Question

我试图对两个对象进行分类。我想从evaluate.py脚本中获得准确度和交叉熵。

这是我正在尝试的代码。

evaluate.py (by tensorflow for poets)
#!/usr/bin/python
#
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 
implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import sys
import argparse

import numpy as np
import PIL.Image as Image
import tensorflow as tf

import scripts.retrain as retrain
from scripts.count_ops import load_graph


def evaluate_graph(graph_file_name):
    with load_graph(graph_file_name).as_default() as graph:
    ground_truth_input = tf.placeholder(
        tf.float32, [None, 5], name='GroundTruthInput')

    image_buffer_input = graph.get_tensor_by_name('input:0')
    final_tensor = graph.get_tensor_by_name('final_result:0')
    accuracy, _ = retrain.add_evaluation_step(final_tensor, ground_truth_input)

    logits = graph.get_tensor_by_name("final_training_ops/Wx_plus_b/add:0")
    xent = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
        labels=ground_truth_input,
        logits=logits))

#image_dir = 'tf_files/flower_photos'
image_dir = 'tf_files/test_images'
testing_percentage = 10
validation_percentage = 10
validation_batch_size = 100
category = 'testing'

image_lists = retrain.create_image_lists(
    image_dir, testing_percentage,
    validation_percentage)
class_count = len(image_lists.keys())

ground_truths = []
filenames = []

for label_index, label_name in enumerate(image_lists.keys()):
    for image_index, image_name in enumerate(image_lists[label_name][category]):
        image_name = retrain.get_image_path(
            image_lists, label_name, image_index, image_dir, category)
        ground_truth = np.zeros([1, class_count], dtype=np.float32)
        ground_truth[0, label_index] = 1.0
        ground_truths.append(ground_truth)
        filenames.append(image_name)

accuracies = []
xents = []
with tf.Session(graph=graph) as sess:
    for filename, ground_truth in zip(filenames, ground_truths):
        image = Image.open(filename).resize((224, 224), Image.ANTIALIAS)
        image = np.array(image, dtype=np.float32)[None, ...]
        image = (image - 128) / 128.0

        feed_dict = {
            image_buffer_input: image,
            ground_truth_input: ground_truth}

        eval_accuracy, eval_xent = sess.run([accuracy, xent], feed_dict)

        accuracies.append(eval_accuracy)
        xents.append(eval_xent)

return np.mean(accuracies), np.mean(xents)


if __name__ == "__main__":
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    accuracy, xent = evaluate_graph(*sys.argv[1:])
    print('Accuracy: %g' % accuracy)
    print('Cross Entropy: %g' % xent)

但是当我运行上面的脚本进行预测时，我收到以下错误：

ValueError：无法为Tensor提供形状值（1,224,224） u'input：0'，其形状为'（1,224,224,3）'

如何解决此错误？

Answer 1

您似乎正在将灰度图像输入到输入占位符中。灰度图像只有1个通道，因此形状(224, 224, )（尺寸1的尺寸被省略），而您正在尝试的预训练网络需要具有3个通道和形状的RGB图像(224, 224, 3)

如果您的图片是实际的RGB，则可能会出现错误：

image = np.array(image, dtype=np.float32)[None, ...]

这个索引：[None, ...]似乎没必要。

如果您的图像实际上是灰度图像，您可以使用PIL.convert()将它们转换为RGB格式（一个通道将重复3次）：

image = image.convert("RGB")

虽然使用频道复制运行3频道CNN效率低（对同一数据执行3次计算）并且可能比彩色图像表现更差，但这应该运行脚本并使您快速进入正轨。

为TensorFlow方法传递错误的参数

1 个答案: