前言
笔者权当做笔记，借鉴的是《Python 深度学习》这本书，里面的代码也都是书上的代码，用的是jupyter notebook 编写代码。今天正式接触卷积神经网络。
一、代码和运行结果
import keras
from keras import layers
from keras import models
from keras.datasets import mnist
from keras.utils import to_categorical
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
h = model.fit(train_images, train_labels, epochs=5, batch_size=64)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(test_acc)
二、图例解读代码过程
以下是我个人理解的手写辨识的代码理解。
网络的架构是：
个人图示：
总结
刚刚接触的卷积神经网络，MNIST数字识别算得上是卷积神经网络中的“Hello World”;我也仔细阅读了《Python 深度学习》这本书有关这方面的内容，真心不错！当作笔记，记录美好生活(唉昨天原神小保底出了个地卢克)，向前冲！🙂。