在运行以下代码时
model = load_model('unet_brain_mri_seg.hdf5', custom_objects={'dice_coef_loss': dice_coef_loss, 'iou': iou, 'dice_coef': dice_coef})
test_gen = train_generator(df_test, BATCH_SIZE,
dict(),
target_size=(im_height, im_width))
results = model.evaluate(test_gen, steps=len(df_test) / BATCH_SIZE)
print("Test lost: ",results[0])
print("Test IOU: ",results[1])
print("Test Dice Coefficent: ",results[2])报错
TypeError Traceback (most recent call last)
<ipython-input-23-9684583ed357> in <module>()
2 dict(),
3 target_size=(im_height, im_width))
----> 4 results = model.evaluate(test_gen, steps=len(df_test) / BATCH_SIZE)
5 print("Test lost: ",results[0])
6 print("Test IOU: ",results[1])
9 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
TypeError: in user code:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1233 test_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1224 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1219 run_step **
with ops.control_dependencies(_minimum_control_deps(outputs)):
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:2793 _minimum_control_deps
outputs = nest.flatten(outputs, expand_composites=True)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py:341 flatten
return _pywrap_utils.Flatten(structure, expand_composites)
TypeError: '<' not supported between instances of 'function' and 'str'
但是在model.evaluate之前运行model.fit没有报错，具体代码如下
EPOCHS = 150
BATCH_SIZE = 32
learning_rate = 1e-4
train_generator_args = dict(rotation_range=0.2,
width_shift_range=0.05,
height_shift_range=0.05,
shear_range=0.05,
zoom_range=0.05,
horizontal_flip=True,
fill_mode='nearest')
train_gen = train_generator(df_train, BATCH_SIZE,
train_generator_args,
target_size=(im_height, im_width))
test_gener = train_generator(df_val, BATCH_SIZE,
dict(),
target_size=(im_height, im_width))
model = unet(input_size=(im_height, im_width, 3))
decay_rate = learning_rate / EPOCHS
opt = Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=decay_rate, amsgrad=False)
model.compile(optimizer=opt, loss=dice_coef_loss, metrics=["binary_accuracy", iou, dice_coef])
callbacks = [ModelCheckpoint('unet_brain_mri_seg.hdf5', verbose=1, save_best_only=True)]
history = model.fit(train_gen,
steps_per_epoch=len(df_train) / BATCH_SIZE,
epochs=EPOCHS,
callbacks=callbacks,
validation_data = test_gener,
validation_steps=len(df_val) / BATCH_SIZE)
数据类型错误，检查一下BATCH_SIZE是不是int类型，如果是的话，再使用steps= int(len(df_test) / BATCH_SIZE),转换数据类型试试看。
这个TypeError: '<' not supported between instances of 'function' and 'str'是数据类型错误，function是函数方法，str是字符串
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更改版本号也可解决该问题。之前的TensorFlow版本号为2.4.1, keras版本号为2.4.3, 更改为以下版本号
pip install --upgrade tensorflow==2.2.0
pip install --upgrade keras==2.3.0
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