我确实对Keras的CNN有疑问,如果您想帮助我,我将非常感谢。
免责声明:我是CNN和Keras的菜鸟,我现在正在学习它们。
我的数据:
2个班级(狗和猫)
交易:每个类别30张图片
测试:每个类别14张图片
有效:每个类别30张图片
我的代码:
data_path = Path("../data")
train_path = data_path / "train"
test_path = data_path / "test"
valid_path = data_path / "valid"
train_batch = ImageDataGenerator().flow_from_directory(directory=train_path,
target_size=(200, 200),
classes=animals,
batch_size=10)
valid_batch = ImageDataGenerator().flow_from_directory(directory=valid_path,
target_size=(200, 200),
classes=animals,
batch_size=10)
test_path = ImageDataGenerator().flow_from_directory(directory=test_path,
target_size=(200, 200),
classes=animals,
batch_size=4)
imgs, labels = next(train_batch)
model = Sequential(
[Conv2D(32, (3, 3), activation="relu", input_shape=(200, 200, 3)), Flatten(),
Dense(len(animals), activation='softmax')])
model.compile(Adam(lr=.0001), loss='categorical_crossentropy', metrics=['accuracy'])
model.fit_generator(train_path, steps_per_epoch=4, validation_data=valid_batch, validation_steps=3, epochs=5, verbose=2)
这是我的错误消息:
我已将路径替换为“”
Traceback (most recent call last):
File "", line 191, in <module>
model.fit_generator(train_path, steps_per_epoch=4, validation_data=valid_batch, validation_steps=3, epochs=5, verbose=2)
File "y", line 91, in wrapper
return func(*args, **kwargs)
File "", line 1732, in fit_generator
initial_epoch=initial_epoch)
File "", line 185, in fit_generator
generator_output = next(output_generator)
File "", line 742, in get
six.reraise(*sys.exc_info())
File "", line 693, in reraise
raise value
File "", line 711, in get
inputs = future.get(timeout=30)
File "", line 657, in get
raise self._value
File "", line 121, in worker
result = (True, func(*args, **kwds))
File "", line 650, in next_sample
return six.next(_SHARED_SEQUENCES[uid])
TypeError: 'PosixPath' object is not an iterator
有人可以告诉我我做错了什么吗?另外,如果这是一个离题的问题,请告诉我在哪里可以问到。
答案 0 :(得分:0)
此行不是必需的
imgs, labels = next(train_batch)
是一个生成器对象,没有提供的字符串。像这样
model.fit_generator(train_path, steps_per_epoch=4, validation_data=valid_batch, validation_steps=3, epochs=5, verbose=2)
答案 1 :(得分:0)
您遇到的问题是您没有通过训练的生成器,而是文件的路径(您使用的是 train_path
而不是 {{1 }} 。
使用train_batch
时需要为对象传递生成器:
.fit_generator()