Getting constant accuracies for training and validation sets despite their losses are changing during CNN training? - tensorflow

Question

As the title clearly describes the issue I've been experiencing during the training of my CNN model, the accuracies of training and validation sets are constant despite the losses of them are changing. I have included the detail regarding the model and its training setup below. What may cause this issue?
Here is the data that was used by training (X_train & y_train), validation, and test sets (X_test and y_test):
df = pd.read_csv(CSV_PATH, sep=',', header=None)
print(f'Shape of all data: {df.shape}')
y = df.iloc[:, -1].values
X = df.iloc[:, :-1].values
encoder = LabelEncoder()
encoder.fit(y)
encoded_Y = encoder.transform(y)
dummy_y = to_categorical(encoded_Y)
X_train, X_test, y_train, y_test = train_test_split(X, dummy_y, test_size=0.3, random_state=RANDOM_STATE)
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
Here are the shapes of training and test sets:
Shape of X_train: (1322, 10800, 1)
Shape of Y_train: (1322, 3)
Shape of X_test: (567, 10800, 1)
Shape of y_test: (567, 3)
Here is my CNN model:
# Model hyper-parameters
activation_fn = 'relu'
n_lr = 1e-4
weight_decay = 1e-4
batch_size = 64
num_epochs = 200*10*10
num_classes = 3
n_dropout = 0.6
n_momentum = 0.5
n_kernel = 5
n_reg = 1e-5
# the sequential model
model = Sequential()
model.add(Conv1D(128, n_kernel, input_shape=(10800, 1)))
model.add(BatchNormalization())
model.add(Activation(activation_fn))
model.add(MaxPooling1D(pool_size=2, strides=2))
model.add(Dropout(n_dropout))
model.add(Conv1D(256, n_kernel))
model.add(BatchNormalization())
model.add(Activation(activation_fn))
model.add(MaxPooling1D(pool_size=2, strides=2))
model.add(Dropout(n_dropout))
model.add(GlobalAveragePooling1D()) # have tried model.add(Flatten()) as well
model.add(Dense(256, activation=activation_fn))
model.add(Dropout(n_dropout))
model.add(Dense(64, activation=activation_fn))
model.add(Dropout(n_dropout))
model.add(Dense(num_classes, activation='softmax'))
adam = Adam(lr=n_lr, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=weight_decay)
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['acc'])
Here is how I have evaluated the model:
Y_pred = model.predict(X_test, verbose=0)
y_pred = np.argmax(Y_pred, axis=1)
y_test_int = np.argmax(y_test, axis=1)
And, my model always predicts the same class of three classes during the model evaluation as you can see from the classification result below (via classification_result(y_test_int, y_pred) function):
precision recall f1-score support
normal 0.743 1.000 0.852 421
apb 0.000 0.000 0.000 45
pvc 0.000 0.000 0.000 101
The model was trained using the EarlyStopping callback of Keras. Thus, the training has continued for 4,173 epochs. Here is the obtained losses during the training for training and validation sets:
Here are the obtained accuracies during the training for training and validation sets:
The model was implemented using Keras and hosted on Google Colab.

Answer 1

Although such issues are difficult to resolve without the data, there are a couple of general rules applicable.
The very first thing we do when the model does not seem to learn anything, like here (despite the mild drop in the loss), is to remove all dropout.
In fact, dropout is not supposed to be used by default; its nominal function is to guard against overfitting - but of course, before starting to worry about overfitting, you must first have some success with fitting, something that is clearly not happening here. The fact that, with a dropout rate of n_dropout = 0.6, you also seem to be rather too aggressive in its use, does not help, either.