TensorFlow错误：ValueError（“形状％s和％s不兼容”％（自身，其他））

我正在尝试将PCB的图像分为损耗函数，分为两类（defected和undefected）categorical cross-entropy。相同的代码如下：

import numpy as np
import matplotlib.pyplot as plt

import tensorflow
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint


from keras.applications.resnet50 import preprocess_input
from keras.preprocessing.image import ImageDataGenerator

from sklearn.model_selection import train_test_split

def create_compiled_model():
  model = Sequential()
  model.add(ResNet50(include_top=False, weights=RESNET50_WEIGHTS, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3), pooling=RESNET50_POOLING_AVERAGE))
  model.add(Dense(NUM_CLASSES, activation=DENSE_LAYER_ACTIVATION))
  model.layers[0].trainable = False

  sgd = SGD(lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = True)
  model.compile(optimizer = sgd, loss = OBJECTIVE_FUNCTION, metrics = LOSS_METRICS)

  return model

def data_splitor():
  x = np.load("/content/data/xtrain.npy")
  y = np.load("/content/data/ytrain.npy")

  # Getting the Test and Train splits
  x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= TRAIN_TEST_SPLIT, shuffle= True)

  # Getting the Train and Validation splits
  x__train, x__valid, y__train, y__valid = train_test_split(x_train, y_train, test_size= TRAIN_TEST_SPLIT, shuffle= True)

  return x__train, x__valid, x_test, y__train, y__valid, y_test

def data_generator(x, y, batch_size, seed=None, shuffle=True):
  data_generator = ImageDataGenerator(horizontal_flip=True, vertical_flip=True, rotation_range=180, brightness_range=[0.3, 1.0], preprocessing_function=preprocess_input)
  generator = data_generator.flow(x_train, y_train, batch_size= batch_size, seed= seed, shuffle=shuffle)
  return generator

def run_program():
  x_train, x_valid, x_test, y_train, y_valid, y_test = data_splitor()
  train_generator = data_generator(x_train, y_train, BATCH_SIZE_TRAINING)
  validation_generator = data_generator(x_valid, y_valid, BATCH_SIZE_VALIDATION)

  cb_early_stopper = EarlyStopping(monitor = 'val_loss', patience = EARLY_STOP_PATIENCE)
  cb_checkpointer = ModelCheckpoint(filepath = '/content/model/best.hdf5', monitor = 'val_loss', save_best_only = True, mode = 'auto')

  model = create_compiled_model()

  fit_history = model.fit_generator(
        train_generator,
        steps_per_epoch=STEPS_PER_EPOCH_TRAINING,
        epochs = NUM_EPOCHS,
        validation_data=validation_generator,
        validation_steps=STEPS_PER_EPOCH_VALIDATION,
        callbacks=[cb_checkpointer, cb_early_stopper]
  )

  plt.figure(1, figsize = (15,8)) 

  plt.subplot(221)
  plt.plot(fit_history.history['acc'])  
  plt.plot(fit_history.history['val_acc'])  
  plt.title('model accuracy')  
  plt.ylabel('accuracy')  
  plt.xlabel('epoch')  
  plt.legend(['train', 'valid']) 

  plt.subplot(222)  
  plt.plot(fit_history.history['loss'])  
  plt.plot(fit_history.history['val_loss'])  
  plt.title('model loss')  
  plt.ylabel('loss')  
  plt.xlabel('epoch')  
  plt.legend(['train', 'valid']) 

  plt.show()


  # Testing
  test_generator = data_generator(x_test, y_test, BATCH_SIZE_TESTING, 123, False)
  test_generator.reset()

  model.load_weights("/content/model/best.hdf5")
  pred = model.predict_generator(test_generator, steps = len(test_generator), verbose = 1)

  predicted_class_indices = np.argmax(pred, axis = 1)


# Running the program
try:
  with tensorflow.device('/device:GPU:0'):
    run_program()
except RuntimeError as e:
  print(e)

执行此操作后，我得到的ValueError如下所示：

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:571 train_function  *
        outputs = self.distribute_strategy.run(
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:533 train_step  **
        y, y_pred, sample_weight, regularization_losses=self.losses)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:204 __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:143 __call__
        losses = self.call(y_true, y_pred)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:246 call
        return self.fn(y_true, y_pred, **self._fn_kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:1527 categorical_crossentropy
        return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:4561 categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py:1117 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self, other))

    ValueError: Shapes (None, 1) and (None, 2) are incompatible

我已经查看过this，this和this，但是无法解决错误。

我非常感谢您提供的修复此问题的帮助。

感谢Praveen

这是完整的追溯...链接