'''
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()
keras 可视化模型结果,f1_score,recall,acc,acc_valid,checkpoint
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转载自blog.csdn.net/u011311291/article/details/81511913
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