I have an issue with my model. I'm trying to use the most basic Conv1D model to analyze review data and output a rating of 1-5 class, therefore the loss is categorical_crossentropy. Model structure is as below
# define model model = Sequential() model.add(Embedding(vocab_size, 100, input_length=max_length)) model.add(Conv1D(filters=32, kernel_size=8, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(10, activation='relu')) model.add(Dense(5, activation='softmax')) # compile network model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # fit network model.fit(final_X_train, final_Y_train, epochs=5, batch_size=50, validation_data=(final_X_val, final_Y_val), callbacks=callback)
Total params: 14,977,717
Trainable params: 14,977,717
Non-trainable params: 0
Train on 212135 samples, validate on 69472 samples
Epoch 1/5 loss: 0.9452 - acc: 0.5781 - val_loss: 0.8440 - val_acc: 0.6309
Epoch 2/5 loss: 0.7707 - acc: 0.6711 - val_loss: 0.8234 - val_acc: 0.6433
Epoch 3/5 loss: 0.5807 - acc: 0.7657 - val_loss: 0.9144 - val_acc: 0.6345
Epoch 4/5 loss: 0.3736 - acc: 0.8575 - val_loss: 1.1982 - val_acc: 0.6194
Epoch 5/5 loss: 0.2285 - acc: 0.9173 - val_loss: 1.5770 - val_acc: 0.6073
Training acc increases and loss decreases as expected. But validation loss and validation acc decrease straight after the 2nd epoch itself. The overall testing after training gives an accuracy around 60s.
The total accuracy is : 0.6046845041714888
I've already cleaned, shuffled, down-sampled (all classes have 42427 number of data samples) and split the data properly to training(70%) / validation(10%) / testing(20%).
If you see any improvements to fix this problem, please let me know. :)