keras 可视化模型结果，f1_score，recall，acc,acc_valid,checkpoint

keras 可视化模型结果，f1_score，recall，acc,acc_valid,checkpoint

姚贤贤 2018-08-08 17:16:25 4488 收藏 10
展开
'''
Created on 2018年8月8日

'''

import keras
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
import tflearn
import tflearn.datasets.mnist as mnist
from keras.callbacks import Callback,ModelCheckpoint
from sklearn.metrics import f1_score, precision_score, recall_score
import matplotlib.pyplot as plt

class Metrics(Callback):
def on_train_begin(self, logs={}):
self.val_f1s = []
self.val_recalls = []
self.val_precisions = []

def on_epoch_end(self, epoch, logs={}):
val_predict = (np.asarray(self.model.predict(self.validation_data[0]))).round()
val_targ = self.validation_data[1]
_val_f1 = f1_score(val_targ, val_predict)
_val_recall = recall_score(val_targ, val_predict)
_val_precision = precision_score(val_targ, val_predict)
self.val_f1s.append(_val_f1)
self.val_recalls.append(_val_recall)
self.val_precisions.append(_val_precision)
print('- val_f1: %.4f - val_precision: %.4f - val_recall: %.4f'%(_val_f1, _val_precision, _val_recall))
return

#只拿0,1数据
x_train, y_train, x_test, y_test = mnist.load_data(one_hot=False)
train_index0 = np.where(y_train == 0)[0]
train_index1 = np.where(y_train == 1)[0]
test_index0 = np.where(y_test == 0)[0]
test_index1 = np.where(y_test == 1)[0]
print(len(train_index0))
print(len(train_index1))
train_indexs = np.append(train_index0,train_index1)
test_indexs = np.append(test_index0,test_index1)
print(len(train_indexs))

x_train = x_train[train_indexs,:]
y_train = y_train[train_indexs]
x_test = x_test[test_indexs,:]
y_test = y_test[test_indexs]
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=784))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='sgd',
metrics=['accuracy'])

checkpoint_filepath = 'models/model-ep{epoch:03d}-loss{loss:.3f}-acc{acc:.3f}-val_loss{val_loss:.3f}-val_acc{val_acc:.3f}.h5'
checkpoint = ModelCheckpoint(checkpoint_filepath, monitor='acc', verbose=1, save_best_only=True, mode='max')

metrics = Metrics()

history = model.fit(x_train, y_train,
epochs=10,
batch_size=32,
validation_data=(x_test, y_test),
callbacks=[metrics,checkpoint])
# print(history.history.keys())
plt.plot(history.history['acc'],'b--')
plt.plot(history.history['val_acc'],'y-')
plt.plot(metrics.val_f1s,'r.-')
plt.plot(metrics.val_precisions,'g-')
plt.plot(metrics.val_recalls,'c-')

plt.title('DenseNet201 model report')
plt.ylabel('evaluation')
plt.xlabel('epoch')
plt.legend(['train_accuracy', 'val_accuracy','val_f1-score','val_precisions','val_recalls'], loc='lower right')
plt.savefig('results/result_acc.png')
plt.show()
————————————————
版权声明：本文为CSDN博主「姚贤贤」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/u011311291/java/article/details/81511913