12.5由于jupyter问题重新安装了anaconda

蛋没关系啊,唉俩小时以后,他就装好了

from keras.datasets import mnist
(train_images,train_labels),(test_images,test_labels) = mnist.load_data()

第一步,加载mnist数据集


from keras import models
from keras import layers
network = models.Sequential()
network.add(layers.Dense(512, activation='relu',input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softmax'))

第二步,构建网络框架,keras作为最快上手的,基本上一开始直接相信教程中的选择。

该网络层有俩个层,他们是全连接的神经层

relu函数作为一开始介绍到的激活函数

softmax作为一个10个概率值的恒等函数,他的输出就是识别的结果


network.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])

第三步:编译步骤(相信即可):确定损失函数,优化器等,暂不做具体介绍


train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28 * 28))
test_images = test_images.astype('float32') / 255

第四步:准备图像数据,对图像数据进行一个预处理


from keras.utils import to_categorical
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

第五步:准备标签,准备开始拟合!


network.fit(train_images, train_labels, epochs=5, batch_size=128)

进行拟合


完整代码:

from keras.datasets import mnist
(train_images,train_labels),(test_images,test_labels) = mnist.load_data()
from keras import models
from keras import layers
network = models.Sequential()
network.add(layers.Dense(512, activation='relu',input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softmax'))

network.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])

train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28 * 28))
test_images = test_images.astype('float32') / 255

from keras.utils import to_categorical
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

network.fit(train_images, train_labels, epochs=5, batch_size=128)

执行结果(你可能会不一样)

Epoch 1/5
60000/60000 [==============================] - 6s 97us/step - loss: 0.2550 - acc: 0.9263
Epoch 2/5
60000/60000 [==============================] - 7s 116us/step - loss: 0.1043 - acc: 0.9696
Epoch 3/5
60000/60000 [==============================] - 8s 131us/step - loss: 0.0694 - acc: 0.9782
Epoch 4/5
60000/60000 [==============================] - 6s 95us/step - loss: 0.0503 - acc: 0.9853
Epoch 5/5
60000/60000 [==============================] - 6s 97us/step - loss: 0.0374 - acc: 0.9883
Last modification:December 5th, 2020 at 03:18 am
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