passing data from a dataset into a neural network - neural-network

Question

I am an absolute beginner in neural networks and I have problems passing data into the network.
This is the model:
model = keras.Sequential([keras.layers.Flatten(input_shape=(300,150,3)),keras.layers.Dense(128,activation='relu'),keras.layers.Dense(10,activation='softmax')])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
The data consists of images with labels as tuples. Its a mutli-label dataset, where each label, e.g. varroa_output, contains 1 if the characterisitic was present in the image and a 0 if it wasn't. This is the train_batch I tried to feed into the neural network:
<BatchDataset element_spec=(TensorSpec(shape=(None, 300, 150, 3), dtype=tf.uint8, name=None), {'cooling_output': TensorSpec(shape=(None,), dtype=tf.float64, name=None), 'pollen_output': TensorSpec(shape=(None,), dtype=tf.float64, name=None), 'varroa_output': TensorSpec(shape=(None,), dtype=tf.float64, name=None), 'wasps_output': TensorSpec(shape=(None,), dtype=tf.float64, name=None)})>
This is how I tried to train the model:
history = model.fit(train_batches,epochs=5,validation_data=validation_batches)
acc = history.history['accuracy']
print(acc)
And the following error occurs:
ValueError: Found unexpected losses or metrics that do not correspond to any Model output: dict_keys(['cooling_output', 'pollen_output', 'varroa_output', 'wasps_output']). Valid mode output names: ['dense_11']. Received struct is: {'cooling_output': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=float64>, 'pollen_output': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=float64>, 'varroa_output': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=float64>, 'wasps_output': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=float64>}.
I am sure there is an obvious explanation but I am an absolute beginner and would appreciate any help.