InvalidArgumentError when I use model.fit() - python-3.x

Question

I am trying to build a custom layer in keras/tensorflow. The purpose would be to extend it as a RNN. The problem appears during training, where, apparently, there is an incompatibility with the output shape and the training dataset.
X_train.shape
(100, 5)
X_target.shape
(100, 5)
This is the custom layer:
class MyLayer(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(MyLayer, self).__init__(**kwargs)
def build(self, input_shape):
print(self.output_dim)
print(input_shape)
# Create a trainable weight variable for this layer.
self.kernel = self.add_weight(name='kernel',
shape=(self.output_dim, input_shape[1]),
initializer='uniform',
trainable=True)
super(MyLayer, self).build(input_shape) # Be sure to call this at the end
def call(self, x):
print('shape x: ')
print(x.shape)
print('shape kernel: ')
print(self.kernel.shape)
matrix = tf.transpose(self.kernel)
print('matrix')
print(matrix.shape)
prod = K.dot(x, matrix)
print('after product')
print(prod.shape)
return prod
def compute_output_shape(self, input_shape):
print('Compute output shape')
print(input_shape)
print(self.output_dim)
return (input_shape[0], self.output_dim)
model = Sequential()
model.add(MyLayer(5, batch_input_shape=(100, 5)))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X_train, X_target)
tf.keras.__version__ = '2.1.6-tf'
Since I am passing a x_training with a shape of (100,5), I would expect that, by multiplying it by a matrix of (5,5), I would obtain a matrix of (100, 5), which is the same shape as the target and thus, I would be able to train, in this case, a 5x5 weight matrix. Instead, I get this:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-77-4dee23ead957> in <module>()
6 model.compile(optimizer='adam', loss='mse')
7 # fit model
----> 8 model.fit(X_train, X_target)#, epochs=300, verbose=0)
~/anaconda3/envs/ldsa/lib/python3.5/site-packages/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, **kwargs)
1037 initial_epoch=initial_epoch,
1038 steps_per_epoch=steps_per_epoch,
-> 1039 validation_steps=validation_steps)
1040
1041 def evaluate(self, x=None, y=None,
~/anaconda3/envs/ldsa/lib/python3.5/site-packages/keras/engine/training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
197 ins_batch[i] = ins_batch[i].toarray()
198
--> 199 outs = f(ins_batch)
200 outs = to_list(outs)
201 for l, o in zip(out_labels, outs):
~/anaconda3/envs/ldsa/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
2713 return self._legacy_call(inputs)
2714
-> 2715 return self._call(inputs)
2716 else:
2717 if py_any(is_tensor(x) for x in inputs):
~/anaconda3/envs/ldsa/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py in _call(self, inputs)
2673 fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
2674 else:
-> 2675 fetched = self._callable_fn(*array_vals)
2676 return fetched[:len(self.outputs)]
2677
~/anaconda3/envs/ldsa/lib/python3.5/site-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
1380 ret = tf_session.TF_SessionRunCallable(
1381 self._session._session, self._handle, args, status,
-> 1382 run_metadata_ptr)
1383 if run_metadata:
1384 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~/anaconda3/envs/ldsa/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
517 None, None,
518 compat.as_text(c_api.TF_Message(self.status.status)),
--> 519 c_api.TF_GetCode(self.status.status))
520 # Delete the underlying status object from memory otherwise it stays alive
521 # as there is a reference to status from this from the traceback due to
InvalidArgumentError: Incompatible shapes: [100,5] vs. [32,5]
[[Node: training_12/Adam/gradients/loss_17/my_layer_19_loss/sub_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _class=["loc:#training_12/Adam/gradients/loss_17/my_layer_19_loss/sub_grad/Reshape"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](training_12/Adam/gradients/loss_17/my_layer_19_loss/sub_grad/Shape, training_12/Adam/gradients/loss_17/my_layer_19_loss/sub_grad/Shape_1)]]
My surprise comes from this: Incompatible shapes: [100,5] vs. [32,5], where does this 32 come from?