3、keras画acc和loss曲线图
<pre><code>#加载keras模块
from __future__ import print_function
import numpy as np
np.random.seed(1337) # for reproducibility
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD, Adam, RMSprop
from keras.utils import np_utils
import matplotlib.pyplot as plt
%matplotlib inline
#写一个LossHistory类，保存loss和acc
class LossHistory(keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self.losses = {'batch':[], 'epoch':[]}
self.accuracy = {'batch':[], 'epoch':[]}
self.val_loss = {'batch':[], 'epoch':[]}
self.val_acc = {'batch':[], 'epoch':[]}
def on_batch_end(self, batch, logs={}):
self.losses['batch'].append(logs.get('loss'))
self.accuracy['batch'].append(logs.get('acc'))
self.val_loss['batch'].append(logs.get('val_loss'))
self.val_acc['batch'].append(logs.get('val_acc'))
def on_epoch_end(self, batch, logs={}):
self.losses['epoch'].append(logs.get('loss'))
self.accuracy['epoch'].append(logs.get('acc'))
self.val_loss['epoch'].append(logs.get('val_loss'))
self.val_acc['epoch'].append(logs.get('val_acc'))
def loss_plot(self, loss_type):
iters = range(len(self.losses[loss_type]))
plt.figure()
# acc
plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')
# loss
plt.plot(iters, self.losses[loss_type], 'g', label='train loss')
if loss_type == 'epoch':
# val_acc
plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')
# val_loss
plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')
plt.grid(True)
plt.xlabel(loss_type)
plt.ylabel('acc-loss')
plt.legend(loc="upper right")
plt.show()
#变量初始化
batch_size = 128
nb_classes = 10
nb_epoch = 20
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.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
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
#建立模型 使用Sequential（）
model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))
#打印模型
model.summary()
#训练与评估
#编译模型
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(),
metrics=['accuracy'])
#创建一个实例history
history = LossHistory()
#迭代训练（注意这个地方要加入callbacks）
model.fit(X_train, Y_train,
batch_size=batch_size, nb_epoch=nb_epoch,
verbose=1,
validation_data=(X_test, Y_test),
callbacks=[history])
#模型评估
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
#绘制acc-loss曲线
history.loss_plot('epoch')
</code></pre>
<p>作者：安静会
来源：CSDN
原文：<a href="https://blog.csdn.net/u013381011/article/details/78911848">https://blog.csdn.net/u013381011/article/details/78911848</a>
版权声明：本文为博主原创文章，转载请附上博文链接！</p>