Input 0 of layer "model_2" is incompatible with the layer: expected shape=(None, 299, 299, 3), found shape=(None, 229, 229, 3)

[itsmebabysmiley]

I am trying to train the model on custom dataset but I got this error. The expected shape and found shape are the same. I don't understand

WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.
Epoch 1/5
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-43-c72e19685f40>](https://localhost:8080/#) in <module>
     21   train_ds,
     22   validation_data=val_ds,
---> 23   epochs=epochs,
     24 )

1 frames
[/usr/local/lib/python3.7/dist-packages/keras/engine/training.py](https://localhost:8080/#) in tf__train_function(iterator)
     13                 try:
     14                     do_return = True
---> 15                     retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
     16                 except:
     17                     do_return = False

ValueError: in user code:

    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1051, in train_function  *
        return step_function(self, iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1040, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1030, in run_step  **
        outputs = model.train_step(data)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 889, in train_step
        y_pred = self(x, training=True)
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 264, in assert_input_compatibility
        raise ValueError(f'Input {input_index} of layer "{layer_name}" is '

    ValueError: Input 0 of layer "model_2" is incompatible with the layer: expected shape=(None, 299, 299, 3), found shape=(None, 229, 229, 3)

Here is how I create the model

base_model = create_model(num_classes=2, dropout_prob=0.2, weights="imagenet", include_top=False)

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(2, activation='softmax')(x)

model = Model(inputs=base_model.input, outputs=predictions)

for layer in base_model.layers:
    layer.trainable = False

opt = Adam(learning_rate=0.001)
model.compile(optimizer=opt,loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model.summary()

#Train the model
epochs = 5
history = model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=epochs,
)

IMAGE_SIZE = (229, 229)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    dataset_dir,
    validation_split=0.2,
    subset="training",
    seed=1337,
    image_size=IMAGE_SIZE,
    batch_size=batch_size,
)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    dataset_dir,
    validation_split=0.2,
    subset="validation",
    seed=1337,
    image_size=IMAGE_SIZE,
    batch_size=batch_size,
)