可用的kaggle-mnist代码

摸鱼时探索kaggle比赛机制
写了一个小代码

import pandas as pd
import numpy as np
import tensorflow as tf
#from tensorflow.keras.layers import Conv2D,BatchNormalization,Activation,MaxPool2D,Dropout, Flatten,Dense

data_train = pd.read_csv("../input/digit-recognizer/train.csv")
data_test = pd.read_csv("../input/digit-recognizer/test.csv")
#print(data_train[:10])
y_train=data_train['label'].tolist()
y_train=np.array(y_train)
x_train=data_train.iloc[:,1:].values
x_train=np.array(x_train)
#print(x_train[:10])
x_test=data_test.values
x_test=np.array(x_test)
y_test=[]
model = tf.keras.models.Sequential([
                                    tf.keras.layers.Dense(784,activation='relu'),
                                    tf.keras.layers.Dense(128, activation='relu'),
                                    tf.keras.layers.Dense(10, activation='softmax')
                                    ])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['sparse_categorical_accuracy'])

model.fit(x_train, y_train, epochs=15,batch_size=32)
print(len(x_test))
y_test=model.predict(x_test,batch_size = 1)
#print(y_test.shape)
y_test=tf.argmax(y_test,axis=1).numpy().tolist()
print(y_test[:10])
xid=[i+1 for i in range(len(y_test))]
daf= {
    "ImageId":xid,
    "Label":y_test,  
}
daf=pd.DataFrame(daf)
print(daf.iloc[:10])
daf.to_csv("ex2.csv",index=None)