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kin4stat
Asked:2020-05-20 03:20:08 +0000 UTC2020-05-20 03:20:08 +0000 UTC

您输入的数据用完了；中断训练

  • 772

缺少数据错误。大约 4-5 天前，同一代码上不存在此错误。模型创建代码：

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(27, 48, 1)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation("relu"))
model.add(Dropout(0.5))
model.add(Dense(10))
model.add(Activation('sigmoid'))
model.compile(loss="categorical_crossentropy", optimizer="adam",
              metrics=["accuracy"])

学习代码

datagen = ImageDataGenerator()
dirTrain = "/content/GeneratedI/train"
train_data = datagen.flow_from_directory(dirTrain, target_size=(27, 48), batch_size=100,
                                         class_mode="categorical", color_mode="grayscale")
dirVal = "/content/GeneratedI/val"
validation_data = datagen.flow_from_directory(dirVal, target_size=(27, 48), batch_size=100,
                                              class_mode="categorical", color_mode="grayscale")
print("Training the network...")
t_start = time.time()
history = model.fit_generator(train_data,
                              steps_per_epoch=60000 // 10,
                              epochs=1,
                              validation_data=validation_data,
                              validation_steps=10000 // 10)
print(time.time() - t_start)

肯定有图像，60k 用于训练，10k 用于验证。甚至输出也证实了这一点：

Found 60000 images belonging to 10 classes.
Found 10000 images belonging to 10 classes.

我使用谷歌协作

python

1 个回答

  1. Best Answer
    history = model.fit_generator(train_data,
                              steps_per_epoch=60000 // 10,  # <-- 1
                              epochs=1,
                              validation_data=validation_data,
                              validation_steps=10000 // 10)  # <-- 2

    第一种和第二种情况，需要除以batch的大小。根据生成器判断，你有 100 个：

    train_data = datagen.flow_from_directory(dirTrain, 
                                         target_size=(27, 48), 
                                         batch_size=100,  # <-- 3
                                         class_mode="categorical", 
                                         color_mode="grayscale")
    • 1

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