Question

我正在通过函数构建图形，并尝试提取变量的值以添加进一步的操作。我写的函数的一部分如下所示：

def build(self, save_path=None, save_name=None):
    g = tf.Graph()
    with g.as_default():
        init_op = tf.initialize_all_variables()
        images = tf.placeholder(tf.float32, shape=[None, 300, 300, 3], name='input')
        with tf.variable_scope('conv1_'):
            conv11 = self.conv_relu(images, kernel_shape=[3, 3, 3, 64], bias_shape=64, name='c1')
            conv12 = self.conv_relu(conv11, kernel_shape=[3, 3, 64, 64], bias_shape=64, name='c2')

        pool1 = tf.nn.max_pool(conv12, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')


        with tf.variable_scope('conv2_'):
            conv21 = self.conv_relu(pool1, kernel_shape=[3, 3, 64, 128], bias_shape=128, name='c1')
            conv22 = self.conv_relu(conv21, kernel_shape=[3, 3, 128, 128], bias_shape=128, name='c2')

        pool2 = tf.nn.max_pool(conv22, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2')


        with tf.variable_scope('conv3_'):
            conv31 = self.conv_relu(pool2, kernel_shape=[3, 3, 128, 256], bias_shape=256, name='c1')
            conv32 = self.conv_relu(conv31, kernel_shape=[3, 3, 256, 256], bias_shape=256, name='c2')
            conv33 = self.conv_relu(conv32, kernel_shape=[3, 3, 256, 256], bias_shape=256, name='c3')

        pool3 = tf.nn.max_pool(conv33, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool3')

        with tf.variable_scope('conv4_'):
            conv41 = self.conv_relu(pool3, kernel_shape=[3, 3, 256, 512], bias_shape=512, name='c1')
            conv42 = self.conv_relu(conv41, kernel_shape=[3, 3, 512, 512], bias_shape=512, name='c2')
            conv43 = self.conv_relu(conv42, kernel_shape=[3, 3, 512, 512], bias_shape=512, name='c3')

        pool4 = tf.nn.max_pool(conv43, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool4')

        with tf.variable_scope('conv5_'):
            conv51 = self.conv_relu(pool4, kernel_shape=[3, 3, 512, 512], bias_shape=512, name='c1')
            conv52 = self.conv_relu(conv51, kernel_shape=[3, 3, 512, 512], bias_shape=512, name='c2')
            conv53 = self.conv_relu(conv52, kernel_shape=[3, 3, 512, 512], bias_shape=512, name='c3')

        pool5 = tf.nn.max_pool(conv53, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool5')

        pool5_shape = tf.shape(pool5)

        pool5_reshaped = tf.reshape(pool5, shape=[pool5_shape[0], -1], name='pool5_reshaped')

        weight_rows = pool5_shape[1] * pool5_shape[2] * pool5_shape[3]
    sess = tf.Session(graph=g)
    inp = np.zeros(shape=(2, 300, 300, 3))
    print(inp.shape)
    sess.run(init_op)
    print(sess.run(weight_rows, feed_dict={images:inp}))
    sess.close()

在print(sess.run(weight_rows, feed_dict={images:inp}))行，我收到以下错误：

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value conv5_/biasesc3
     [[Node: conv5_/biasesc3/read = Identity[T=DT_FLOAT, _class=["loc:@conv5_/biasesc3"], _device="/job:localhost/replica:0/task:0/cpu:0"](conv5_/biasesc3)]]

之前在会话中运行init_op操作时出现此错误的原因是什么？究竟这是如何工作的以及我在这里做错了什么？

Answer 1

在声明所有变量后，您需要定义init_op（即调用tf.initialize_all_variables（））。通过tf.get_variable或tf.Variable创建变量将其置于GLOBAL_VARIABLES集合中（除非另有collections kwarg指定）。 tf.initialize_all_variables()查看此集合并创建一个初始化列出的变量的操作。

要查看GLOBAL_VARIABLES集合，您可以将tf.get_collection与tf.GraphKeys.GLOBAL_VARIABLES一起用作参数。

TL; DR 在创建图表后放置init_op = tf.initialize_all_variables()。

变量初始化有问题

1 个答案: