Keras自定义评估函数
1. 比较一般的自定义函数：
需要注意的是，不能像sklearn那样直接定义，因为这里的y_true和y_pred是张量，不是numpy数组。示例如下：
from keras import backend def rmse(y_true, y_pred): return backend.sqrt(backend.mean(backend.square(y_pred - y_true), axis=-1))
用的时候直接：
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=[rmse])
2. 比较复杂的如AUC函数：
AUC的计算需要整体数据，如果直接在batch里算，误差就比较大，不能合理反映整体情况。这里采用回调函数写法，每个epoch计算一次：
from sklearn.metrics import roc_auc_score class roc_callback(keras.callbacks.Callback): def __init__(self,training_data, validation_data): self.x = training_data[0] self.y = training_data[1] self.x_val = validation_data[0] self.y_val = validation_data[1] def on_train_begin(self, logs={}): return def on_train_end(self, logs={}): return def on_epoch_begin(self, epoch, logs={}): return def on_epoch_end(self, epoch, logs={}): y_pred = self.model.predict(self.x) roc = roc_auc_score(self.y, y_pred) y_pred_val = self.model.predict(self.x_val) roc_val = roc_auc_score(self.y_val, y_pred_val) print('\rroc-auc: %s - roc-auc_val: %s' % (str(round(roc,4)),str(round(roc_val,4))),end=100*' '+'\n') return def on_batch_begin(self, batch, logs={}): return def on_batch_end(self, batch, logs={}): return
调用回调函数示例：
model.fit(X_train, y_train, epochs=10, batch_size=4,
callbacks = [roc_callback(training_data=[X_train, y_train], validation_data=[X_test, y_test])] )
整体示例：
from tensorflow import keras from sklearn import datasets from sklearn import model_selection from sklearn.metrics import roc_auc_score def rmse(y_true, y_pred): return keras.backend.sqrt(keras.backend.mean(keras.backend.square(y_pred - y_true), axis=-1)) class roc_callback(keras.callbacks.Callback): def __init__(self,training_data, validation_data): self.x = training_data[0] self.y = training_data[1] self.x_val = validation_data[0] self.y_val = validation_data[1] def on_train_begin(self, logs={}): return def on_train_end(self, logs={}): return def on_epoch_begin(self, epoch, logs={}): return def on_epoch_end(self, epoch, logs={}): y_pred = self.model.predict(self.x) roc = roc_auc_score(self.y, y_pred) y_pred_val = self.model.predict(self.x_val) roc_val = roc_auc_score(self.y_val, y_pred_val) print('\rroc-auc: %s - roc-auc_val: %s' % (str(round(roc,4)),str(round(roc_val,4))),end=100*' '+'\n') return def on_batch_begin(self, batch, logs={}): return def on_batch_end(self, batch, logs={}): return X, y = datasets.make_classification(n_samples=100, n_features=4, n_classes=2, random_state=2018) X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=2018) print("TrainSet", X_train.shape, "TestSet", X_test.shape) model = keras.models.Sequential() model.add(keras.layers.Dense(20, input_shape=(4,), activation='relu')) model.add(keras.layers.Dense(1, activation='sigmoid')) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=[rmse]) model.fit(X_train, y_train, epochs=10, batch_size=4, callbacks = [roc_callback(training_data=[X_train, y_train], validation_data=[X_test, y_test])] )
运行结果：
TrainSet (80, 4) TestSet (20, 4) Epoch 1/10 roc-auc: 0.1604 - roc-auc_val: 0.2738 80/80 [==============================] - 0s - loss: 0.8132 - rmse: 0.5298 Epoch 2/10 roc-auc: 0.4874 - roc-auc_val: 0.619 80/80 [==============================] - 0s - loss: 0.7432 - rmse: 0.5049 Epoch 3/10 roc-auc: 0.7715 - roc-auc_val: 0.9643 80/80 [==============================] - 0s - loss: 0.6821 - rmse: 0.4807 Epoch 4/10 roc-auc: 0.9602 - roc-auc_val: 1.0 80/80 [==============================] - 0s - loss: 0.6268 - rmse: 0.4560 Epoch 5/10 roc-auc: 0.9842 - roc-auc_val: 1.0 80/80 [==============================] - 0s - loss: 0.5747 - rmse: 0.4301 Epoch 6/10 roc-auc: 0.9956 - roc-auc_val: 1.0 80/80 [==============================] - 0s - loss: 0.5230 - rmse: 0.4025 Epoch 7/10 roc-auc: 0.9975 - roc-auc_val: 1.0 80/80 [==============================] - 0s - loss: 0.4743 - rmse: 0.3739 Epoch 8/10 roc-auc: 0.9987 - roc-auc_val: 1.0 80/80 [==============================] - 0s - loss: 0.4289 - rmse: 0.3454 Epoch 9/10 roc-auc: 0.9987 - roc-auc_val: 1.0...] - ETA: 0s - loss: 0.4019 - rmse: 0.3301 80/80 [==============================] - 0s - loss: 0.3830 - rmse: 0.3149 Epoch 10/10 roc-auc: 0.9987 - roc-auc_val: 1.0 80/80 [==============================] - 0s - loss: 0.3424 - rmse: 0.2865
