我正在尝试使用tensorflow记录精确度和召回率的摘要统计数据，以便与tensor-board一起使用，代码如下所示。
我已经添加了全局和本地变量初始化器，但仍然抛出错误，告诉我’recall’的值未初始化。
有人知道为什么这仍然会抛出错误吗？
错误信息在代码块下方
def classifier_graph(x, y, learning_rate=0.1): with tf.name_scope('classifier'): with tf.name_scope('model'): W = tf.Variable(tf.zeros([xdim, ydim]), name='W') b = tf.Variable(tf.zeros([ydim]), name='b') y_ = tf.matmul(x, W) + b with tf.name_scope('cross_entropy'): diff = tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=y_) cross_entropy = tf.reduce_mean(diff) summary = tf.summary.scalar('cross_entropy', cross_entropy) with tf.name_scope('train'): #cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_), reduction_indices=[1]), name='cross_entropy') train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy) # minimise cross_entropy via GD #with tf.name_scope('init'): #init = tf.global_variables_initializer() #local_init = tf.local_variables_initializer() #init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) with tf.name_scope('init'): init = tf.global_variables_initializer() init_l = tf.local_variables_initializer() with tf.name_scope('metrics'): recall = tf.metrics.recall(y, y_ ) precision = tf.metrics.precision(y, y_) v_rec = tf.summary.scalar('recall', recall) v_prec = tf.summary.scalar('precision', precision) metrics = tf.summary.merge_all() return [W, b, y_, cross_entropy, train_step, init, init_l, metrics]def train_classifier(insamples, outsamples, batch_size, iterations, feature_set_index=1, model=None, device): x = tf.placeholder(tf.float32, [None, xdim], name='x') # None indications arbitrary first dimension y = tf.placeholder(tf.float32, [None, ydim], name='y') W, b, y_, cross_entropy, train_step, init, init_l, metrics = classifier_graph(x, y) with tf.Session(config=config) as sess, tf.device(device): sess.run(init) sess.run(init_l) file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph()) t = 0 while t < iterations: t += 1 _, err, metrics_str = sess.run([train_step, cross_entropy, metrics], feed_dict={x: batch_x, y: batch_y }) all_err.append(err) file_writer.add_summary(metrics_str,t) return 'Done'具体错误信息如下：
FailedPreconditionError (see above for traceback): Attempting to use uninitialized value recall/true_positives/count [[Node: recall/true_positives/count/read = Identity[T=DT_FLOAT, _class=["loc:@recall/true_positives/count"], _device="/job:localhost/replica:0/task:0/gpu:0"](recall/true_positives/count)]]谢谢！
编辑：
在按照@Ishant Mrinal 建议的更改后，我遇到了之前遇到过的错误：
InvalidArgumentError (see above for traceback): tags and values not the same shape: [] != [2] (tag 'precision_1')这表明精确度张量的形状与其他不同，对于交叉熵或召回率不会抛出此错误。
回答：
这是因为两个初始化行的位置，将这两行移动到train_classifier函数中。错误表明某些变量未初始化。
def train_classifier(...): ... init = tf.global_variables_initializer() init_l = tf.local_variables_initializer() with tf.Session(config=config) as sess, tf.device(device): sess.run(init) sess.run(init_l)