Keras回调以跟踪数字一致性
numeraicb的Python项目详细描述
在培训期间计算模型一致性的keras回调 每个时代。回调打印一致性，并在 在consistency键下训练历史的每个纪元的结束。
用法
下面是一个用法示例：
import pandas as pd
from numeraicb import Consistency
from keras.models import Sequential
from keras.layers.core import Dense
train = pd.read_csv('numerai_training_data.csv')
tourn = pd.read_csv('numerai_tournament_data.csv')
validation = tourn[tourn.data_type == 'validation']
features = ['feature{}'.format(i) for i in range(1, 51)]
X = train[features].values
Y = train.target.values
X_validation = validation[features].values
Y_validation = validation.target.values
model = Sequential()
model.add(Dense(30, kernel_initializer='uniform', input_dim=X.shape[1], activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adamax', loss='binary_crossentropy')
cb = Consistency(tourn)
# Now your models consistency will be printed at each epoch
history = model.fit(X, Y, callbacks=[cb], validation_data=(X_validation, Y_validation))
# Consistency is stored in the history as well
for epoch, consistency in enumerate(history.history['consistency']):
print('consistency at epoch {}: {:.2%}'.format(epoch, consistency))
欢迎加入QQ群-->： 979659372
