Question

好的，我通过一个样本来预测特定产品质量在Tensorflow上是好还是坏。我的最后一段代码如下所示：

# Merge summaries for TensorBoard
merged_summaries = tf.summary.merge_all()

with tf.Session() as sess:

    log_directory = create_log_directory()
    summary_writer = tf.summary.FileWriter(log_directory, sess.graph)

    tf.global_variables_initializer().run()

    for i in range(epochs):
        average_cost = 0
        number_of_batches = int(len(X_train) / batch_size)
        for start, end in zip(range(0, len(X_train), batch_size), range(batch_size, len(X_train), batch_size)):
            feed = {X: X_train[start:end], y: y_train[start:end]}
            sess.run(training_step, feed_dict=feed)
            # Compute average loss
            average_cost += sess.run(cost, feed_dict=feed) / number_of_batches
        if i % epochs_to_print == 0:
            feed = {X: X_test, y: y_test}
            result = sess.run([merged_summaries, accuracy], feed_dict=feed)
            summary = result[0]
            current_accuracy = result[1]
            summary_writer.add_summary(summary, i)
            print("Epoch: {:4d}, average cost = {:.3f}, accuracy = {:.3f}".format(i+1, average_cost, current_accuracy))

    print("Final accuracy = {:.3f}".format(sess.run(accuracy, feed_dict={X: X_test, y: y_test})))

它推出了一套很好的10个时期，最高精度为0.527，我认为准确率为52.7％。

Saving summaries to tmp/logs/run_32/
Epoch:    1, average cost = 3.300, accuracy = 0.174
Epoch:  101, average cost = 0.685, accuracy = 0.528
Epoch:  201, average cost = 0.682, accuracy = 0.527
Epoch:  301, average cost = 0.680, accuracy = 0.527
Epoch:  401, average cost = 0.680, accuracy = 0.527
Epoch:  501, average cost = 0.679, accuracy = 0.527
Epoch:  601, average cost = 0.679, accuracy = 0.527
Epoch:  701, average cost = 0.679, accuracy = 0.527
Epoch:  801, average cost = 0.679, accuracy = 0.527
Epoch:  901, average cost = 0.679, accuracy = 0.527
Final accuracy = 0.527

问题在于，现在，我想从（可能）一个numpy数组中仅将一行数据反馈到Tensorflow中以获得预测。我该怎么做呢？我假设它遵循这样的模式：

input =[1.939501945438227,-1.8459679631200792,1.9134581818982566,-0.6741964131111666,-0.5720868389043996,0.3926397708073837,-2.0777995164924112,0.03405362776450469,0.33621509508483066]
output = <<some function call here>>
print(output)

Answer 1

这取决于您的图表。如果您检查accuracy节点，它可能看起来像

tf.reduce_mean(tf.equal(my_prediction, correct_label))

其中correct_label与y_train有关，并且my_prediction将是某个节点，可能看起来像tf.round(...)取决于您的实现。

你在这里要做的是找到my_prediction，然后你可以使用

进行预测

output = sess.run([my_prediction], feed_dict={X: [input]})

如何在Tensorflow中打印预测

1 个答案: