python mnist数据集导入

在Python中，可以使用`tensorflow`或`keras`库来导入MNIST数据集。下面是使用`keras`库导入MNIST数据集的示例代码：

from keras.datasets import mnist

# 加载MNIST数据集
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# 打印训练集和测试集的形状
print("训练集形状:", train_images.shape)  # (60000, 28, 28)
print("训练集标签形状:", train_labels.shape)  # (60000,)
print("测试集形状:", test_images.shape)  # (10000, 28, 28)
print("测试集标签形状:", test_labels.shape)  # (10000,)

上述代码中，`mnist.load_data()`函数会自动下载MNIST数据集并将其分为训练集和测试集。训练集包含60000个样本，测试集包含10000个样本。每个样本都是一个28x28像素的灰度图像，对应的标签是0到9之间的数字。

相关问题

mnist数据集导入python

为了导入mnist数据集到Python中，可以使用tensorflow框架提供的input_data模块。首先，需要确保已经安装了tensorflow库。然后，可以按照以下步骤导入数据集：

1. 导入所需的库:

import tensorflow as tf

2. 加载数据集:

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

3. 数据预处理:

x_train = x_train / 255.0
x_test = x_test / 255.0

4. 可视化数据集:

import matplotlib.pyplot as plt

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(x_train[i], cmap=plt.cm.binary)
    plt.xlabel(y_train[i])
plt.show()

这样就成功导入了mnist数据集，并进行了简单的数据可视化。

MLP多层感知模型 代码 Python mnist数据集

以下是一个简单的 Python 代码示例，用于使用 MLP 多层感知模型训练和测试 MNIST 数据集。

首先，需要导入所需的库和模块：

import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop

然后，加载 MNIST 数据集并将其分为训练集和测试集：

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

接下来，对数据进行预处理，将图像数据转换为 1D 向量并将其归一化：

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

然后，将标签数据进行 one-hot 编码：

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

定义 MLP 模型：

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

编译模型并进行训练：

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

history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    validation_data=(x_test, y_test))

最后，评估模型并绘制准确率和损失曲线：

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# 绘制准确率和损失曲线
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.plot(history.history['loss'], label='loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.title('Training and validation accuracy and loss')
plt.xlabel('Epoch')
plt.ylabel('Accuracy/Loss')
plt.legend()
plt.show()

完整的代码示例：

import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop

# 设置参数
batch_size = 128
num_classes = 10
epochs = 20

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

# 数据预处理
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# 标签 one-hot 编码
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

# 定义模型
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

# 编译模型并训练
model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])

history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    validation_data=(x_test, y_test))

# 评估模型
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# 绘制准确率和损失曲线
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.plot(history.history['loss'], label='loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.title('Training and validation accuracy and loss')
plt.xlabel('Epoch')
plt.ylabel('Accuracy/Loss')
plt.legend()
plt.show()