def run(name, d):
print("building {0} ...".format(name))
tf.reset_default_graph()
d.define_holders(from_restore=False)
model = CtrModel(holder_dict=d.feats_holder_dict,
sparse_dim_dict=d.sparse_dim_dict,
methods_list=name,
drop_rate=0.5,
dnn_h_list=[[1024, 1024, 1024, 1024], [512, 512, 512]],
embed_base=40.,
embed_exp=1 / 4.)
loss, opt = model.get_loss_and_minimizer(labels=d.labels_holder,
clip_by=100.)
solver = Solver()
lr_assign = tf.assign(model.learning_rate_tensor, 1e-4)
with tf.Session() as sess:
# write graph
writer = tf.summary.FileWriter('./logs/' + name, sess.graph)
def run(name, d, _list):
for idx, struct in enumerate(_list):
print("building model {0} with {1}".format(name, struct))
tf.reset_default_graph()
d.define_holders(from_restore=False)
model = CtrModel(
holder_dict=d.feats_holder_dict,
sparse_dim_dict=d.sparse_dim_dict,
methods_list=[name],
dcn_degree=struct,
drop_rate=None,
dnn_h_list=struct,
dnn_act_fn=tf.nn.relu,
embed_w_scale=1e2,
nn_w_scale=1e2,
# nn_b_scale=1e2,
lr_w_scale=1e2,
# lr_b_scale=1e2,
embed_base=10.,
embed_exp=0.)
loss, opt = model.get_loss_and_minimizer(labels=d.labels_holder,
method="momentum",
clip_by=50.)
solver = Solver()
d.with_train = False
with tf.Session() as sess:
# write graph
# writer = tf.summary.FileWriter('./logs/'+name, sess.graph)
sess.run(tf.global_variables_initializer())
train_loss_hist, valid_loss_hist = solver.train(
sess=sess,
loss=loss,
logits=model.logits,
pred=model.predicts,
learning_rate_tensor=model.learning_rate_tensor,
data=d,
minimizer=opt,
auto_stop=True,
phase=model.phase_tensor,
global_step_tensor=model.global_step_tensor,
learning_rate_init=1e-4,
learning_rate_min=1e-6,
save_path="./results/0403/{0}/".format(name),
save_valid_loss=0.0820,
with_ema=False,
max_train_step=10000,
tol=1,
log_period=100)