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 = 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'

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)]]