0f7616bdade868fc1dd8c90adb6f7789176305a6,research/audioset/vggish/vggish_train_demo.py,,main,#Any#,128

Before Change

      // Add training ops.
      with tf.variable_scope("train"):
        global_step = tf.Variable(
            0, name="global_step", trainable=False,
            collections=[tf.GraphKeys.GLOBAL_VARIABLES,
                         tf.GraphKeys.GLOBAL_STEP])

        // Labels are assumed to be fed as a batch multi-hot vectors, with
        // a 1 in the position of each positive class label, and 0 elsewhere.
        labels = tf.placeholder(

After Change

      // Add a fully connected layer with 100 units. Add an activation function
      // to the embeddings since they are pre-activation.
      num_units = 100
      fc = slim.fully_connected(tf.nn.relu(embeddings), num_units)

      // Add a classifier layer at the end, consisting of parallel logistic
      // classifiers, one per class. This allows for multi-class tasks.
      logits = slim.fully_connected(

Instances