从数据集中包含的值创建具有特定池的顺序网络。这个过程在“图像识别模块”中也得到了很好的应用。

以下步骤用于使用PyTorch创建卷积神经网络(Convents)的序列处理模型：

1、导入模块

导入必要的模块，以执行序列处理使用卷积神经网络(Convents)。

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

2、执行操作

使用下面的代码执行必要的操作，以相应的顺序创建一个模式：

batch_size = 128 
num_classes = 10 
epochs = 12
# input image dimensions 
img_rows, img_cols = 28, 28
# the data, split between train and test sets 
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000,28,28,1) 
x_test = x_test.reshape(10000,28,28,1)
print('x_train shape:', x_train.shape) 
print(x_train.shape[0], 'train samples') 
print(x_test.shape[0], 'test samples')
y_train = keras.utils.to_categorical(y_train, num_classes) 
y_test = keras.utils.to_categorical(y_test, num_classes)

3、编译模型

编译模型并拟合上述常规神经网络模型，如下图所示：

model.compile(loss = 
keras.losses.categorical_crossentropy, 
optimizer = keras.optimizers.Adadelta(), metrics = 
['accuracy'])
model.fit(x_train, y_train, 
batch_size = batch_size, epochs = epochs, 
verbose = 1, validation_data = (x_test, y_test)) 
score = model.evaluate(x_test, y_test, verbose = 0) 
print('Test loss:', score[0]) 
print('Test accuracy:', score[1])

生成的输出如下：