x_train,x_test,y_train,y_test=train_test_split(img_list,y_onehot_arr,test_size=0.2,random_state=123) x_train.shape,x_test.shape,y_train.shape,y_test.shape

这是一个机器学习中的数据集划分问题，可以回答。x_train,x_test,y_train,y_test是用于训练和测试模型的数据集，其中img_list是图像数据，y_onehot_arr是对应的标签数据。train_test_split是一个函数，用于将数据集划分为训练集和测试集。其中test_size表示测试集所占比例，random_state表示随机种子，用于保证每次划分的结果相同。最后，x_train.shape,x_test.shape,y_train.shape,y_test.shape是用于查看数据集的维度信息。

相关问题

下面的代码哪里有问题，帮我改一下from __future__ import print_function import numpy as np import tensorflow import keras from keras.models import Sequential from keras.layers import Dense,Dropout,Flatten from keras.layers import Conv2D,MaxPooling2D from keras import backend as K import tensorflow as tf import datetime import os np.random.seed(0) from sklearn.model_selection import train_test_split from PIL import Image import matplotlib.pyplot as plt from keras.datasets import mnist images = [] labels = [] (x_train,y_train),(x_test,y_test)=mnist.load_data() X = np.array(images) print (X.shape) y = np.array(list(map(int, labels))) print (y.shape) x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0) print (x_train.shape) print (x_test.shape) print (y_train.shape) print (y_test.shape) ############################ ########## batch_size = 20 num_classes = 4 learning_rate = 0.0001 epochs = 10 img_rows,img_cols = 32 , 32 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 print('x_train shape:',x_train.shape) print(x_train.shape[0],'train samples') print(x_test.shape[0],'test samples')

代码中存在以下问题：

1. 变量 images 和 labels 没有被赋值，而后面却使用了它们来构造 X 和 y，导致 X 和 y 的值都为空。

2. 导入的 mnist 数据集没有被使用，而是使用了未定义的 images 和 labels，需要将其替换为 mnist 数据集。

3. input_shape 的值应该是三元组 (img_rows, img_cols, 1)，而代码中缺少了最后一个 1。

4. 在 if K.image_data_format() =='channels_first': 分支中，x_train 和 x_test 被改变了形状，但 y_train 和 y_test 却没有被改变，需要将其同步修改。

下面是修改后的代码：

from __future__ import print_function
import numpy as np
import tensorflow
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import tensorflow as tf
import datetime
import os
np.random.seed(0)
from sklearn.model_selection import train_test_split
from PIL import Image
import matplotlib.pyplot as plt
from keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
print(x_train.shape)
print(y_train.shape)

############################
##########
batch_size = 20
num_classes = 10
learning_rate = 0.0001
epochs = 10

img_rows, img_cols = 32, 32

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 = (img_rows, img_cols, 1)
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)

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print('input_shape:', input_shape)

实现中文手写数字识别，读取data文件夹里的图片并提取标签里的数字，读取chinese_mnist.csv文件里的并提取character。将图片进行预处理，其中包括该图片大小：150*150；添加RGB三通道，数据归一化；再进行划分测试集与训练集，其比例为2：8；再进行CNN神经网络的搭建，再进行模型编译，模型训练。最后随机选取一张图片进行实现图像识别

好的，我理解了你的要求。实现中文手写数字识别需要以下步骤：

1. 读取data文件夹里的图片并提取标签里的数字，读取chinese_mnist.csv文件里的并提取character。

import os
import pandas as pd
from PIL import Image

data_path = '/path/to/data/'
csv_path = '/path/to/chinese_mnist.csv'

# 读取csv文件
csv_data = pd.read_csv(csv_path)

# 读取图片并提取标签里的数字
images = []
labels = []
for filename in os.listdir(data_path):
    if filename.endswith('.jpg'):
        img_path = os.path.join(data_path, filename)
        im = Image.open(img_path)
        images.append(im)
        label = int(filename.split('_')[0])
        labels.append(label)

# 提取character
characters = csv_data['character'].tolist()

2. 对图片进行预处理，包括该图片大小：150*150；添加RGB三通道，数据归一化。

import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split

# 图片大小
IMG_SIZE = 150

# 转换为numpy数组
images = np.array([np.array(im.resize((IMG_SIZE, IMG_SIZE)).convert('RGB')) for im in images])

# 数据归一化
images = images / 255.0

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)

3. 进行CNN神经网络的搭建。

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout

# 定义模型
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dropout(0.5),
    Dense(15, activation='softmax')
])

# 输出模型结构
model.summary()

4. 进行模型编译，模型训练。

# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 模型训练
model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))

5. 随机选取一张图片进行实现图像识别。

import random

# 随机选取一张图片
index = random.randint(0, len(X_test)-1)
image = X_test[index]
label = y_test[index]
character = characters[label]

# 进行预测
prediction = model.predict(np.array([image]))
predicted_label = np.argmax(prediction)

# 输出结果
print('实际标签：', label)
print('预测标签：', predicted_label)
print('实际字符：', character)

以上就是实现中文手写数字识别的完整代码。