Comment calculer Récepteur (ROC) et de l'ASC dans keras?

En raison que vous ne pouvez pas calculer ROC&de l'ASC de mini-lots, vous ne pouvez la calculer sur la fin d'une époque. Il existe une solution de jamartinh, je patch les codes ci-dessous pour plus de commodité:

from sklearn.metrics import roc_auc_score
from keras.callbacks import Callback
class roc_callback(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

model.fit(X_train, y_train, validation_data=(X_test, y_test), callbacks=[roc_callback(training_data=(X_train, y_train),validation_data=(X_test, y_test))])

Plus piratable chemin à l'aide tf.contrib.metrics.streaming_auc:

import numpy as np
import tensorflow as tf
from sklearn.metrics import roc_auc_score
from sklearn.datasets import make_classification
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from keras.callbacks import Callback, EarlyStopping
# define roc_callback, inspired by https://github.com/keras-team/keras/issues/6050#issuecomment-329996505
def auc_roc(y_true, y_pred):
# any tensorflow metric
value, update_op = tf.contrib.metrics.streaming_auc(y_pred, y_true)
# find all variables created for this metric
metric_vars = [i for i in tf.local_variables() if 'auc_roc' in i.name.split('/')[1]]
# Add metric variables to GLOBAL_VARIABLES collection.
# They will be initialized for new session.
for v in metric_vars:
tf.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, v)
# force to update metric values
with tf.control_dependencies([update_op]):
value = tf.identity(value)
return value
# generation a small dataset
N_all = 10000
N_tr = int(0.7 * N_all)
N_te = N_all - N_tr
X, y = make_classification(n_samples=N_all, n_features=20, n_classes=2)
y = np_utils.to_categorical(y, num_classes=2)
X_train, X_valid = X[:N_tr, :], X[N_tr:, :]
y_train, y_valid = y[:N_tr, :], y[N_tr:, :]
# model & train
model = Sequential()
model.add(Dense(2, activation="softmax", input_shape=(X.shape[1],)))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy', auc_roc])
my_callbacks = [EarlyStopping(monitor='auc_roc', patience=300, verbose=1, mode='max')]
model.fit(X, y,
validation_split=0.3,
shuffle=True,
batch_size=32, nb_epoch=5, verbose=1,
callbacks=my_callbacks)
# # or use independent valid set
# model.fit(X_train, y_train,
#           validation_data=(X_valid, y_valid),
#           batch_size=32, nb_epoch=5, verbose=1,
#           callbacks=my_callbacks)

OriginalL'auteur Tom

Vous pouvez envelopper tf.métriques (par exemple tf.metrics.auc) dans keras.métriques.

De ma réponse à Comment utiliser TensorFlow métriques dans Keras:

def as_keras_metric(method):
import functools
from keras import backend as K
import tensorflow as tf
@functools.wraps(method)
def wrapper(self, args, **kwargs):
""" Wrapper for turning tensorflow metrics into keras metrics """
value, update_op = method(self, args, **kwargs)
K.get_session().run(tf.local_variables_initializer())
with tf.control_dependencies([update_op]):
value = tf.identity(value)
return value
return wrapper

D'utilisation de base:

auc_roc = as_keras_metric(tf.metrics.auc)
recall = as_keras_metric(tf.metrics.recall)
...

Compiler le keras modèle:

model.compile(..., metrics=[auc_roc])

OriginalL'auteur Christian Skjødt

La solution suivante a fonctionné pour moi:

import tensorflow as tf
from keras import backend as K
def auc(y_true, y_pred):
auc = tf.metrics.auc(y_true, y_pred)[1]
K.get_session().run(tf.local_variables_initializer())
return auc
model.compile(loss="binary_crossentropy", optimizer='adam', metrics=[auc])

OriginalL'auteur B. Kanani