我该如何解决此错误，logit和标签的第一维必须相同

Question

这是我第一次使用神经网络。拟合我的代码后，出现此错误：

logit和标签必须具有相同的第一尺寸，logits形状为[4,4096]，标签形状为[16384] [[node loss / activation_27_loss / SparseSoftmaxCrossEntropyWithLogits / SparseSoftmaxCrossEntropyWithLogits（在C：\ Users \ admin \ Miniconda3 \ lib \ site-packages \ tensorflow_core \ python \ framework \ ops.py：1751定义）]] [操作：__ inference_distributed_function_8265]函数调用堆栈：分布式功能

您能帮我为什么出现此错误，这是我的代码：

batch_size = 5
learning_rate = 0.8
no_classes = 1
no_epochs = 3
validation_split = 0.2
verbosity = 0
import tensorflow as tf
import tensorflow.python.keras 
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Input
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D
from os import listdir
from os.path import isfile, join
import pickle
from tensorflow.python.keras.utils import to_categorical
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.layers.normalization import BatchNormalization


pickle_in = open("X.pickle","rb")
X= pickle.load(pickle_in)

pickle_in = open("Y.pickle","rb")
Y = pickle.load(pickle_in)

# Y=Y/255

img_rows=img_cols=64

if K.image_data_format()== 'channels_first':
    X = np.array(X).reshape(np.array(X).shape[0], 1, img_rows, img_cols)
    Y= np.array(Y).reshape(np.array(Y).shape[0], 1, img_rows, img_cols)
    print(X.shape)
    print(Y.shape)
    input_shape = (1, img_rows, img_cols)
else:
    X = np.array(X).reshape(np.array(X).shape[0], img_rows, img_cols, 1)
    Y = np.array(Y).reshape(np.array(Y).shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols,1)    

print(X.shape)
print(Y.shape)
print(input_shape)

model = Sequential()
model.add(Conv2D(64, (3, 3),input_shape=input_shape,padding="same"))
model.add(Activation('relu'))

model.add(Conv2D(64, (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*64))
model.add(Activation('relu'))

model.summary()

model.compile(loss=tensorflow.keras.losses.sparse_categorical_crossentropy,
              optimizer=tensorflow.keras.optimizers.Adam(),
              metrics=['accuracy'])

model.fit(X,Y,
          batch_size=5,
          epochs=no_epochs,
          verbose=verbosity,
          validation_split=validation_split)
score = model.evaluate(X,Y, batch_size=5)

我不知道该如何处理这个错误

Answer 1

由于使用sparse_categorical_crossentropy丢失功能而发生错误，请用categorical_crossentropy替换。

请在下面替换您的model.complie块

model.compile(loss=tensorflow.keras.losses.categorical_crossentropy,
              optimizer=tensorflow.keras.optimizers.Adam(),
              metrics=['accuracy'])