我正在训练一个图像分类器来区分猫和狗,来自Kaggle集。
这是我的相关代码:
FINAL_ACTIVATION = "softmax"
OPTIMIZER = keras.optimizers.Adamax()
STRIDES = (2, 2)
DROPOUT = 0.5
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape = INPUT_SHAPE))
model.add(keras.layers.ELU())
model.add(MaxPooling2D(pool_size = (2, 2), strides = STRIDES))
model.add(Conv2D(64, (3, 3)))
model.add(keras.layers.ELU())
model.add(MaxPooling2D(pool_size = (2, 2), strides = STRIDES))
model.add(Conv2D(128, (3, 3)))
model.add(keras.layers.ELU())
model.add(MaxPooling2D(pool_size = (2, 2), strides = STRIDES))
model.add(Conv2D(128, (3, 3)))
model.add(keras.layers.ELU())
model.add(MaxPooling2D(pool_size = (2, 2), strides = STRIDES))
model.add(Flatten())
model.add(Dense(100))
model.add(keras.layers.ELU())
model.add(Dropout(DROPOUT))
model.add(Dense(50))
model.add(keras.layers.ELU())
model.add(Dropout(DROPOUT))
model.add(Dense(2))
model.add(Activation(FINAL_ACTIVATION))
model.compile(
loss="categorical_crossentropy",
optimizer = OPTIMIZER,
metrics = ["accuracy"]
)
train_datagen = ImageDataGenerator(
rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True
)
train_generator = train_datagen.flow(
x_train,
y_train,
batch_size = BATCH_SIZE
)
model.summary()
model.fit_generator(
train_generator,
steps_per_epoch = x_train.shape[0] // BATCH_SIZE,
epochs = EPOCHS
)
由于训练文件数量众多,因此没有测试集。但是,当我尝试训练这个网络时,我收到错误"ValueError:检查目标时出错:预期activation_1具有形状 (2,(,但得到具有形状 (1,( 的数组。我在这里做错了什么?
卷积神经网络对我来说仍然有点黑魔法,所以我可能在这里犯了一些初学者的错误。我想我可能是。
这是我的模型:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 98, 98, 32) 896
_________________________________________________________________
elu_1 (ELU) (None, 98, 98, 32) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 49, 49, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 47, 47, 64) 18496
_________________________________________________________________
elu_2 (ELU) (None, 47, 47, 64) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 23, 23, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 21, 21, 128) 73856
_________________________________________________________________
elu_3 (ELU) (None, 21, 21, 128) 0
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 10, 10, 128) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 8, 8, 128) 147584
_________________________________________________________________
elu_4 (ELU) (None, 8, 8, 128) 0
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 4, 4, 128) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 2048) 0
_________________________________________________________________
dense_1 (Dense) (None, 100) 204900
_________________________________________________________________
elu_5 (ELU) (None, 100) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 100) 0
_________________________________________________________________
dense_2 (Dense) (None, 50) 5050
_________________________________________________________________
elu_6 (ELU) (None, 50) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 50) 0
_________________________________________________________________
dense_3 (Dense) (None, 2) 102
_________________________________________________________________
activation_1 (Activation) (None, 2) 0
=================================================================
Total params: 450,884
Trainable params: 450,884
Non-trainable params: 0
_________________________________________________________________
由于训练文件数量众多,因此没有测试集。
虽然这不是你的问题,但我觉得应该解决这个问题:我知道有些小组遵循非常不同的方法并且不保留有效/测试集,但这对我来说听起来非常非正统和不确定。就个人而言,我很少看到没有测试测试或某种训练后验证的文章。
测试集非常有用,因为它们是模型适用于看不见的样本的经验证据。使用它们 + 统计显著性/置信度检验,您可以根据统计基础断言您的模型不仅是一个非常复杂的 if-else 机器。
最重要的是,如果您是该地区的新手,则不应遵守此类冒险策略。
关于您的问题:y_train.shape
是什么?我相信您正在流动稀疏标签([1., 0., 1., 2., 3., ..., 4.]
(而不是独热编码的标签:
[[0. 1.]
[1. 0.]
[0. 1.]
...
[1. 0.]]
argmax(x, axis=1)
为您提供真正的类索引。
如何解决:
将loss="categorical_crossentropy"
替换为 sparse_categorical_crossentropy
。这是内存效率高的,但要求每个样本与单个标签相关联。
或
在将y_train
传递给ImageDataGenerator#flow
之前对其进行单热编码:
from keras.utils import to_categorical
y_train = to_categorical(y_train)
train_datagen = ImageDataGenerator(...)
train_generator = train_datagen.flow(x_train, y_train, ...)
...
这会占用更多内存,但允许单个样本与多个标签相关联:
[[1. 1. 0.]
[0. 0. 1.]
...
[1. 0. 1.]]