Question

我是深度学习领域的初学者，并且我正在尝试测量保存的tensorflow模型[cifar10上的Resnet]（使用低级tensorflow API的Pb文件）的准确性。我正在尝试完成以下任务：

加载图形
[使用keras]带来测试数据集
使用加载的图形针对测试数据集运行会话
使用混淆矩阵手动测量准确性。

问题在于准确性是否符合法律规定，因此，我确信我在会话运行中做错了什么。

代码如下：

import tensorflow as tf
import numpy as np
import keras
from keras.datasets import cifar10


def load_graph(frozen_graph_filename):
    # We load the protobuf file from the disk and parse it to retrieve the 
    # unserialized graph_def
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we import the graph_def into a new Graph and returns it 
    with tf.Graph().as_default() as graph:
        # The name var will prefix every op/nodes in your graph
        # Since we load everything in a new graph, this is not needed
        tf.import_graph_def(graph_def, name="prefix")
    return graph
graph = load_graph("./model_original.pb")
# We can verify that we can access the list of operations in the graph
#for op in graph.get_operations():
#    print(op.name)

x = graph.get_tensor_by_name('prefix/net_input:0')
y = graph.get_tensor_by_name('prefix/net_output:0')

print(x.shape)
print(y.shape)

# The data, split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
#  launch a Session
with tf.Session(graph=graph) as sess:
  y_out = sess.run(y, feed_dict={x: x_test})

然后尝试查看准确性，如下所示：

from mlxtend.evaluate import confusion_matrix
cm = confusion_matrix(np.argmax(y_test, axis=1),np.argmax(y_out, axis=1))
def accuracy(confusion_matrix):
    diagonal_sum = confusion_matrix.trace()
    sum_of_all_elements = confusion_matrix.sum()
    return diagonal_sum / sum_of_all_elements


accuracy(cm) * 100

在tensorflow上访问保存的模型（Pb文件）的准确性

0 个答案: