传递TensorFlow方法的错误参数

Question

我想分类两个对象。我想从evaluate.py脚本获得精度和交叉熵。

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) 

然而，当我运行上面的脚本进行预测，我得到以下错误：

ValueError: Cannot feed value of shape (1, 224, 224) for Tensor u'input:0', which has shape '(1, 224, 224, 3)'

我怎样才能解决这个问题？

Answer 1

看起来你正在将灰度图像输入到输入占位符中。灰度图像仅具有1个通道，因此形状(224, 224,)（省略了尺寸1的尺寸），而你正在试图预训练的网络要求与3个通道RGB图像和形状(224, 224, 3)

如果图像是actualy RGB，则可能有错误在这里：

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

这个索引：[None, ...]似乎并不必要。

如果你的图片实际上是灰度，可以将它们转换成使用PIL.convert() RGB格式（一个通道将被重复3次）：

image = image.convert("RGB") 

虽然与渠道重复运行3通道CNN是无效的（计算执行相同的数据3次），并可能会比彩色图像更糟糕，这应该运行脚本，并会让你快速跟踪。