Question

我正在使用Tensorflow for Poets教程对图像进行分类。我使用下面的代码对图像进行分类，但是想要将numpy数组作为图像而不是jpeg，代码必须如何改变？

import tensorflow as tf
import sys

# change this as you see fit
image_path = sys.argv[1]

# Read in the image_data
image_data = tf.gfile.FastGFile(image_path, 'rb').read()

# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line 
                   in tf.gfile.GFile("/tf_files/retrained_labels.txt")]

# Unpersists graph from file
with tf.gfile.FastGFile("/tf_files/retrained_graph.pb", 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='')

with tf.Session() as sess:
    # Feed the image_data as input to the graph and get first prediction
    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')

    predictions = sess.run(softmax_tensor, \
             {'DecodeJpeg/contents:0': image_data})

    # Sort to show labels of first prediction in order of confidence
    top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]

    for node_id in top_k:
        human_string = label_lines[node_id]
        score = predictions[0][node_id]
print('%s (score = %.5f)' % (human_string, score))

image_data = tf.gfile.FastGFile(image_path, 'rb').read() - 如果我没有阅读文件，我想我不需要这个。

predictions = sess.run(softmax_tensor, {'DecodeJpeg/contents:0': image_data}) - 我知道我不需要覆盖feed_dict的这个方面，但我该怎么办呢？

总的来说，我怎样才能确保代表图像的nparray能够正确用于预测？

Answer 1

谢谢大家，我找到了答案：

假设我有一个名为image的大小为（100,132,3）的三维numpy数组。

我所要做的就是使用'DecodeJpeg:0而不是DecodeJpeg/contents:0将其传递到softmax分类器中......

predictions = sess.run(softmax_tensor, {'DecodeJpeg:0': image})

......你有它

对numpy数组图像进行分类

1 个答案: