![](/img/trans.png)
[英]ValueError: Shape mismatch: The shape of labels (received (1,)) should equal the shape of logits except for the last dimension (received (10, 30))
[英]ValueError: Shape mismatch: The shape of labels (received (15,)) should equal the shape of logits except for the last dimension (received (5, 3))
尝试拟合 model 时出现此错误:
ValueError:形状不匹配:标签的形状(接收到的(15,))应该等于逻辑的形状,除了最后一个维度(接收到的(5,3))。
产生错误的代码:
history = model.fit_generator(
train_generator,
epochs=10,
validation_data=validation_generator)
这就是train_generator,验证生成器类似:
train_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(IMG_WIDTH, IMG_HEIGHT),
batch_size=5)
我尝试获取形状:
for data_batch, labels_batch in train_generator:
print('data batch shape:', data_batch.shape)
print('labels batch shape:', labels_batch.shape)
break
数据批次形状:(5, 192, 192, 3) 标签批次形状:(5, 3)
当我更改批次大小时,错误中标签的形状会相应更改(批次大小为 3 会产生形状为 label (9,) 的错误,例如,我有 3 个类)。 但我担心的是它是由 train_generator 完成的,我能做些什么来解决这个问题吗? 此外,当我从 train_generator 打印形状时,它似乎是正确的。
这是 model,以备不时之需:
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
for i in range(2):
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(3, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
谢谢!
编辑 - 完整代码:
该目录包含两个文件夹 - train 和 validation,每个文件夹都有三个子文件夹,其中包含相应类别的图像。
try:
%tensorflow_version 2.x # enable TF 2.x in Colab
except Exception:
pass
from tensorflow.keras import datasets, layers, models
IMG_WIDTH = 192
IMG_HEIGHT = 192
train_dir = 'train'
validation_dir = 'validation'
from google.colab import drive
drive.mount('/content/drive')
import os
os.chdir("drive/My Drive/colab")
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(IMG_WIDTH, IMG_HEIGHT),
batch_size=5)
validation_datagen = ImageDataGenerator(rescale=1./255)
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(IMG_WIDTH, IMG_HEIGHT),
batch_size=5)
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
for i in range(2):
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(3, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
history = model.fit_generator(
train_generator,
epochs=10,
validation_data=validation_generator)
谢谢!
sparse_categorical_crossentropy
和categorical_crossentropy
之间的区别在于您的目标是否是单热编码的。
label 批次的形状是(5,3)
,这意味着它已经过一次热编码。 所以你应该使用categorical_crossentropy
loss function。
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
我认为这与您的损失有关 function 只是尝试使用“categorical_crossentropy”而不是“sparse ...”
我在处理 Fashion Most 数据集时遇到了这个错误。 我想我的 model 中没有包括 Flatten 层。添加该层后,问题就解决了。
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