Я пытаюсь классифицировать изображения печатных плат на две категории (defected и undefected), используя categorical cross-entropy в качестве функции потерь. Код для того же приведен ниже:
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)
И после выполнения этого я получаю ошибку ValueError, показанную ниже:
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
Я уже посмотрел на это , это и это , но не удалось устранить ошибку.
Я очень признателен за помощь в исправлении этого.
Спасибо, Правин
Вот полный трекбек ... ссылка