数字识别器 kaggle 版本

如何解决数字识别器 kaggle 版本

在使用 MNIST 数据集解决数字识别任务后，我尝试在 kaggle 上解决相同的精确竞争：

https://www.kaggle.com/c/digit-recognizer

尝试拟合模型后，出现以下错误：

InvalidArgumentError:  logits and labels must have the same first dimension,got logits shape [32,10] and labels shape [640]
     [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at <ipython-input-122-653fb886dd19>:3) ]] [Op:__inference_train_function_250757]

Function call stack:
train_function

在进行了一些搜索之后，我尝试将其中一个参数更改为 categorical_crossentropy，而没有 sparse，但这会产生另一个错误。 我不知道该怎么做才能让它发挥作用。

import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
# %matplotlib inline
import numpy as np
import pandas as pd
import cv2 as cv
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras.utils import to_categorical

mnist_train = pd.read_csv(r"train_digit_recognition.csv")
mnist_test = pd.read_csv(r"test_digit_recognition.csv")
X_train=mnist_train.drop(['label'],axis=1) #df= df.drop(['Date'],axis=1)
y_train=mnist_train['label']
X_test=(mnist_test)
train_images = X_train.to_numpy()
train_labels=y_train.to_numpy()
test_images=X_test.to_numpy()
train_images = train_images.reshape((42000,28,1))
test_images = test_images.reshape((28000,1))
# Normalize the train dataset
train_images = tf.keras.utils.normalize(train_images,axis=1)
# Normalize the test dataset
test_images = tf.keras.utils.normalize(test_images,axis=1)
#to categorical
train_labels = to_categorical(train_labels) 
#Build the model object
model = tf.keras.models.Sequential()
# Add the Flatten Layer
model.add(tf.keras.layers.Flatten())
# Build the input and the hidden layers
model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu))
# Build the output layer
model.add(tf.keras.layers.Dense(10,activation=tf.nn.softmax))
# Compile the model
model.compile(optimizer="adam",loss="sparse_categorical_crossentropy",metrics=["accuracy"])
model.fit(x=train_images,y=train_labels,epochs=15)

最后两行给了我错误。