def host_call_fn(gs, g_loss, d_loss, real_audio, generated_audio):
"""Training host call. Creates scalar summaries for training metrics.
This function is executed on the CPU and should not directly reference
any Tensors in the rest of the `model_fn`. To pass Tensors from the
model to the `metric_fn`, provide as part of the `host_call`. See
https://www.tensorflow.org/api_docs/python/tf/contrib/tpu/TPUEstimatorSpec
for more information.
Arguments should match the list of `Tensor` objects passed as the second
element in the tuple passed to `host_call`.
Args:
gs: `Tensor with shape `[batch]` for the global_step
g_loss: `Tensor` with shape `[batch]` for the generator loss.
d_loss: `Tensor` with shape `[batch]` for the discriminator loss.
real_audio: `Tensor` with shape `[batch, 8192, 1]`
generated_audio: `Tensor` with shape `[batch, 8192, 1]`
Returns:
List of summary ops to run on the CPU host.
"""
gs = gs[0]
with summary.create_file_writer(FLAGS.model_dir).as_default():
with summary.always_record_summaries():
summary.scalar('g_loss', g_loss, step=gs)
summary.scalar('d_loss', d_loss, step=gs)
summary.audio('real_audio', real_audio, sample_rate=_FS, max_outputs=10, step=gs)
summary.audio('generated_audio', generated_audio, sample_rate=_FS, max_outputs=10, step=gs)
return summary.all_summary_ops()
def host_call_fn(gs,
loss,
lr,
mix=None,
gt_sources=None,
est_sources=None):
"""Training host call. Creates scalar summaries for training metrics.
This function is executed on the CPU and should not directly reference
any Tensors in the rest of the `model_fn`. To pass Tensors from the
model to the `metric_fn`, provide as part of the `host_call`. See
https://www.tensorflow.org/api_docs/python/tf/contrib/tpu/TPUEstimatorSpec
for more information.
Arguments should match the list of `Tensor` objects passed as the second
element in the tuple passed to `host_call`.
Args:
gs: `Tensor with shape `[batch]` for the global_step
loss: `Tensor` with shape `[batch]` for the training loss.
lr: `Tensor` with shape `[batch]` for the learning_rate.
input: `Tensor` with shape `[batch, mix_samples, 1]`
gt_sources: `Tensor` with shape `[batch, sources_n, output_samples, 1]`
est_sources: `Tensor` with shape `[batch, sources_n, output_samples, 1]`
Returns:
List of summary ops to run on the CPU host.
"""
gs = gs[0]
with summary.create_file_writer(
model_config["model_base_dir"] + os.path.sep +
str(model_config["experiment_id"])).as_default():
with summary.always_record_summaries():
summary.scalar('loss', loss[0], step=gs)
summary.scalar('learning_rate', lr[0], step=gs)
if gs % 10000 == 0:
with summary.record_summaries_every_n_global_steps(
model_config["audio_summaries_every_n_steps"]):
summary.audio('mix',
mix,
model_config['expected_sr'],
max_outputs=model_config["num_sources"])
for source_id in range(gt_sources.shape[1].value):
summary.audio('gt_sources_{source_id}'.format(
source_id=source_id),
gt_sources[:, source_id, :, :],
model_config['expected_sr'],
max_outputs=model_config["num_sources"])
summary.audio('est_sources_{source_id}'.format(
source_id=source_id),
est_sources[:, source_id, :, :],
model_config['expected_sr'],
max_outputs=model_config["num_sources"])
return summary.all_summary_ops()