我对Tensorflow非常陌生，因此我不确定你需要什么来解决我的问题。因此，如果您需要任何其他信息，请告诉我。
基本上，我正在尝试通过Sequential运行图像。基于https://www.tensorflow.org/tutorials/images/classification上的教程，我正在尝试插入和播放到我自己的数据集。
我目前被困在运行模型使用模型。fit()，它给了我以下错误:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-90-85c03bda7f8f> in <module>
16
17 epochs=1
---> 18 history = model.fit(
19 train_data,
20 validation_data=test_data,
~/opt/anaconda3/lib/python3.8/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)
1132 _r=1):
1133 callbacks.on_train_batch_begin(step)
-> 1134 tmp_logs = self.train_function(iterator)
1135 if data_handler.should_sync:
1136 context.async_wait()
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
816 tracing_count = self.experimental_get_tracing_count()
817 with trace.Trace(self._name) as tm:
--> 818 result = self._call(*args, **kwds)
819 compiler = "xla" if self._jit_compile else "nonXla"
820 new_tracing_count = self.experimental_get_tracing_count()
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
860 # This is the first call of __call__, so we have to initialize.
861 initializers = []
--> 862 self._initialize(args, kwds, add_initializers_to=initializers)
863 finally:
864 # At this point we know that the initialization is complete (or less
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
701 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
702 self._concrete_stateful_fn = (
--> 703 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
704 *args, **kwds))
705
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3018 args, kwargs = None, None
3019 with self._lock:
-> 3020 graph_function, _ = self._maybe_define_function(args, kwargs)
3021 return graph_function
3022
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3412
3413 self._function_cache.missed.add(call_context_key)
-> 3414 graph_function = self._create_graph_function(args, kwargs)
3415 self._function_cache.primary[cache_key] = graph_function
3416
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3247 arg_names = base_arg_names + missing_arg_names
3248 graph_function = ConcreteFunction(
-> 3249 func_graph_module.func_graph_from_py_func(
3250 self._name,
3251 self._python_function,
~/opt/anaconda3/lib/python3.8/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)
996 _, original_func = tf_decorator.unwrap(python_func)
997
--> 998 func_outputs = python_func(*func_args, **func_kwargs)
999
1000 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
610 xla_context.Exit()
611 else:
--> 612 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
613 return out
614
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
983 except Exception as e: # pylint:disable=broad-except
984 if hasattr(e, "ag_error_metadata"):
--> 985 raise e.ag_error_metadata.to_exception(e)
986 else:
987 raise
ValueError: in user code:
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:839 train_function *
return step_function(self, iterator)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:829 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1262 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2734 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3423 _call_for_each_replica
return fn(*args, **kwargs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:822 run_step **
outputs = model.train_step(data)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:788 train_step
y_pred = self(x, training=True)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:1032 __call__
outputs = call_fn(inputs, *args, **kwargs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py:398 call
outputs = layer(inputs, **kwargs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:1028 __call__
self._maybe_build(inputs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:2722 _maybe_build
self.build(input_shapes) # pylint:disable=not-callable
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/layers/convolutional.py:188 build
input_channel = self._get_input_channel(input_shape)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/layers/convolutional.py:367 _get_input_channel
raise ValueError('The channel dimension of the inputs '
ValueError: The channel dimension of the inputs should be defined. Found `None`.
下面是我的模型代码:
model = Sequential([
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(4)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
epochs=10
history = model.fit(
train_data,
validation_data=test_data,
epochs=epochs
)
我知道在教程中他们使用了一个内置的预处理函数，但是我试图构建我自己的预处理函数来促进我的学习。
def preprocessing(image, target_size):
# Extracting labels
parts = tf.strings.split(image, os.sep)
label = parts[-2]
# Decoding image file
path = tf.io.read_file(image)
image = tf.image.decode_jpeg(path)
# Cropping
image = tf.image.crop_to_bounding_box(image, offset_height=25, offset_width=25, target_height=image_size, target_width=image_size)
# Normalizing
image = image / 255
return image, label
list_ds = tf.data.Dataset.list_files(DATA_DIR + '/*/*')
preprocess_function = partial(preprocessing, target_size=image_size)
processed_data = list_ds.map(preprocess_function)
train_data = processed_data.take(8000).batch(batch_size)
test_data = processed_data.skip(8000).batch(batch_size)
我可以提供的其他信息是图像是灰度的，因此有一个通道，我在预处理函数中将其归一化为/255,image_size为300,batch_size为100。
试试这个:
image = tf.image.decode_jpeg(path, channels=1)