Accuracy doesn't change over all epochs with multi-class classification - machine-learning

Question

I am trying to train a model to solve multi-class classification problem.
I've got a problem that is training accuracy and validation accuracy doesn't change over all epochs. Like this:
Train on 4642 samples, validate on 516 samples
Epoch 1/100
- 1s - loss: 1.7986 - acc: 0.4649 - val_loss: 1.7664 - val_acc: 0.4942
Epoch 2/100
- 1s - loss: 1.6998 - acc: 0.5017 - val_loss: 1.7035 - val_acc: 0.4942
Epoch 3/100
- 1s - loss: 1.6956 - acc: 0.5022 - val_loss: 1.7000 - val_acc: 0.4942
Epoch 4/100
- 1s - loss: 1.6900 - acc: 0.5022 - val_loss: 1.6954 - val_acc: 0.4942
Epoch 5/100
- 1s - loss: 1.6931 - acc: 0.5017 - val_loss: 1.7058 - val_acc: 0.4942
...
Epoch 98/100
- 1s - loss: 1.6842 - acc: 0.5022 - val_loss: 1.6995 - val_acc: 0.4942
Epoch 99/100
- 1s - loss: 1.6844 - acc: 0.5022 - val_loss: 1.6977 - val_acc: 0.4942
Epoch 100/100
- 1s - loss: 1.6838 - acc: 0.5022 - val_loss: 1.6934 - val_acc: 0.4942
My code with keras:
y_train = to_categorical(y_train, num_classes=11)
X_train, X_test, Y_train, Y_test = train_test_split(x_train, y_train,
test_size=0.1, random_state=42)
model = Sequential()
model.add(Dense(64, init='normal', activation='relu', input_dim=160))
model.add(Dropout(0.3))
model.add(Dense(32, init='normal', activation='relu'))
model.add(BatchNormalization())
model.add(Dense(11, init='normal', activation='softmax'))
model.summary()
print("[INFO] compiling model...")
model.compile(optimizer=keras.optimizers.Adam(lr=0.01, beta_1=0.9,
beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),
loss='categorical_crossentropy',
metrics=['accuracy'])
print("[INFO] training network...")
model.fit(X_train, Y_train, epochs=100, batch_size=32, verbose=2, validation_data = (X_test, Y_test))
Please help me. Thank you!

Answer 1

I had a similar problem once. For me it turned out that making sure I didnt have too many missing values in x_train (having to fill with value representing unknown or filling with median value), dropping columns that really didnt help (all had same value), and normalizing the x_train data helped.
Example from my data/model,
# load data
x_main = pd.read_csv("glioma DB X.csv")
y_main = pd.read_csv("glioma DB Y.csv")
# fill with median (will have to improve later, not done yet)
fill_median =['Surgery_SBRT','df','Dose','Ki67','KPS','BMI','tumor_size']
x_main[fill_median] = x_main[fill_median].fillna(x_main[fill_median].median())
x_main['Neurofc'] = x_main['Neurofc'].fillna(2)
x_main['comorbid'] = x_main['comorbid'].fillna(int(x_main['comorbid'].median()))
# drop surgery
x_main = x_main.drop(['Surgery'], axis=1)
# normalize all x
x_main_normalized = x_main.apply(lambda x: (x-np.mean(x))/(np.std(x)+1e-10))