Question

我正在尝试将预测图像保存在我使用Tensorflow编写的CNN网络上。在我的代码y_pred_cls中包含我预测的标签，而y_pred_cls是一个尺寸为1 x批量大小的张量。现在，我想迭代y_pred_cls作为一个数组，并创建一个包含pred class，true class和一些索引号的文件名，然后找出与预测标签相关的图像，并使用imsave保存为图像。

with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_writer.add_graph(sess.graph)



print("{} Start training...".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
print("{} Open Tensorboard at --logdir {}".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'), tensorboard_dir))

for epoch in range(FLAGS.num_epochs):
    print("{} Epoch number: {}".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'), epoch + 1))
    step = 1

    # Start training
    while step < train_batches_per_epoch:
        batch_xs, batch_ys = train_preprocessor.next_batch(FLAGS.batch_size)
        opt, train_acc = sess.run([optimizer, accuracy], feed_dict={x: batch_xs, y_true: batch_ys})

        # Logging
        if step % FLAGS.log_step == 0:
            s = sess.run(sum, feed_dict={x: batch_xs, y_true: batch_ys})
            train_writer.add_summary(s, epoch * train_batches_per_epoch + step)

        step += 1

    # Epoch completed, start validation
    print("{} Start validation".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
    val_acc = 0.
    val_count = 0
    cm_running_total = None

    for _ in range(val_batches_per_epoch):
        batch_tx, batch_ty = val_preprocessor.next_batch(FLAGS.batch_size)
        acc, loss , conf_m= sess.run([accuracy, cost, tf.confusion_matrix(y_true_cls, y_pred_cls, FLAGS.num_classes)],
                                      feed_dict={x: batch_tx, y_true: batch_ty})



        if cm_running_total is None:
            cm_running_total = conf_m
        else:
            cm_running_total += conf_m


        val_acc += acc
        val_count += 1

    val_acc /= val_count

    s = tf.Summary(value=[
        tf.Summary.Value(tag="validation_accuracy", simple_value=val_acc),
        tf.Summary.Value(tag="validation_loss", simple_value=loss)
    ])

    val_writer.add_summary(s, epoch + 1)
    print("{} -- Training Accuracy = {:.4%} -- Validation Accuracy = {:.4%} -- Validation Loss = {:.4f}".format(
        datetime.now().strftime('%Y-%m-%d %H:%M:%S'), train_acc, val_acc, loss))

    # Reset the dataset pointers
    val_preprocessor.reset_pointer()
    train_preprocessor.reset_pointer()

    print("{} Saving checkpoint of model...".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))

    # save checkpoint of the model
    checkpoint_path = os.path.join(checkpoint_dir, 'model_epoch.ckpt' + str(epoch+1))
    save_path = saver.save(sess, checkpoint_path)
    print("{} Model checkpoint saved at {}".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'), checkpoint_path))

batch_tx，batch_ty分别是我的RGB数据和标签。

提前致谢。

Answer 1

从张量中提取数据到python变量使用

from PIL import Image img = Image.fromarray(data, 'RGB') img.save('name.png')

这将为您提供一个单热矢量标签的数组或一个标量标签的int变量。

要将数组保存到图像，可以使用PIL库

其余的应该是直截了当的，

从batch_tx，batch_ty和y_pred_cls张量中提取数据
遍历每个三元组
使用当前name = str(y)+'_'+str(y_hat)
创建recursive cte
保存您的图片

如果您在应用这些步骤时遇到问题，我可以为您提供进一步的帮助

迭代张量作为数组Tensorflow

1 个答案: