---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\framework\op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
    510                 as_ref=input_arg.is_ref,
--> 511                 preferred_dtype=default_dtype)
    512           except TypeError as err:

~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\framework\ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx, accept_symbolic_tensors)
   1174     if ret is None:
-> 1175       ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
   1176

~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\framework\ops.py in _TensorTensorConversionFunction(t, dtype, name, as_ref)
    976         "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" %
--> 977         (dtype.name, t.dtype.name, str(t)))
    978   return t

ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor("metrics_16/acc/Cast_6:0", shape=(?, 1), dtype=float32)'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-26-7ccbae3113af> in <module>
     58             model.compile(optimizer='adam',
     59                          loss='sparse_categorical_crossentropy',
---> 60                          metrics=['accuracy'])
     61             model.fit(X, y,
     62                       batch_size=32,

~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\training\checkpointable\base.py in _method_wrapper(self, *args, **kwargs)
    440     self._setattr_tracking = False  # pylint: disable=protected-access
    441     try:
--> 442       method(self, *args, **kwargs)
    443     finally:
    444       self._setattr_tracking = previous_value  # pylint: disable=protected-access

~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\keras\engine\training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, distribute, **kwargs)
    497           targets=self.targets,
    498           skip_target_indices=skip_target_indices,
--> 499           sample_weights=self.sample_weights)
    500
    501       # Prepare gradient updates and state updates.

~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\keras\engine\training.py in _handle_metrics(self, outputs, skip_target_indices, targets, sample_weights, masks, return_stateful_result)
   1842                 output,
   1843                 output_mask,
-> 1844                 return_stateful_result=return_stateful_result))
   1845         metric_results.extend(
   1846             self._handle_per_output_metrics(

~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\keras\engine\training.py in _handle_per_output_metrics(self, metrics_dict, y_true, y_pred, mask, weights, return_stateful_result)
   1799           # stateless fns.
   1800           stateful_metric_result = _call_stateful_fn(stateful_fn)
-> 1801           metric_result = _call_stateless_fn(metric_fn)
   1802           _track_metric_tensors(metric_name, metric_result,
   1803                                 stateful_metric_result)

~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\keras\engine\training.py in _call_stateless_fn(fn)
   1775         def _call_stateless_fn(fn):
   1776           weighted_metric_fn = training_utils.weighted_masked_objective(fn)
-> 1777           return weighted_metric_fn(y_true, y_pred, weights=weights, mask=mask)
   1778
   1779         def _track_metric_tensors(name, stateless_result, stateful_result):

~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\keras\engine\training_utils.py in weighted(y_true, y_pred, weights, mask)
    645     """
    646     # score_array has ndim >= 2
--> 647     score_array = fn(y_true, y_pred)
    648     if mask is not None:
    649       mask = math_ops.cast(mask, y_pred.dtype)

~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\keras\metrics.py in binary_accuracy(y_true, y_pred, threshold)
   1531   threshold = math_ops.cast(threshold, y_pred.dtype)
   1532   y_pred = math_ops.cast(y_pred > threshold, y_pred.dtype)
-> 1533   return K.mean(math_ops.equal(y_true, y_pred), axis=-1)
   1534
   1535

~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\ops\gen_math_ops.py in equal(x, y, name)
   3307   try:
   3308     _, _, _op = _op_def_lib._apply_op_helper(
-> 3309         "Equal", x=x, y=y, name=name)
   3310   except (TypeError, ValueError):
   3311     result = _dispatch.dispatch(

~\Anaconda3\envs\venv\lib\site-packages\tensorflow\python\framework\op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
    545                   "%s type %s of argument '%s'." %
    546                   (prefix, dtypes.as_dtype(attrs[input_arg.type_attr]).name,
--> 547                    inferred_from[input_arg.type_attr]))
    548
    549           types = [values.dtype]

TypeError: Input 'y' of 'Equal' Op has type float32 that does not match type int32 of argument 'x'.