error during running 03_03_vae_digits_train.ipynb
krv76 opened this issue · comments
I've got the following output in cell containing
vae.train(
x_train[:1000]
, batch_size = BATCH_SIZE
, epochs = EPOCHS
, run_folder = RUN_FOLDER
# , print_every_n_batches = PRINT_EVERY_N_BATCHES
, initial_epoch = INITIAL_EPOCH
)
Could you help me with this?
(03_01_autoencoder_train.ipynb works fine; replacing VariationalAutoencoder with models.AE.Autoencoder also helps but I still can't pass through most of book examples; output is pretty big, last line looks the most informative but I can't figure out how to repair this notebook).
TypeError Traceback (most recent call last)
in ()
5 , run_folder = RUN_FOLDER
6 # , print_every_n_batches = PRINT_EVERY_N_BATCHES
----> 7 , initial_epoch = INITIAL_EPOCH
8 )
d:\jupyter\GDL_code\models\VAE.py in train(self, x_train, batch_size, epochs, run_folder, print_every_n_batches, initial_epoch, lr_decay)
200 , epochs = epochs
201 , initial_epoch = initial_epoch
--> 202 , callbacks = callbacks_list
203 )
204
~\AppData\Roaming\Python\Python36\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()
~\AppData\Roaming\Python\Python36\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()
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
869 # This is the first call of call, so we have to initialize.
870 initializers = []
--> 871 self._initialize(args, kwds, add_initializers_to=initializers)
872 finally:
873 # At this point we know that the initialization is complete (or less
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
724 self._concrete_stateful_fn = (
725 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 726 *args, **kwds))
727
728 def invalid_creator_scope(*unused_args, **unused_kwds):
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2967 args, kwargs = None, None
2968 with self._lock:
-> 2969 graph_function, _ = self._maybe_define_function(args, kwargs)
2970 return graph_function
2971
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3359
3360 self._function_cache.missed.add(call_context_key)
-> 3361 graph_function = self._create_graph_function(args, kwargs)
3362 self._function_cache.primary[cache_key] = graph_function
3363
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3204 arg_names=arg_names,
3205 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3206 capture_by_value=self._capture_by_value),
3207 self._function_attributes,
3208 function_spec=self.function_spec,
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
988 _, original_func = tf_decorator.unwrap(python_func)
989
--> 990 func_outputs = python_func(*func_args, **func_kwargs)
991
992 # invariant: func_outputs contains only Tensors, CompositeTensors,
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
632 xla_context.Exit()
633 else:
--> 634 out = weak_wrapped_fn().wrapped(*args, **kwds)
635 return out
636
~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
TypeError: in user code:
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\training.py:805 train_function *
return step_function(self, iterator)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\training.py:795 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\training.py:788 run_step **
outputs = model.train_step(data)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\training.py:756 train_step
y, y_pred, sample_weight, regularization_losses=self.losses)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\compile_utils.py:238 __call__
total_loss_metric_value, sample_weight=batch_dim)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\utils\metrics_utils.py:90 decorated
update_op = update_state_fn(*args, **kwargs)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\metrics.py:177 update_state_fn
return ag_update_state(*args, **kwargs)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\metrics.py:364 update_state **
sample_weight, values)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\weights_broadcast_ops.py:155 broadcast_weights
values = ops.convert_to_tensor(values, name="values")
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\profiler\trace.py:163 wrapped
return func(*args, **kwargs)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py:1540 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\constant_op.py:339 _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\constant_op.py:265 constant
allow_broadcast=True)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\constant_op.py:283 _constant_impl
allow_broadcast=allow_broadcast))
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\tensor_util.py:435 make_tensor_proto
values = np.asarray(values)
C:\Users\user\Anaconda3\lib\site-packages\numpy\core\_asarray.py:83 asarray
return array(a, dtype, copy=False, order=order)
C:\Users\user\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\keras_tensor.py:274 __array__
'Cannot convert a symbolic Keras input/output to a numpy array. '
TypeError: Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model.
