TensorBoard 图表中 "n tensors" 的含义是什么？

Question

我正在阅读 TensorFlow 教程代码 mnist_deep.py 并保存图表。

范围 fc1 的输出应该具有 [-1, 1024] 的形状。但它在 TensorBoard 的图表中 2 tensors。

TensorBoard 图中 "n tensors" 的含义是什么？

  # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
  # is down to 7x7x64 feature maps -- maps this to 1024 features.
  with tf.name_scope('fc1'):
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

  # Dropout - controls the complexity of the model, prevents co-adaptation of
  # features.
  with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

Answer 1

应该是说Relu的输出tensor在Droupout节点中使用了两次。如果您尝试展开它，您应该会看到输入进入两个不同的节点。

TensorBoard 图表中 "n tensors" 的含义是什么？

What's meaning of "n tensors" in TensorBoard graph?

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