머신러닝 문제
조회수 999회
마지막 실행을 위한 model.fit 전까지는 오류가 안났는 데 실행 시키자 마자 오류가 떴습니다.
처리 과정 중에 문제가 있었던 것 같은 데 알려주시면 감사하겠습니다.
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import datasets
(train_x, train_y), (test_x, test_y) = datasets.mnist.load_data()
input = layers.Input([28,28,1])
net = layers.Conv2D(32,3,1,'SAME',activation = 'relu')(input)
net = layers.Conv2D(32,3,1,'SAME',activation = 'relu')(net)
net = layers.MaxPooling2D(pool_size = (2,2))(net)
net = layers.Dropout(0.25)(net)
net = layers.Conv2D(64, (3, 3), padding='SAME',activation = 'relu')(net)
net = layers.Conv2D(64, (3, 3), padding='SAME',activation = 'relu')(net)
net = layers.MaxPooling2D(pool_size=(2, 2))(net)
net = layers.Dropout(0.25)(net)
net = layers.Flatten()(net)
net = layers.Dense(512,activation = 'relu')(net)
net = layers.Dropout(0.5)(net)
net = layers.Dense(10,activation = 'softmax')(net) # num_classes
model = tf.keras.Model(inputs=input, outputs=net, name='Basic_CNN')
###위와 같이 모델을 만들었다
model.compile(loss = 'sparse_categorical_crossentropy',
optimizer = tf.keras.optimizers.Adam(),
metrics = [tf.keras.metrics.Accuracy()])
###위를 통해서 이제 모델은 다 만들었다 이제 데이터를 집어넣자
train_x.shape,train_y.shape
import numpy as np
train_x = train_x[...,tf.newaxis]
test_x = test_x[...,tf.newaxis]
# 자료가 커서 나눔으로써 자료를 줄였다
train_x = train_x/255
test_x = test_x/255
### 데이터도 다 만들었으니 이제 학습에
model.fit(train_x,train_y, batch_size=32, epochs=10,shuffle = True)
이까지가 제 코드입니다.
이거에 대한 오류메세지 입니다
ValueError Traceback (most recent call last)
<ipython-input-1-369acf19b763> in <module>
49 # 한번에 학습할 때 사용하는 데이터 갯수 batch_size
50
---> 51 model.fit(train_x,train_y, batch_size=32, epochs=10,shuffle = True)
~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in _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)
109
110 # Running inside `run_distribute_coordinator` already.
~\anaconda3\lib\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)
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()
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
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
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
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))
698
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _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
2857
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3211
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
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
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,
~\anaconda3\lib\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)
984 _, original_func = tf_decorator.unwrap(python_func)
985
--> 986 func_outputs = python_func(*func_args, **func_kwargs)
987
988 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
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)
601 weak_wrapped_fn = weakref.ref(wrapped_fn)
602
~\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in 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:\Users\a0108\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self, iterator)
C:\Users\a0108\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\a0108\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:\Users\a0108\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:\Users\a0108\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\a0108\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step **
outputs = model.train_step(data)
C:\Users\a0108\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:759 train_step
self.compiled_metrics.update_state(y, y_pred, sample_weight)
C:\Users\a0108\anaconda3\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:409 update_state
metric_obj.update_state(y_t, y_p, sample_weight=mask)
C:\Users\a0108\anaconda3\lib\site-packages\tensorflow\python\keras\utils\metrics_utils.py:90 decorated
update_op = update_state_fn(*args, **kwargs)
C:\Users\a0108\anaconda3\lib\site-packages\tensorflow\python\keras\metrics.py:176 update_state_fn
return ag_update_state(*args, **kwargs)
C:\Users\a0108\anaconda3\lib\site-packages\tensorflow\python\keras\metrics.py:612 update_state **
matches = ag_fn(y_true, y_pred, **self._fn_kwargs)
C:\Users\a0108\anaconda3\lib\site-packages\tensorflow\python\keras\metrics.py:3208 accuracy **
y_pred.shape.assert_is_compatible_with(y_true.shape)
C:\Users\a0108\anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1134 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (32, 10) and (32, 1) are incompatible
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