ValueError building a neural network with 2 outputs in Keras

Question

I tried to build a network having a single input X (a 2-dimensions matrix of size Xa*Xb) and 2 outputs Y1 and Y2 (both in 1 dimension). Even though it isn't the case in the code I posted below, Y1 is supposed to be a classifier that outputs a one-hot vector and Y2 is supposed to be for regression (the original code raised the same error).

When training the network I get the following error:

ValueError: Shapes (None, None) and (None, 17, 29) are incompatible

Obviously, (None, 17, 29) translates to (None, size_Xa, size_Y1) , and I don't understand why Xa and Y1 should be related (independantly from Xb) in the first place.

Here is my code. I tried to reduce it to the minimum in order to make it easier to understand.

import numpy as np
from keras.layers import Dense, LSTM, Input
from keras.models import Model

def dataGenerator():
    while True:
        yield makeBatch()
def makeBatch():
    """generates a batch of artificial training data"""
    x_batch, y_batch = [], {}
    x_batch = np.random.rand(batch_size, size_Xa, size_Xb)
    #x_batch = np.random.rand(batch_size, size_Xa)
    y_batch['output1'] = np.random.rand(batch_size, size_Y1)
    y_batch['output2'] = np.random.rand(batch_size, size_Y2)
    return x_batch, y_batch

def generate_model():
    input_layer = Input(shape=(size_Xa, size_Xb))
    #input_layer = Input(shape=(size_Xa))
    common_branch = Dense(128, activation='relu')(input_layer)
    branch_1  = Dense(size_Y1, activation='softmax', name='output1')(common_branch)
    branch_2  = Dense(size_Y2, activation='relu',    name='output2')(common_branch)
    model = Model(inputs=input_layer,outputs=[branch_1,branch_2])

    losses = {"output1":"categorical_crossentropy", "output2":"mean_absolute_error"}
    model.compile(optimizer="adam",
                        loss=losses,
                        metrics=['accuracy'])
    return model

batch_size=5
size_Xa = 17
size_Xb = 13
size_Y2 = 100 
size_Y1 = 29

model = generate_model()

model.fit(  x=dataGenerator(),
            steps_per_epoch=50,
            epochs=15,
            validation_data=dataGenerator(), validation_steps=50, verbose=1)

If I uncomment the 2 commented lines in makeBatch and generate_model, the error disappears. So if the input X is in 1 dimension it runs, but when I change it to 2 dimensions (keeping everything else the same) the error appears.

Is this related to the architecture with 2 outputs? I think there is something I'm missing here, any help is welcome.

I add the full error log for reference:

Epoch 1/15
Traceback (most recent call last):
  File "neuralnet_minimal.py", line 41, in <module>
    model.fit(  x=dataGenerator(),
  File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 66, in _method_wrapper
    return method(self, *args, **kwargs)
  File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 848, in fit
    tmp_logs = train_function(iterator)
  File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 580, in __call__
    result = self._call(*args, **kwds)
  File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 627, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 505, in _initialize
    self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
  File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2446, in _get_concrete_function_internal_garbage_collected
    graph_function, _, _ = self._maybe_define_function(args, kwargs)
  File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2777, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2657, in _create_graph_function
    func_graph_module.func_graph_from_py_func(
  File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 981, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 441, in wrapped_fn
    return weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 968, in wrapper
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:

    /path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:571 train_function  *
        outputs = self.distribute_strategy.run(
    /path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
        return fn(*args, **kwargs)
    /path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:532 train_step  **
        loss = self.compiled_loss(
    /path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/engine/compile_utils.py:205 __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    /path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:143 __call__
        losses = self.call(y_true, y_pred)
    /path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:246 call
        return self.fn(y_true, y_pred, **self._fn_kwargs)
    /path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/losses.py:1527 categorical_crossentropy
        return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
    /path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/keras/backend.py:4561 categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)
    /path/of/my/project/venv/lib/python3.8/site-packages/tensorflow/python/framework/tensor_shape.py:1117 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self, other))

    ValueError: Shapes (None, None) and (None, 17, 29) are incompatible

Answer 1

Strangely enough, the error disappears when I add a Flatten() layer before the network splits... It has to do with the shape of the network but I still don't get the real reason behind all of this.

I will mark this as correct answer as it solves the problem, unless someone else posts something. Please tell me if this is not the right way to do it.