Hello,
I need some guidance/help to run the notebook on a local system.
I am running into a ValueError message on the Convolutional Neural Network cell of the provided Week 3 Lab 1 notebook. The notebook is ran on a local machine with Anaconda installed - tensorflow version 2.3.0. Initial run has no code changes as is and shows the following error message:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [3], in <cell line: 24>()
22 # Train the model
23 print(f'\nMODEL TRAINING:')
---> 24 model.fit(training_images, training_labels, epochs=5)
26 # Evaluate on the test set
27 print(f'\nMODEL EVALUATION:')
File C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:108, in enable_multi_worker.<locals>._method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
110 # Running inside `run_distribute_coordinator` already.
111 if dc_context.get_current_worker_context():
File C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:1098, in Model.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)
1091 with trace.Trace(
1092 'TraceContext',
1093 graph_type='train',
1094 epoch_num=epoch,
1095 step_num=step,
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
File C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py:780, in Function.__call__(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
782 new_tracing_count = self._get_tracing_count()
783 without_tracing = (tracing_count == new_tracing_count)
File C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py:823, in Function._call(self, *args, **kwds)
820 try:
821 # This is the first call of __call__, so we have to initialize.
822 initializers = []
--> 823 self._initialize(args, kwds, add_initializers_to=initializers)
824 finally:
825 # At this point we know that the initialization is complete (or less
826 # interestingly an exception was raised) so we no longer need a lock.
827 self._lock.release()
File C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py:696, in Function._initialize(self, args, kwds, add_initializers_to)
693 self._lifted_initializer_graph = lifted_initializer_graph
694 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695 self._concrete_stateful_fn = (
--> 696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
697 *args, **kwds))
699 def invalid_creator_scope(*unused_args, **unused_kwds):
700 """Disables variable creation."""
File C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\eager\function.py:2855, in Function._get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853 args, kwargs = None, None
2854 with self._lock:
-> 2855 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856 return graph_function
File C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\eager\function.py:3213, in Function._maybe_define_function(self, args, kwargs)
3210 return self._define_function_with_shape_relaxation(args, kwargs)
3212 self._function_cache.missed.add(call_context_key)
-> 3213 graph_function = self._create_graph_function(args, kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function, args, kwargs
File C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\eager\function.py:3065, in Function._create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3060 missing_arg_names = [
3061 "%s_%d" % (arg, i) for i, arg in enumerate(missing_arg_names)
3062 ]
3063 arg_names = base_arg_names + missing_arg_names
3064 graph_function = ConcreteFunction(
-> 3065 func_graph_module.func_graph_from_py_func(
3066 self._name,
3067 self._python_function,
3068 args,
3069 kwargs,
3070 self.input_signature,
3071 autograph=self._autograph,
3072 autograph_options=self._autograph_options,
3073 arg_names=arg_names,
3074 override_flat_arg_shapes=override_flat_arg_shapes,
3075 capture_by_value=self._capture_by_value),
3076 self._function_attributes,
3077 function_spec=self.function_spec,
3078 # Tell the ConcreteFunction to clean up its graph once it goes out of
3079 # scope. This is not the default behavior since it gets used in some
3080 # places (like Keras) where the FuncGraph lives longer than the
3081 # ConcreteFunction.
3082 shared_func_graph=False)
3083 return graph_function
File C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py:986, 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)
983 else:
984 _, original_func = tf_decorator.unwrap(python_func)
--> 986 func_outputs = python_func(*func_args, **func_kwargs)
988 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
989 # TensorArrays and `None`s.
990 func_outputs = nest.map_structure(convert, func_outputs,
991 expand_composites=True)
File C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py:600, in Function._defun_with_scope.<locals>.wrapped_fn(*args, **kwds)
585 # We register a variable creator with reduced priority. If an outer
586 # variable creator is just modifying keyword arguments to the variable
587 # constructor, this will work harmoniously. Since the `scope` registered
(...)
595 # better than the alternative, tracing the initialization graph but giving
596 # the user a variable type they didn't want.
597 with ops.get_default_graph()._variable_creator_scope(scope, priority=50): # pylint: disable=protected-access
598 # __wrapped__ allows AutoGraph to swap in a converted function. We give
599 # the function a weak reference to itself to avoid a reference cycle.
--> 600 return weak_wrapped_fn().__wrapped__(*args, **kwds)
File C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py:973, in func_graph_from_py_func.<locals>.wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code:
C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self, iterator)
C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step **
outputs = model.train_step(data)
C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:747 train_step
y_pred = self(x, training=True)
C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:975 __call__
input_spec.assert_input_compatibility(self.input_spec, inputs,
C:\anaconda\anaconda3\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:191 assert_input_compatibility
raise ValueError('Input ' + str(input_index) + ' of layer ' +
ValueError: Input 0 of layer sequential_1 is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: [32, 28, 28]
Does the error indicate that the training_images shape needs to be different from 60000x28x28 … even though it was explicitly asked to see the images as 28x28x1 on the Conv2D line via the Convolutional Neural Network cell?
Adding the following code (below) gets me to run the cell further but still fails after the 5 epoch on sequential_1 or when sequential_2 begins, which still provides the same ValueError message.
training_images = training_images.reshape(-1, 28, 28, 1)
Thank you.