c48128868e7edcbf354ff79fa29fa0b79740b2f4,scripts/tf_cnn_benchmarks/benchmark_cnn.py,BenchmarkCNN,_build_fetches,#BenchmarkCNN#Any#Any#Any#Any#Any#Any#Any#Any#Any#,2233

Before Change

        if self.params.summary_verbosity >= 2:
          // Histogram of log values of all non-zero gradients.
          all_grads = []
          for grad, var in avg_grads:
            all_grads.append(tf.reshape(grad, [-1]))
          grads = tf.abs(tf.concat(all_grads, 0))
          // exclude grads with zero values.
          indices_for_non_zero_grads = tf.where(tf.not_equal(grads, 0))
          log_grads = tf.reshape(
              tf.log(tf.gather(grads, indices_for_non_zero_grads)), [-1])
          tf.summary.histogram("log_gradients", log_grads)

        if self.params.summary_verbosity >= 3:
          for grad, var in avg_grads:

After Change

                                          self.model, examples_per_step)

        if gradient_clip is not None:
          with tf.name_scope("clip_gradients"):
            clipped_grads = [(tf.clip_by_value(grad, -gradient_clip,
                                               +gradient_clip), var)
                             for grad, var in avg_grads]
        else:
          clipped_grads = avg_grads

        learning_rate = tf.identity(learning_rate, name="learning_rate_tensor")

Instances