04.绘制精度和损失曲线
程序说明
时间：2016年11月16日
说明：该程序是一个包含两个隐藏层的神经网络，程序会保存每轮训练的acc和loss，并且绘制它们。
数据集：MNIST
1.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
Using TensorFlow backend.
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()
2.变量初始化
batch_size = 128
nb_classes = 10
nb_epoch = 20
3.准备数据
# 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')
60000 train samples
10000 test samples
转换类标号
# 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)
4.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()
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
dense_1 (Dense) (None, 512) 401920 dense_input_1[0][0]
____________________________________________________________________________________________________
activation_1 (Activation) (None, 512) 0 dense_1[0][0]
____________________________________________________________________________________________________
dropout_1 (Dropout) (None, 512) 0 activation_1[0][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 512) 262656 dropout_1[0][0]
____________________________________________________________________________________________________
activation_2 (Activation) (None, 512) 0 dense_2[0][0]
____________________________________________________________________________________________________
dropout_2 (Dropout) (None, 512) 0 activation_2[0][0]
____________________________________________________________________________________________________
dense_3 (Dense) (None, 10) 5130 dropout_2[0][0]
____________________________________________________________________________________________________
activation_3 (Activation) (None, 10) 0 dense_3[0][0]
====================================================================================================
Total params: 669706
____________________________________________________________________________________________________
5.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])
Train on 60000 samples, validate on 10000 samples
Epoch 1/20
60000/60000 [==============================] - 5s - loss: 0.2448 - acc: 0.9239 - val_loss: 0.1220 - val_acc: 0.9623
Epoch 2/20
60000/60000 [==============================] - 5s - loss: 0.1025 - acc: 0.9689 - val_loss: 0.0775 - val_acc: 0.9760
Epoch 3/20
60000/60000 [==============================] - 5s - loss: 0.0752 - acc: 0.9773 - val_loss: 0.0792 - val_acc: 0.9775
Epoch 4/20
60000/60000 [==============================] - 5s - loss: 0.0608 - acc: 0.9819 - val_loss: 0.0758 - val_acc: 0.9790
Epoch 5/20
60000/60000 [==============================] - 5s - loss: 0.0500 - acc: 0.9852 - val_loss: 0.0903 - val_acc: 0.9766
Epoch 6/20
60000/60000 [==============================] - 5s - loss: 0.0449 - acc: 0.9863 - val_loss: 0.0780 - val_acc: 0.9798
Epoch 7/20
60000/60000 [==============================] - 5s - loss: 0.0398 - acc: 0.9875 - val_loss: 0.0798 - val_acc: 0.9823
Epoch 8/20
60000/60000 [==============================] - 5s - loss: 0.0349 - acc: 0.9899 - val_loss: 0.0794 - val_acc: 0.9834
Epoch 9/20
60000/60000 [==============================] - 5s - loss: 0.0301 - acc: 0.9912 - val_loss: 0.0932 - val_acc: 0.9805
Epoch 10/20
60000/60000 [==============================] - 5s - loss: 0.0281 - acc: 0.9919 - val_loss: 0.0819 - val_acc: 0.9842
Epoch 11/20
60000/60000 [==============================] - 5s - loss: 0.0276 - acc: 0.9921 - val_loss: 0.1007 - val_acc: 0.9814
Epoch 12/20
60000/60000 [==============================] - 5s - loss: 0.0250 - acc: 0.9931 - val_loss: 0.0978 - val_acc: 0.9824
Epoch 13/20
60000/60000 [==============================] - 5s - loss: 0.0228 - acc: 0.9935 - val_loss: 0.1063 - val_acc: 0.9827
Epoch 14/20
60000/60000 [==============================] - 5s - loss: 0.0234 - acc: 0.9939 - val_loss: 0.1014 - val_acc: 0.9815
Epoch 15/20
60000/60000 [==============================] - 4s - loss: 0.0188 - acc: 0.9946 - val_loss: 0.1052 - val_acc: 0.9839
Epoch 16/20
60000/60000 [==============================] - 4s - loss: 0.0180 - acc: 0.9946 - val_loss: 0.1027 - val_acc: 0.9833
Epoch 17/20
60000/60000 [==============================] - 5s - loss: 0.0213 - acc: 0.9946 - val_loss: 0.1103 - val_acc: 0.9840
Epoch 18/20
60000/60000 [==============================] - 5s - loss: 0.0204 - acc: 0.9949 - val_loss: 0.1168 - val_acc: 0.9818
Epoch 19/20
60000/60000 [==============================] - 5s - loss: 0.0185 - acc: 0.9954 - val_loss: 0.1318 - val_acc: 0.9809
Epoch 20/20
60000/60000 [==============================] - 5s - loss: 0.0194 - acc: 0.9954 - val_loss: 0.1173 - val_acc: 0.9831
<keras.callbacks.History at 0x7f1ef8066590>
模型评估
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
Test score: 0.117298825225
Test accuracy: 0.9831
绘制acc-loss曲线
history.loss_plot('epoch')
