Validation Accuracy stuck at .5073

Question

I am trying to create a regression model but my validation accuracy stays at .5073 . I am trying to train on images and have the network find the position of an object and the rough area it covers. I increased the unfrozen layers and the plateau for accuracy dropped to .4927 . I would appreciate any help finding out what I am doing wrong.

base = MobileNet(weights='imagenet', include_top=False, input_shape=(200,200,3), dropout=.3)
location = base.output
location = GlobalAveragePooling2D()(location)
location = Dense(16, activation='relu', name="locdense1")(location)
location = Dense(32, activation='relu', name="locdense2")(location)
location = Dense(64, activation='relu', name="locdense3")(location)
finallocation = Dense(3, activation='sigmoid', name="finalLocation")(location)

model = Model(inputs=base_model.input,outputs=finallocation)#[types, finallocation])
for layer in model.layers[:91]: #freeze up to 87
    if ('loc' or 'Loc') in layer.name:
        layer.trainable=True
    else: layer.trainable=False

optimizer = Adam(learning_rate=.001)
model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['accuracy'])
history = model.fit(get_batches(type='Train'), validation_data=get_batches(type='Validation'), validation_steps=500, steps_per_epoch=1000, epochs=10)

Data is generated from a tfrecord file which has image data and some labels. This is the last bit of that generator.

IMG_SIZE = 200
def format_position(image, positionx, positiony, width):
    image = tf.cast(image, tf.float32)
    image = (image/127.5) - 1
    image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))
    labels = tf.stack([positionx, positiony, width])
    return image, labels

Get batches: dataset is loaded from two directories with tfrecord files, one for training, and other for validation

def get_batches(type):
    dataset = load_dataset(type=type)
    if type == 'Train':
        databatch = dataset.repeat()
    databatch = dataset.batch(32)
    databatch = databatch.prefetch(2)
    return databatch

```positionx positiony width``` are all normalized from 0-1 (relative position with respect to the image.
Here is an example output:

Epoch 1/10 1000/1000 [==============================] - 233s 233ms/step - loss: 0.0267 - accuracy: 0.5833 - val_loss: 0.0330 - val_accuracy: 0.5073 Epoch 2/10 1000/1000 [==============================] - 283s 283ms/step - loss: 0.0248 - accuracy: 0.6168 - val_loss: 0.0337 - val_accuracy: 0.5073 Epoch 3/10 1000/1000 [==============================] - 221s 221ms/step - loss: 0.0238 - accuracy: 0.6309 - val_loss: 0.0312 - val_accuracy: 0.5073

Answer 1

1. The final activation function in your model should not be sigmoid since it will output numbers between 0 and 1 and I am assuming your labels (ie, positionx , positiony , and width are not in this range). You could replace it with either 'linear' or 'relu' .
2. You're doing regression, and your loss function is 'mean_squared_error' . You cannot use accuracy as the metric function. You should use 'mae' (mean absolute error) or 'mse' to check the difference between your predictions and actual target values.