shithub: opus

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Add tensorboard logging

--- a/dnn/training_tf2/train_lpcnet.py

+++ b/dnn/training_tf2/train_lpcnet.py

@@ -52,6 +52,7 @@

 parser.add_argument('--lr', metavar='<learning rate>', type=float, help='learning rate')

 parser.add_argument('--decay', metavar='<decay>', type=float, help='learning rate decay')

 parser.add_argument('--gamma', metavar='<gamma>', type=float, help='adjust u-law compensation (default 2.0, should not be less than 1.0)')

+parser.add_argument('--logdir', metavar='<log dir>', help='directory for tensorboard log files')

 args = parser.parse_args()

@@ -185,6 +186,13 @@

     grub_sparsify = lpcnet.SparsifyGRUB(2000, 40000, 400, args.grua_size, grub_density)

 model.save_weights('{}_{}_initial.h5'.format(args.output, args.grua_size))

-csv_logger = CSVLogger('training_vals.log')

+

 loader = LPCNetLoader(data, features, periods, batch_size, lpc_out=flag_e2e)

-model.fit(loader, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify, grub_sparsify, csv_logger])

+

+callbacks = [checkpoint, sparsify, grub_sparsify]

+if args.logdir is not None:

+    logdir = '{}/{}_{}_logs'.format(args.logdir, args.output, args.grua_size)

+    tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)

+    callbacks.append(tensorboard_callback)

+

+model.fit(loader, epochs=nb_epochs, validation_split=0.0, callbacks=callbacks)

-- 

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