PyTorch 卷积神经网络(Convents)的序列处理

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：