InvalidArgumentError on a mixed CNN - tensorflow

Question

I'm very new to Tensorflow/Keras and deep learning, so my apologies in advance.
I'm creating a basic mixed convolutional neural net to classify images and metadata. I've created the following using the Keras Functional API:
# Define inputs
meta_inputs = tf.keras.Input(shape=(2065,))
img_inputs = tf.keras.Input(shape=(80,120,3,))
# Model 1
meta_layer1 = tf.keras.layers.Dense(64, activation='relu')(meta_inputs)
meta_output_layer = tf.keras.layers.Dense(1, activation='sigmoid')(meta_layer1)
# Model 2
img_conv_layer1 = tf.keras.layers.Conv2D(32, kernel_size=(3,3), padding='same', activation='relu')(img_inputs)
img_pooling_layer1 = tf.keras.layers.MaxPooling2D()(img_conv_layer1)
img_conv_layer2 = tf.keras.layers.Conv2D(64, kernel_size=(3,3), padding='same', activation='relu')(img_pooling_layer1)
img_pooling_layer2 = tf.keras.layers.MaxPooling2D()(img_conv_layer2)
img_flatten_layer = tf.keras.layers.Flatten()(img_pooling_layer2)
img_dense_layer = tf.keras.layers.Dense(1024, activation='relu')(img_flatten_layer)
img_output_layer = tf.keras.layers.Dense(1, activation='sigmoid')(img_dense_layer)
# Merge models
merged = tf.keras.layers.add([meta_output_layer, img_output_layer])
# Define functional model
model = tf.keras.Model(inputs=[meta_inputs, img_inputs], outputs=merged)
# Compile model
auc = tf.keras.metrics.AUC(name = 'auc')
model.compile('adam', loss='binary_crossentropy', metrics=[auc])
I then proceed to fit the model:
epochs = 15
history = model.fit([meta_train, img_train], y_train, epochs=epochs, batch_size=500, validation_data=([meta_test, img_test], y_test))
This produces an error, and I'm quite frankly not sure what to do with it:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-11-5ec0cf9ac1d1> in <module>
1 epochs = 15
----> 2 history = model.fit([meta_train, img_train], y_train, epochs=epochs, batch_size=500, validation_data=([meta_test, img_test], y_test))
~/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1098 _r=1):
1099 callbacks.on_train_batch_begin(step)
-> 1100 tmp_logs = self.train_function(iterator)
1101 if data_handler.should_sync:
1102 context.async_wait()
~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
886 # Lifting succeeded, so variables are initialized and we can run the
887 # stateless function.
--> 888 return self._stateless_fn(*args, **kwds)
889 else:
890 _, _, _, filtered_flat_args = \
~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
2940 (graph_function,
2941 filtered_flat_args) = self._maybe_define_function(args, kwargs)
-> 2942 return graph_function._call_flat(
2943 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
2944
~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1916 and executing_eagerly):
1917 # No tape is watching; skip to running the function.
-> 1918 return self._build_call_outputs(self._inference_function.call(
1919 ctx, args, cancellation_manager=cancellation_manager))
1920 forward_backward = self._select_forward_and_backward_functions(
~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
553 with _InterpolateFunctionError(self):
554 if cancellation_manager is None:
--> 555 outputs = execute.execute(
556 str(self.signature.name),
557 num_outputs=self._num_outputs,
~/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
57 try:
58 ctx.ensure_initialized()
---> 59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
InvalidArgumentError: assertion failed: [predictions must be <= 1] [Condition x <= y did not hold element-wise:] [x (model/add/add:0) = ] [[1.47704351][1.48876262][1.50816929]...] [y (Cast_4/x:0) = ] [1]
[[{{node assert_less_equal/Assert/AssertGuard/else/_11/assert_less_equal/Assert/AssertGuard/Assert}}]] [Op:__inference_train_function_1331]
Function call stack:
train_function
Being new to Tensorflow, Keras, and deep learning in general, I'm not even sure where to begin diagnosing the issue. I don't know what [predictions must be <= 1] [Condition x <= y did not hold element-wise:] is referring to, for example: y_train is an array of 1s and 0s, and should hold to that assertion. I've even tried reshaping it to a (N,1)-D array, to no effect.
Can anyone point me in the right direction on this?

Answer 1

AUC metrics need probabilities in [0,1].
In your model, this not happen due to the sum you do in merged layer. You can solve for example using an average instead of a sum:
merged = tf.keras.layers.Average()([meta_output_layer, img_output_layer])