2. Use pretrained layers(this is known as transfer learning)
3. Use regularization parameter
4. Use Dropout
5. Use Batch normalization (Won't be very Effective)

Getting more data or using pre-trained layers will be highly effective in your case!!

Answered By - Varun Singh