相关疑难解决方法(0)

TensorFlow有两种方法来评估图的一部分:Session.run在变量列表和Tensor.eval.这两者有区别吗？

我正在使用EstimatorTensorflow 的 API，遇到以下问题。我想检查 f1 分数而不是准确性，当我在训练后评估时，根本没有问题，当我测试时，它要求标准化值，我已经标准化了。

这是我的网络模型（第一部分省略）：

#### architecture omitted #####

predictions = {
        "classes": tf.argmax(input=logits, axis=1),
        "probabilities": tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.cast(labels, tf.float32), logits=tf.cast(logits, tf.float32), name="sigmoid_tensor")
}


if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=labels, logits=logits)

if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
    #optimizer = tf.train.MomentumOptimizer(learning_rate=0.01, momentum=0.96)
    train_op = optimizer.minimize(
                              loss=loss,
                              global_step=tf.train.get_global_step())
    logging_hook = tf.train.LoggingTensorHook({"loss" : loss}, every_n_iter=10)
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op, training_hooks = [logging_hook])


eval_metric_ops = {
    "accuracy": tf.metrics.accuracy(
    labels=tf.argmax(input=labels, axis=1),
    predictions=predictions["classes"]),

    "f1 score" : tf.contrib.metrics.f1_score(
    labels = tf.argmax(input=labels, …

我已经在使用TensorFlow的帮助下实现了Nueral Network模型的分类.但是,我不知道如何通过使用预测分数(准确度)来绘制混淆矩阵.我不是TensorFlow的专家,仍处于学习阶段.在这里,我粘贴了下面的代码,请告诉我如何编写代码以便从以下代码中产生混淆:

# Launch the graph
with tf.Session() as sess:
sess.run(init)

# Set logs writer into folder /tmp/tensorflow_logs
#summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def)

# Training cycle
for epoch in range(training_epochs):
    avg_cost = 0.
    total_batch = int(X_train.shape[0]/batch_size)

    # Loop over total length of batches
    for i in range(total_batch):  
        #picking up random batches from training set of specific size
        batch_xs, batch_ys = w2v_utils.nextBatch(X_train, y_train, batch_size)
        # Fit training using batch data
        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
        # Compute average loss
        avg_cost += sess.run(cost, …