def main(flags):
# initialize data reader
# optional for what type of layer the network ends with
if len(flags.tconv_dims) == 0:
output_size = flags.fc_filters[-1]
else:
output_size = flags.tconv_dims[-1]
features, labels, train_init_op, valid_init_op = data_reader.read_data(
input_size=flags.input_size,
output_size=output_size - 2 * flags.clip,
x_range=flags.x_range,
y_range=flags.y_range,
cross_val=flags.cross_val,
val_fold=flags.val_fold,
batch_size=flags.batch_size,
shuffle_size=flags.shuffle_size)
# make network
ntwk = network_maker.CnnNetwork(features,
labels,
utils.my_model_fn_tens,
flags.batch_size,
clip=flags.clip,
fc_filters=flags.fc_filters,
tconv_Fnums=flags.tconv_Fnums,
tconv_dims=flags.tconv_dims,
tconv_filters=flags.tconv_filters,
n_filter=flags.n_filter,
n_branch=flags.n_branch,
reg_scale=flags.reg_scale,
learn_rate=flags.learn_rate,
decay_step=flags.decay_step,
decay_rate=flags.decay_rate)
# define hooks for monitoring training
train_hook = network_helper.TrainValueHook(flags.verb_step,
ntwk.loss,
ckpt_dir=ntwk.ckpt_dir,
write_summary=True)
lr_hook = network_helper.TrainValueHook(flags.verb_step,
ntwk.learn_rate,
ckpt_dir=ntwk.ckpt_dir,
write_summary=True,
value_name='learning_rate')
valid_hook = network_helper.ValidationHook(flags.eval_step,
valid_init_op,
ntwk.labels,
ntwk.logits,
ntwk.loss,
ntwk.preconv,
ntwk.preTconv,
ckpt_dir=ntwk.ckpt_dir,
write_summary=True)
# train the network
ntwk.train(train_init_op,
flags.train_step, [train_hook, valid_hook, lr_hook],
write_summary=True)
def main(flags):
# initialize data reader
if len(flags.tconv_dims) == 0:
output_size = flags.fc_filters[-1]
else:
output_size = flags.tconv_dims[-1]
reader = data_reader.DataReader(input_size=flags.input_size,
output_size=output_size,
x_range=flags.x_range,
y_range=flags.y_range,
cross_val=flags.cross_val,
val_fold=flags.val_fold,
batch_size=flags.batch_size,
shuffle_size=flags.shuffle_size)
features, labels, train_init_op, valid_init_op = reader.get_data_holder_and_init_op(
(flags.train_file, flags.valid_file))
# make network
ntwk = network_maker.CnnNetwork(features,
labels,
utils.my_model_fn,
flags.batch_size,
fc_filters=flags.fc_filters,
tconv_dims=flags.tconv_dims,
tconv_filters=flags.tconv_filters,
learn_rate=flags.learn_rate,
decay_step=flags.decay_step,
decay_rate=flags.decay_rate)
# define hooks for monitoring training
train_hook = network_helper.TrainValueHook(flags.verb_step,
ntwk.loss,
ckpt_dir=ntwk.ckpt_dir,
write_summary=True)
lr_hook = network_helper.TrainValueHook(flags.verb_step,
ntwk.learn_rate,
ckpt_dir=ntwk.ckpt_dir,
write_summary=True,
value_name='learning_rate')
valid_hook = network_helper.ValidationHook(flags.eval_step,
valid_init_op,
ntwk.labels,
ntwk.logits,
ntwk.loss,
ckpt_dir=ntwk.ckpt_dir,
write_summary=True)
# train the network
ntwk.train(train_init_op,
flags.train_step, [train_hook, valid_hook, lr_hook],
write_summary=True)
def get_hook_list(flags,
ntwk,
valid_init_op,
losses,
loss_names,
forward_or_backward_str,
detail_train_loss=True,
summary_op=None):
hook_list = []
if (detail_train_loss):
print("Losses:", losses)
print("loss_name", loss_names)
for cnt, (loss, name) in enumerate(zip(losses, loss_names)):
print("forward_or_backward_str:", forward_or_backward_str)
print("name:", name)
print("loss:", loss)
hook_list.append(
network_helper.TrainValueHook(
flags.verb_step,
loss,
value_name=forward_or_backward_str + name,
ckpt_dir=ntwk.ckpt_dir,
write_summary=True))
#add a summary op hook for histograms
print("Merged Summary op:", ntwk.merged_summary_op)
summary_op_hook = network_helper.SummaryWritingHook(
ntwk.merged_summary_op, flags.write_weight_step)
hook_list.append(summary_op_hook)
#Add a validation hook at the END!! (THE end controls the stopping of the training
valid_hook = network_helper.ValidationHook(
flags.eval_step,
valid_init_op,
ntwk.labels,
ntwk.logits,
ntwk.mse_loss,
stop_threshold=flags.stop_threshold,
value_name=forward_or_backward_str + "test_loss",
ckpt_dir=ntwk.ckpt_dir,
write_summary=True)
hook_list.append(valid_hook) #The validation hook is always in the list
return hook_list