keras（3.4）绘制损失函数曲线

最新推荐文章于 2025-06-08 10:35:28 发布

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最新推荐文章于 2025-06-08 10:35:28 发布

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分类专栏： keras 文章标签： python深度学习

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本文介绍了一种在MNIST数据集上训练简单深度神经网络的方法，通过20轮的训练，实现了98.40%的测试准确率。每轮训练仅需2秒，适用于K520GPU。文章详细展示了数据预处理、模型搭建、参数设置及训练过程，并提供了训练损失和验证损失的可视化图表。

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-- coding: utf-8 --

‘’‘Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is a lot of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
‘’’

from future import print_function

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

batch_size = 128
num_classes = 10
epochs = 20

the data, shuffled and split between train and test sets

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

(x_train, y_train), (x_test, y_test) = mnist.load_data(path=’/home/duchao/下载/mnist.npz’)

import numpy as np

path = ‘/home/duchao/下载/mnist.npz’

f = np.load(path)

x_train, y_train = f[‘x_train’], f[‘y_train’]

x_test, y_test = f[‘x_test’], f[‘y_test’]

f.close()

x_train = x_train.reshape(60000, 784).astype(‘float32’)
x_test = x_test.reshape(10000, 784).astype(‘float32’)
x_train /= 255
x_test /= 255
print(x_train.shape[0], ‘train samples’)
print(x_test.shape[0], ‘test samples’)

convert class vectors to binary class matrices

label为0~9共10个类别，keras要求格式为binary class matrices

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

add by hcq-20171106

Dense of keras is full-connection.

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’))

model.summary()

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

history_dict=history.history
loss_value=history_dict[“loss”]
val_loss_value=history_dict[“val_loss”]

epochs=range(1,len(loss_value)+1)
plt.plot(epochs,loss_value,“bo”,label=“Training loss”)
plt.plot(epochs,val_loss_value,“b”,label=“Validation loss”)
plt.xlabel(“epochs”)
plt.ylabel(“loss”)
plt.legend()
plt.show()