我正在做一个深度学习项目，根据胸部 X 光图像检测一个人是否患有结核病。阅读并预处理我的数据后，我在构建 CNN 模型时遇到了问题。
import tensorflow as tf
from tensorflow.keras import layers, models
input_shape = (32, 256, 256, 3)
model = models.Sequential([
resize_and_rescale,
layers.Conv2D(32, (3,3), activation='relu', input_shape=input_shape),
layers.MaxPooling2D((2,2)),
layers.Conv2D(64, (3,3), activation='relu'),
layers.MaxPooling2D((2,2)),
layers.Conv2D(64, (3,3), activation='relu'),
layers.MaxPooling2D((2,2)),
layers.Flatten(),
layers.Dense(100, activation='relu'),
layers.Dense(50, activation='relu'),
layers.Dense(2, activation='softmax')
])
model.build(input_shape=input_shape)
model.summary()
输出：
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
sequential (Sequential) (32, 256, 256, 3) 0
conv2d (Conv2D) (32, 254, 254, 32) 896
max_pooling2d (MaxPooling2 (32, 127, 127, 32) 0
D)
conv2d_1 (Conv2D) (32, 125, 125, 64) 18496
max_pooling2d_1 (MaxPoolin (32, 62, 62, 64) 0
g2D)
conv2d_2 (Conv2D) (32, 60, 60, 64) 36928
max_pooling2d_2 (MaxPoolin (32, 30, 30, 64) 0
g2D)
flatten (Flatten) (32, 57600) 0
dense (Dense) (32, 100) 5760100
dense_1 (Dense) (32, 50) 5050
dense_2 (Dense) (32, 2) 102
=================================================================
Total params: 5821572 (22.21 MB)
Trainable params: 5821572 (22.21 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accurary'])
model.fit(train_ds, batch_size=32, epochs=15, verbose=1, validation_data=val_ds)
然后弹出这个错误：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[16], line 1
----> 1 model.fit(train_ds, batch_size=32, epochs=15, verbose=1, validation_data=val_ds)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File ~\AppData\Local\Temp\__autograph_generated_file_o3ecbkc.py:15, in outer_factory.<locals>.inner_factory.<locals>.tf__train_function(iterator)
13 try:
14 do_return = True
---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
16 except:
17 do_return = False
TypeError: in user code:
File "C:\Users\tangb\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\training.py", line 1338, in train_function *
return step_function(self, iterator)
File "C:\Users\tangb\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\training.py", line 1322, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\tangb\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\training.py", line 1303, in run_step **
outputs = model.train_step(data)
File "C:\Users\tangb\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\training.py", line 1085, in train_step
return self.compute_metrics(x, y, y_pred, sample_weight)
File "C:\Users\tangb\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\training.py", line 1179, in compute_metrics
self.compiled_metrics.update_state(y, y_pred, sample_weight)
File "C:\Users\tangb\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\engine\compile_utils.py", line 605, in update_state
metric_obj.update_state(y_t, y_p, sample_weight=mask)
File "C:\Users\tangb\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\metrics_utils.py", line 77, in decorated
update_op = update_state_fn(*args, **kwargs)
File "C:\Users\tangb\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\metrics\base_metric.py", line 140, in update_state_fn
return ag_update_state(*args, **kwargs)
File "C:\Users\tangb\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\metrics\base_metric.py", line 723, in update_state **
matches = ag_fn(y_true, y_pred, **self._fn_kwargs)
TypeError: 'str' object is not callable
这是我的训练数据集和验证数据集：
type(train_ds) # type(val_ds) gives the same output
输出：
tensorflow.python.data.ops.prefetch_op._PrefetchDataset
我访问了这个网站：https://www.freecodecamp.org/news/typeerror-str-object-is-not-callable-how-to-fix-in-python/了解原因，但我可以'还没弄清楚。我真的很感谢任何帮助，谢谢。
这只是您代码中的一个拼写错误。在你的
model.compile(...accuracyaccurary更正如下：
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])