Question

我尝试以图像格式显示conv_layer的权重。

以下是我的代码。

def get_scope_variable(scope_name, var, shape=None):
    with tf.variable_scope(scope_name) as scope:
        try:
            v = tf.get_variable(var, shape)
        except ValueError:
            scope.reuse_variables()
            v = tf.get_variable(var)

    return v

# Add convolution layer
def conv_layer(input, size_in, size_out, name="conv"):
    with tf.name_scope(name):
        w = tf.Variable(tf.truncated_normal([5, 5, size_in, size_out], stddev=0.1), name="W")
        b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
        conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="SAME")
        act = tf.nn.relu(conv + b)

    tf.summary.histogram("weights", w)
    tf.summary.histogram("biases", b)
    tf.summary.histogram("activations", act)

    return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")

def mnist_model(learning_rate, use_two_conv, use_two_fc, hparam):
  tf.reset_default_graph()
  sess = tf.Session()

  # Setup placeholders, and reshape the data
  x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
  x_image = tf.reshape(x, [-1, 28, 28, 1])
  tf.summary.image('input', x_image, 3)
  y = tf.placeholder(tf.float32, shape=[None, 10], name="labels")

  if use_two_conv:
    with tf.variable_scope('conv', reuse=True):
        conv1 = conv_layer(x_image, 1, 32, "conv1")

    conv_out = conv_layer(conv1, 32, 64, "conv2")
  else:
    conv1 = conv_layer(x_image, 1, 64, "conv")
    conv_out = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")

并尝试将重量变量命名为＆＃39; W＆＃39;

  W_conv1 = get_scope_variable('conv', 'W')

但是这个试验给了我错误信息。

ValueError：变量conv / W不存在，或者未使用tf.get_variable（）创建。你的意思是在VarScope中设置reuse = None吗？

请帮帮我。我几乎被困了一天。

张量板示例的完整代码链接：https://github.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial/blob/master/mnist.py#L31

如何在tensorboard上显示conv_layer的权重？

0 个答案: