Tricks to improve CNN model performance - tensorflow

Question

I am fitting a large CNN network on my training data, validating on 20%. It appears the model performs better on the training than the validation set. What do you suggest so I can improve the model performance.
CNN Architecture:
model = Sequential()
activ = 'relu'
model.add(Conv2D(32, (1, 3), strides=(1, 1), padding='same', activation=activ, input_shape=(1, 100, 4)))
model.add(Conv2D(32, (1, 3), strides=(1, 1), padding='same', activation=activ))
#model.add(BatchNormalization(axis = 3))
model.add(MaxPooling2D(pool_size=(1, 2) ))
model.add(Conv2D(64, (1, 3), strides=(1, 1), padding='same', activation=activ))
model.add(Conv2D(64, (1, 3), strides=(1, 1), padding='same', activation=activ))
model.add(MaxPooling2D(pool_size=(1, 2)))
model.add(Conv2D(128, (1, 3), strides=(1, 1), padding='same', activation=activ))
model.add(Conv2D(128, (1, 3), strides=(1, 1), padding='same', activation=activ ))
model.add(MaxPooling2D(pool_size=(1, 2)))
model.add(Dropout(.5))
model.add(Flatten())
A = model.output_shape
model.add(Dense(int(A[1] * 1/4.), activation=activ))
model.add(Dropout(.5))
model.add(Dense(5, activation='softmax'))
optimizer = Adam(lr=0.003, beta_1=0.9, beta_2=0.999, epsilon=1e-04, decay=0.0)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=100, batch_size=64, shuffle=False,
validation_split=0.2)
However, the validation accuracy doesn't change for the number of epochs.
Epoch 1/100
1065/1065 [==============================] - 14s 13ms/step - loss: 1.4174 - accuracy: 0.5945 - val_loss: 1.4966 - val_accuracy: 0.4417
Epoch 2/100
1065/1065 [==============================] - 14s 13ms/step - loss: 1.1494 - accuracy: 0.6207 - val_loss: 1.4634 - val_accuracy: 0.4417
Epoch 3/100
1065/1065 [==============================] - 19s 18ms/step - loss: 1.1111 - accuracy: 0.6196 - val_loss: 1.4674 - val_accuracy: 0.4417
Epoch 4/100
1065/1065 [==============================] - 15s 14ms/step - loss: 1.1040 - accuracy: 0.6196 - val_loss: 1.4660 - val_accuracy: 0.4417
Epoch 5/100
1065/1065 [==============================] - 18s 17ms/step - loss: 1.1027 - accuracy: 0.6196 - val_loss: 1.4624 - val_accuracy: 0.4417
NOTE: I Adam´s default learning rate 0.001 as well as 0.003 but the output is the same (log).

Answer 1

Your model is working but improving very slowly. I would reduce the dropout value down to .1 initially, then run the model and see if it overfits or not.If it does then slowly increase the dropout rate. Unless your data is already shuffled I would set shuffle=True in model.fit. Also you might try replacing the Flatten layer with a GlobalMaxPooling layer. I also recommend using the EarlyStopping callback which monitors validation and halts training if the loss fails to reduce after 'patience' number of consecutive epochs. Setting restore_best_weights=True will load the weights for the epoch with the lowest validation loss so you don't have to save then reload the weights. Set epochs to a large number to ensure this callback activates. Also use the ReduceLROnPlateau to automatically adjust the learning rate based on validation loss.
The code I use is shown below
es=tf.keras.callbacks.EarlyStopping( monitor="val_loss", patience=3,
verbose=1, restore_best_weights=True)
rlronp=tf.keras.callbacks.ReduceLROnPlateau( monitor="val_loss", factor=0.5, patience=1,
verbose=1)
callbacks=[es, rlronp]
In model.fit set callbacks=callbacks. Increase the number of epochs you run to say 100 so that the early stopping callback triggers.