深度学习可以确定两个手写样本是否来自同一人吗？

是的，深度学习可以用于确定两个手写样本是否来自同一人。以下是一个使用深度学习模型进行手写样本比较的示例代码：

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.datasets import mnist
from tensorflow.keras import backend as K

# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 数据预处理
img_rows, img_cols = 28, 28
if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# 构建深度学习模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
batch_size = 128
num_classes = 10
epochs = 10
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))

# 使用模型进行预测
predictions = model.predict(x_test)

# 比较两个手写样本是否来自同一人
sample1 = x_test[0]
sample2 = x_test[1]

prediction1 = model.predict(np.expand_dims(sample1, axis=0))
prediction2 = model.predict(np.expand_dims(sample2, axis=0))

if np.argmax(prediction1) == np.argmax(prediction2):
    print("两个手写样本来自同一人")
else:
    print("两个手写样本来自不同人")

上述代码使用了一个基于卷积神经网络的深度学习模型来对手写数字进行分类。在训练完模型后，我们可以使用训练好的模型对两个手写样本进行预测，并根据预测结果判断它们是否来自同一人。