Validation Accuracy stays constant upto 4 decimal places while Training Accuracy increases

This is my model summary for reference:

 Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 98, 98, 32)        320       
_________________________________________________________________
activation (Activation)      (None, 98, 98, 32)        0         
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 49, 49, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 47, 47, 32)        9248      
_________________________________________________________________
activation_1 (Activation)    (None, 47, 47, 32)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 23, 23, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 21, 21, 64)        18496     
_________________________________________________________________
activation_2 (Activation)    (None, 21, 21, 64)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 10, 10, 64)        0         
_________________________________________________________________
flatten (Flatten)            (None, 6400)              0         
_________________________________________________________________
dense (Dense)                (None, 64)                409664    
_________________________________________________________________
activation_3 (Activation)    (None, 64)                0         
_________________________________________________________________
dropout (Dropout)            (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 26)                1690      
_________________________________________________________________
activation_4 (Activation)    (None, 26)                0         
=================================================================
Total params: 439,418
Trainable params: 439,418
Non-trainable params: 0
_________________________________________________________________

This is the output i am getting when I train my model:

WARNING:tensorflow:sample_weight modes were coerced from
  ...
    to  
  ['...']
WARNING:tensorflow:sample_weight modes were coerced from
  ...
    to  
  ['...']
Train for 41 steps, validate for 8 steps
Epoch 1/5
41/41 [==============================] - 29s 719ms/step - loss: 0.4022 - acc: 0.8336 - val_loss: 0.1885 - val_acc: 0.9615
Epoch 2/5
41/41 [==============================] - 20s 480ms/step - loss: 0.2523 - acc: 0.9294 - val_loss: 0.1723 - val_acc: 0.9615
Epoch 3/5
41/41 [==============================] - 20s 481ms/step - loss: 0.2204 - acc: 0.9485 - val_loss: 0.1672 - val_acc: 0.9615
Epoch 4/5
41/41 [==============================] - 20s 482ms/step - loss: 0.2098 - acc: 0.9548 - val_loss: 0.1647 - val_acc: 0.9615
Epoch 5/5
41/41 [==============================] - 20s 498ms/step - loss: 0.1997 - acc: 0.9583 - val_loss: 0.1669 - val_acc: 0.9615

This is the model history after I trained the model:

{'loss': [0.40232268926556125, 0.2524432904698366, 0.22039496203873055, 0.2098230598894365, 0.199863911314976],
 
'acc': [0.83358896, 0.9294478, 0.9485017, 0.9548135, 0.958294],
 
'val_loss': [0.18854963406920433, 0.17227689549326897, 0.16715877316892147, 0.16466446034610271, 0.16689967922866344],
 
'val_acc': [0.9615384, 0.9615384, 0.9615384, 0.9615384, 0.9615384]}

The val_acc stays at 0.9615384 no matter what i do. I have tried increasing the number of epochs, my dataset is unbiased and i am using data augmentation so i don’t think the amount of data is the problem.

NOTE:
This is my first time using ImageDataGenerator(). The error could be because i do not use the proper use of generators.

The code i used to compile my model:

model.compile(
          loss='binary_crossentropy',
          optimizer=tf.keras.optimizers.Adam(learning_rate=0.001, amsgrad=True),
          metrics=['acc']
        )

The code i used to train my model:

model_hist = model.fit( train_generator, 
                             steps_per_epoch=train.shape[0] // batch_size, 
                             epochs=5, 
                             validation_data=validation_generator, 
                             validation_steps=validate.shape[0] // batch_size)

I have used the flow_from_dataframe() function to create the generator form a dataframe called ‘train’

Can someone please help me on how to resolve the problem? Or let me know where am I going wrong?