I am new to deep learning. I have imbalanced class data. I used one hot encoding and scaling to preprocess my data. I have used adamoptimizer as optimizer function and sparse categorical crossentropy as my lass function. The model always gives high accuracy on one class with very low accuracy on other classes. Here is my code:

`

===============separating test data according to classes=================

data_test = data_final[data_final.YEAR.isin(2018)]
data_test_0 = data_test[data_test['DELAY_CLASS']==0]
test_labels_0 = data_test_0.pop('DELAY_CLASS')
data_test_1 = data_test[data_test['DELAY_CLASS']==1]
test_labels_1 = data_test_1.pop('DELAY_CLASS')
data_test_2 = data_test[data_test['DELAY_CLASS']==2]
test_labels_2 = data_test_2.pop('DELAY_CLASS')
data_test_3 = data_test[data_test['DELAY_CLASS']==3]
test_labels_3 = data_test_3.pop('DELAY_CLASS')

============Extracting continuous columns from training data==============

data_train = data_train[['MONTH','DAY_OF_MONTH','DAY_OF_WEEK',
'Dep_Hour','Arr_Hour','CRS_ELAPSED_TIME','DISTANCE','traffic','O_SurfaceTemperatureFahrenheit','O_CloudCoveragePercent','O_WindSpeedMph','O_PrecipitationPreviousHourInches','O_SnowfallInches','D_SurfaceTemperatureFahrenheit',
'D_CloudCoveragePercent','D_WindSpeedMph','D_PrecipitationPreviousHourInches','D_SnowfallInches','Bird_Strike']]

============Extracting continuous columns from testing data==============

data_test = data_test[['MONTH','DAY_OF_MONTH','DAY_OF_WEEK',
'Dep_Hour','Arr_Hour','CRS_ELAPSED_TIME','DISTANCE','traffic','O_SurfaceTemperatureFahrenheit','O_CloudCoveragePercent','O_WindSpeedMph','O_PrecipitationPreviousHourInches','O_SnowfallInches','D_SurfaceTemperatureFahrenheit',
'D_CloudCoveragePercent','D_WindSpeedMph','D_PrecipitationPreviousHourInches','D_SnowfallInches','Bird_Strike']]

print("reached here")

==========SMOTE================

sm = SMOTE(random_state=2)
ad = ADASYN(random_state=2)
data_train, train_labels = sm.fit_sample(data_train, train_labels)

data_train = pd.DataFrame(data_train)
data_train = data_train.rename(columns = {0:'MONTH',1:'DAY_OF_MONTH',2:'DAY_OF_WEEK',3:'Dep_Hour',
4:'Arr_Hour', 5:'CRS_ELAPSED_TIME', 6:'DISTANCE',
7:'traffic',8:'O_SurfaceTemperatureFahrenheit',9:'O_CloudCoveragePercent',
10:'O_WindSpeedMph',11:'O_PrecipitationPreviousHourInches',12:'O_SnowfallInches',
13:'D_SurfaceTemperatureFahrenheit',14:'D_CloudCoveragePercent',15:'D_WindSpeedMph',
16:'D_PrecipitationPreviousHourInches',17:'D_SnowfallInches',18:'Bird_Strike'})

==================taking only continuous columns===================

cols = ['MONTH','DAY_OF_MONTH','DAY_OF_WEEK',
'Dep_Hour','Arr_Hour','CRS_ELAPSED_TIME','DISTANCE','traffic','O_SurfaceTemperatureFahrenheit','O_CloudCoveragePercent','O_WindSpeedMph','O_PrecipitationPreviousHourInches','O_SnowfallInches','D_SurfaceTemperatureFahrenheit','D_CloudCoveragePercent','D_WindSpeedMph','D_PrecipitationPreviousHourInches','D_SnowfallInches','Bird_Strike']

======================scaling=============================

train_mean = data_train[cols].mean(axis=0)
train_std = data_train[cols].std(axis=0)
data_train[cols] = (data_train[cols] - train_mean) / train_std
data_test[cols] = (data_test[cols] - train_mean) / train_std
rain_labels = pd.Series(train_labels)

=====taking continuous columns from test separated data========

data_test_0 = data_test_0[['MONTH','DAY_OF_MONTH','DAY_OF_WEEK',
'Dep_Hour','Arr_Hour','CRS_ELAPSED_TIME','DISTANCE','traffic','O_SurfaceTemperatureFahrenheit','O_CloudCoveragePercent','O_WindSpeedMph','O_PrecipitationPreviousHourInches','O_SnowfallInches','D_SurfaceTemperatureFahrenheit','D_CloudCoveragePercent','D_WindSpeedMph','D_PrecipitationPreviousHourInches','D_SnowfallInches','Bird_Strike']]

data_test_1 = data_test_1[['MONTH','DAY_OF_MONTH','DAY_OF_WEEK','Dep_Hour','Arr_Hour','CRS_ELAPSED_TIME','DISTANCE','traffic','O_SurfaceTemperatureFahrenheit','O_CloudCoveragePercent','O_WindSpeedMph','O_PrecipitationPreviousHourInches','O_SnowfallInches','D_SurfaceTemperatureFahrenheit','D_CloudCoveragePercent','D_WindSpeedMph','D_PrecipitationPreviousHourInches','D_SnowfallInches','Bird_Strike']]

data_test_2 = data_test_2[['MONTH','DAY_OF_MONTH','DAY_OF_WEEK',
'Dep_Hour','Arr_Hour','CRS_ELAPSED_TIME','DISTANCE','traffic','O_SurfaceTemperatureFahrenheit','O_CloudCoveragePercent','O_WindSpeedMph','O_PrecipitationPreviousHourInches','O_SnowfallInches','D_SurfaceTemperatureFahrenheit',
'D_CloudCoveragePercent','D_WindSpeedMph','D_PrecipitationPreviousHourInches','D_SnowfallInches','Bird_Strike']]

data_test_3 = data_test_3[['MONTH','DAY_OF_MONTH','DAY_OF_WEEK',
'Dep_Hour','Arr_Hour','CRS_ELAPSED_TIME','DISTANCE','traffic','O_SurfaceTemperatureFahrenheit','O_CloudCoveragePercent','O_WindSpeedMph','O_PrecipitationPreviousHourInches','O_SnowfallInches','D_SurfaceTemperatureFahrenheit',
'D_CloudCoveragePercent','D_WindSpeedMph','D_PrecipitationPreviousHourInches','D_SnowfallInches','Bird_Strike']]

===================================my model====================

def build_model():
model = keras.Sequential([
layers.Dense(100, activation = 'sigmoid', input_shape=[len(data_train.keys())]),
#layers.Dropout(0.5),
layers.Dense(50, activation = 'softplus'),
#layers.Dropout(0.3),
layers.Dense(25, activation = 'sigmoid'),
#layers.Dropout(0.2),
layers.Dense(4, activation = 'softmax')
])

model.compile(loss='sparse_categorical_crossentropy',#with binary crossentropy use sigmoid and 1 output neuron

              optimizer= tf.train.AdamOptimizer(0.001),

              metrics=['accuracy'])

return model

model = build_model()
model.fit(data_train, train_labels, epochs=5, batch_size=128)

test_loss, test_acc = model.evaluate(data_test_0, test_labels_0)
print(test_acc)
test_loss, test_acc = model.evaluate(data_test_1, test_labels_1)
print(test_acc)
test_loss, test_acc = model.evaluate(data_test_2, test_labels_2)
print(test_acc)
test_loss, test_acc = model.evaluate(data_test_3, test_labels_3)
print(test_acc)`

The training data is flights data of 2016 and 2017 and testing data is of 2018. I have separated classes from testing data to see the class wise accuracy of testing data.

The output is:
Class 0:
44929/44929 [==============================] - 1s 12us/step
0.027710387500278218
Class 1:
10668/10668 [==============================] - 0s 11us/step
0.015935508061492312
Class 2:
33204/33204 [==============================] - 0s 9us/step
0.8956149861318866
Class 3:
274983/274983 [==============================] - 2s 9us/step
0.035293090845941046

The output remains somewhat same if I use adasyn instead of SMOTE or change layers and activation functions. Please help me out.
Thanks in advance.