I am trying to predict a single image. But my model returns a prediction array with the shape (1,1,1,2048) when it should be (1,10). Any idea what I am doing wrong? My x input shape is correct at (1,32,32,3).
ResNet50V2(): IMG_SHAPE = (32, 32, 3) return tf.keras.applications.ResNet50V2(input_shape=IMG_SHAPE, include_top=False, weights=None, classes=10) model = ResNet50V2() x = x[None, :] predictions = model.predict(x)
You are loading your keras-model with parameter
which cuts of the fully-connected projection layer that is responsible for projecting the model output to your expected amout of classes. Change the parameter to True.