我有一个图像张量的元组，每个张量具有(256,256,3)的形状，并且在元组中有1000个。当我将其提供给tensorflow输入时，它表示我的形状错误-收到的形状为[None,256,3]。如何将其转换为TF满意的数据类型？当我尝试images = np.array(list(images))时，它会冻结（或花了我很长时间才放弃）。
我对Python / Numpy / Tensorflow还是很陌生，因此感谢您的反馈。谢谢！
这是我的代码的相关部分：
_root, dirs, files = next(os.walk(TRAINING_PATH))
def decode_img(img):
# convert the compressed string to a 3D uint8 tensor
img = tf.image.decode_png(img, channels=3)
# Use `convert_image_dtype` to convert to floats in the [0,1] range.
img = tf.image.convert_image_dtype(img, tf.float32)
# resize the image to the desired size.
return tf.image.resize(img, [IMG_HEIGHT,IMG_WIDTH])
# Returns a mask array where 0 is null, 1='A' ... 26='Z'
def processMasks(path):
_root, _dirs, files = next(os.walk(path))
combined_mask = np.full((IMG_HEIGHT, IMG_WIDTH,1), 0, dtype=np.uint8)
for mask_path in files:
mask = tf.keras.preprocessing.image.load_img(path+"\\"+mask_path, target_size=[IMG_HEIGHT,IMG_WIDTH],color_mode="grayscale")
mask = tf.keras.preprocessing.image.img_to_array(mask)
letter_index = mask_path.index('_')+1
mask_letter = mask_path[letter_index]
letter_code = ord(mask_letter)-ord('A') + 1
# Only mask where it is above middle gray
combined_mask = np.where(mask>=128,letter_code,combined_mask)
return combined_mask
def processTrain(folder, path = TRAINING_PATH):
img = decode_img(tf.io.read_file(path+folder+"\\"+RENDER_NAME))
mask = processMasks(path+folder+"\\"+MASKS_PATH)
mask = mask[:,:,0] # remove last dimension (single-value)
# as the mask uses 0 as empty, let's push that to 255 and move A->0, z->25 (etc.)
# This way, the one-hot will ignore 255 (anything >25)
mask -= 1
mask = tf.one_hot(mask,26)
return img, mask
dataset = [processTrain(path) for path in dirs]
# Unzip the tuples
images, masks = zip(*dataset)
############# Set up Model #################
mask_model = tf.keras.applications.MobileNetV2(input_shape=[256,256,3], classes=26, include_top=False)
mask_model.compile(loss="categorical_crossentropy",metrics=['accuracy'], optimizer='adam')
mask_model.fit(images,masks, batch_size=100,epochs=5)
我得到的错误是：
>>> mask_model.fit(images,masks, batch_size=100,epochs=5)
Epoch 1/5
WARNING:tensorflow:Model was constructed with shape (None, 256, 256, 3) for input Tensor("input_2:0", shape=(None, 256, 256, 3), dtype=float32), but it was called on an input with incompatible shape (None, 256, 3).
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\keras\engine\training.py", line 66, in _method_wrapper
return method(self, *args, **kwargs)
File "C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\keras\engine\training.py", line 848, in fit
tmp_logs = train_function(iterator)
File "C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\eager\def_function.py", line 580, in __call__
result = self._call(*args, **kwds)
File "C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\eager\def_function.py", line 627, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\eager\def_function.py", line 505, in _initialize
self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
File "C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\eager\function.py", line 2446, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\eager\function.py", line 2777, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\eager\function.py", line 2657, in _create_graph_function
func_graph_module.func_graph_from_py_func(
File "C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\framework\func_graph.py", line 981, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\eager\def_function.py", line 441, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\framework\func_graph.py", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\keras\engine\training.py:571 train_function *
outputs = self.distribute_strategy.run(
C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\keras\engine\training.py:531 train_step **
y_pred = self(x, training=True)
C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:927 __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\keras\engine\network.py:717 call
return self._run_internal_graph(
C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\keras\engine\network.py:888 _run_internal_graph
output_tensors = layer(computed_tensors, **kwargs)
C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:885 __call__
input_spec.assert_input_compatibility(self.input_spec, inputs,
C:\Data\Environments\python\machineLearning\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:176 assert_input_compatibility
raise ValueError('Input ' + str(input_index) + ' of layer ' +
ValueError: Input 0 of layer Conv1_pad is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [None, 256, 3]
答案 0 :(得分：0)
我通过在张量列表上使用tf.stack解决了这个问题