У меня есть эта ошибка в моем коде:
esnet50_weights_tf_dim_ordering_tf_kernels_notop.h5 ', errno = 22, сообщение об ошибке =' Неверный аргумент ', flags = 0, o_flags = 0)
Я пытаюсь запустить двоичную классификацию здесь.
Пожалуйста, дайте мне знать, как вы исправляете это, если у вас была такая же ошибка.
Спасибо
NUM_CLASSES = 2
CHANNELS = 3
IMAGE_RESIZE = 224
RESNET50_POOLING_AVERAGE = 'avg'
DENSE_LAYER_ACTIVATION = 'softmax'
OBJECTIVE_FUNCTION = 'binary_crossentropy'
LOSS_METRICS = ['accuracy']
NUM_EPOCHS = 10
EARLY_STOP_PATIENCE = 3
STEPS_PER_EPOCH_TRAINING = 10
STEPS_PER_EPOCH_VALIDATION = 10
BATCH_SIZE_TRAINING = 100
BATCH_SIZE_VALIDATION = 100
BATCH_SIZE_TESTING = 1
WEIGHTS_PATH = "C:\\Users\\Desktop\\RESNET\\resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5"
model = Sequential()
train_data_dir = "C:\\Users\\Desktop\\RESNET"
from keras.models import load_model
model = ResNet50(include_top=False, pooling='avg', weights='imagenet')
x = model.output
predictions = Dense(1, activation='sigmoid')(x)
predictions = Dense(1, activation='sigmoid')(x)
model = Model(input = model.input, output = predictions)
model.summary()
print(model.summary())
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=SGD(lr=0.01, momentum=0.9), metrics= ['binary_accuracy'])
data_dir = "C:\\Users\\Desktop\\RESNET"
batch_size = 32
from keras.applications.resnet50 import preprocess_input
from keras.preprocessing.image import ImageDataGenerator
image_size = IMAGE_RESIZE
def append_ext(fn):
return fn+".jpg"
from os import listdir
from os.path import isfile, join
dir_path = os.path.dirname(os.path.realpath(__file__))
train_dir_path = dir_path + '\data'
onlyfiles = [f for f in listdir(dir_path) if isfile(join(dir_path, f))]
NUM_CLASSES = 2
data_labels = [0, 1]
t = []
maxi = 25145
LieOffset = 15799
i = 0
while i < maxi: # t = tuple
if i <= LieOffset:
t.append(label['Lie'])
else:
t.append(label['Truth'])
i = i+1
train_datagenerator = ImageDataGenerator(rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
validation_split=0.2) # set validation split 20% versus 80% pour training ### Aka ivghigh
train_generator = train_datagenerator.flow_from_directory(
train_data_dir,
target_size=(image_size, image_size),
batch_size=BATCH_SIZE_TRAINING,
class_mode='binary', shuffle=False, subset='training') # set as training data
validation_generator = train_datagenerator.flow_from_directory(
train_data_dir, # same directory as training data kifkif
target_size=(image_size, image_size),
batch_size=BATCH_SIZE_TRAINING,
class_mode='binary', shuffle=False, subset='validation') # set as validation data
from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint
cb_early_stopper = EarlyStopping(monitor = 'val_loss', patience = EARLY_STOP_PATIENCE)
cb_checkpointer = ModelCheckpoint(filepath = 'C:\\Users\\Desktop\\RESNET \\resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')
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]
)
model.load_weights('..\resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')
Спасибо за вашу помощь и пожелания