您好，我遇到了这个错误，无法解决任何想法。我正在尝试使用自己的数据集构建模型。因此，我选择了转移学习（VGG16），但仍然无法正常工作。先感谢您。 我正在使用Python 3.8X 最新版本的Tensorflow 2.2X 我试图建立一个可以dd
的分类器import tensorflow as tf
from tensorflow.keras.layers import Input, Lambda, Dense, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
import numpy as np
from glob import glob
import matplotlib.pyplot as plt
IMAGE_SIZE = [224, 224]
train_path = 'dataset/Train'
val_path = 'dataset/validation'
vgg = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
for layer in vgg.layers:
layer.trainable = False
folders = glob('datasets/Train/*')
x = Flatten()(vgg.output)
x = Dense(1000, activation='relu')(x)
prediction = Dense(len(folders), activation='softmax')(x)
# create a model object
model = Model(inputs=vgg.input, outputs=prediction)
model.summary()
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('dataset/train',
target_size = (224, 224),
batch_size = 32,
class_mode = 'categorical')
test_set = test_datagen.flow_from_directory('dataset/validation',
target_size = (224, 224),
batch_size = 32,
class_mode = 'categorical')
r = model.fit(
training_set,
validation_data=test_set,
epochs=5,
steps_per_epoch=len(training_set),
validation_steps=len(test_set)
)
下面是错误
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-46-a479a62b157d> in <module>
2 steps_per_epoch = 1,
3 epochs = 10,
----> 4 validation_data = test_set
5 )
~/opt/anaconda3/lib/python3.7/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.
~/opt/anaconda3/lib/python3.7/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)
1100 _r=1):
1101 callbacks.on_train_batch_begin(step)
-> 1102 tmp_logs = self.train_function(iterator)
1103 if data_handler.should_sync:
1104 context.async_wait()
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
794 else:
795 compiler = "nonXla"
--> 796 result = self._call(*args, **kwds)
797
798 new_tracing_count = self._get_tracing_count()
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821 # In this case we have created variables on the first call, so we run the
822 # defunned version which is guaranteed to never create variables.
--> 823 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
824 elif self._stateful_fn is not None:
825 # Release the lock early so that multiple threads can perform the call
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
2920 with self._lock:
2921 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2922 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2923
2924 @property
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _filtered_call(self, args, kwargs, cancellation_manager)
1856 resource_variable_ops.BaseResourceVariable))],
1857 captured_inputs=self.captured_inputs,
-> 1858 cancellation_manager=cancellation_manager)
1859
1860 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1932 # No tape is watching; skip to running the function.
1933 return self._build_call_outputs(self._inference_function.call(
-> 1934 ctx, args, cancellation_manager=cancellation_manager))
1935 forward_backward = self._select_forward_and_backward_functions(
1936 args,
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
555 inputs=args,
556 attrs=attrs,
--> 557 ctx=ctx)
558 else:
559 outputs = execute.execute_with_cancellation(
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: Reduction axis -1 is empty in shape [32,0]
[[node ArgMax_1 (defined at <ipython-input-45-71c422cbdbf7>:4) ]] [Op:__inference_train_function_3410]
Function call stack:
train_function
[ ]: