batch_size=batch_size,
shuffling=False,
cache_path=None)
for lr in learning_rates:
for opt in optimizer_list:
#### IF SOMETHING BREAKS PICKUP WHERE WE LEFT OFF
if model_count < 152:
print(f'Skipping: {model_count}')
model_count += 1
continue
####
if lr == 'custom':
lr = CustomSchedule()
if opt == 'rmsprop':
opt_m = tf.keras.optimizers.RMSprop(lr)
elif opt == 'adam':
opt_m = tf.keras.optimizers.Adam(lr)
elif opt == 'adagrad':
opt_m = tf.keras.optimizers.Adagrad(lr)
elif opt == 'adamax':
opt_m = tf.keras.optimizers.Adamax(lr)
elif opt == 'sgd':
opt_m = tf.keras.optimizers.SGD(lr)
# ### Build Train/ Validation => Model Ready Input
tf_train_ds = create_dataset(tf_train_ds,
batch_size=batch_size,
shuffling=True,
cache_path=None)
tf_valid_ds = create_dataset(tf_valid_ds,
batch_size=batch_size,
shuffling=False,
cache_path=None)
# ### Custom Learning Rate Scheduler
#Example
plt.style.use('ggplot')
schedule = CustomSchedule()
plt.plot(schedule(tf.range(25000, dtype=tf.float32)))
plt.xlabel("Steps")
plt.ylabel("Learning rate")
# ### Setup Callbacks for Tensorboard
# +
start_profile_batch = steps + 10
stop_profile_batch = start_profile_batch + 100
profile_range = f"{start_profile_batch},{stop_profile_batch}"
log_path = log_dir + "/" + datetime.datetime.now().strftime(
"%Y-%m-%d_%H:%M:%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=log_path,
# ### Build Train/ Validation => Model Ready Input
tf_train_ds = create_dataset(tf_train_ds,
batch_size=batch_size,
shuffling=True,
cache_path=None)
tf_valid_ds = create_dataset(tf_valid_ds,
batch_size=batch_size,
shuffling=False,
cache_path=None)
# ### Custom Learning Rate Scheduler
#Example
plt.style.use('ggplot')
schedule = CustomSchedule()
plt.plot(schedule(tf.range(25000, dtype=tf.float32)))
plt.xlabel("Steps")
plt.ylabel("Learning rate")
# ### Setup Callbacks for Tensorboard
# +
start_profile_batch = steps + 10
stop_profile_batch = start_profile_batch + 100
profile_range = f"{start_profile_batch},{stop_profile_batch}"
log_path = log_dir + "/" + datetime.datetime.now().strftime(
"%Y-%m-%d_%H:%M:%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=log_path,