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[英]Error while training: tensorflow:Your input ran out of data; interrupting training
[英]“WARNING:tensorflow:Your input ran out of data” Error appearing when training Keras Model
完整的警告是:WARNING:tensorflow:Your input run out of data; 中斷訓練。 確保您的數據集或生成器至少可以生成steps_per_epoch * epochs
批次(在本例中為 3400 個批次)。 構建數據集時,您可能需要使用 repeat() function。
# importing libraries
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
import tensorflow as tf
train_data_dir = 'marvel/train'
validation_data_dir = 'marvel/valid'
nb_train_samples = 2584
nb_validation_samples = 451
epochs = 100
batch_size_train = 76
batch_size_val = 41
if K.image_data_format() == 'channels_first':
input_shape = (3, 200, 200)
else:
input_shape = (200, 200, 3)
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(200, 200, 3)),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(8, activation='softmax')
])
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)
train_generator = train_datagen.flow_from_directory(train_data_dir,
target_size=(200, 200),
batch_size=batch_size_train,
classes=['black widow', 'captain america', 'doctor strange', 'hulk', 'iron man', 'loki', 'spiderman', 'thanos'],
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(200, 200),
batch_size=batch_size_val, class_mode='categorical')
model.fit(train_generator,
steps_per_epoch=nb_train_samples // batch_size_train,
epochs=epochs, validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size_val)
model.save_weights('characterImg.h5')
print("Saved model characterImg.h5")
以上是我的代碼。 誰能幫我理解錯誤的實際含義? 我有很多麻煩。 謝謝! (如果您需要更多信息,請告訴我)
好的,我不確定這是否適用於所有人,但為了解決這個問題,我只是刪除了該行
steps_per_epoch=nb_train_samples // batch_size_train,
它奏效了。 我意識到這並不理想,但對於那些尋求絕望解決方案的人來說,這可能對你有用
看起來您的數據集長度小於您的nb_train_samples
/ nb_validation_samples
。
在擬合之前添加repeat()
調用:
train_generator = train_generator.repeat()
validation_generator = validation_generator.repeat()
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