k=8,
hash_ids=int(2e5),
batch_size=args.b, # 1024
optimizer="adam",
learning_rate=0.001,
num_display_steps=250,
num_eval_steps=1000,
epoch=1,
metric='auc',
activation=['relu', 'relu', 'relu'],
cross_activation='identity',
init_method='uniform',
init_value=0.1,
feature_nums=len(features),
kfold=5)
utils.print_hparams(hparam)
# # Training model
# In[6]:
kfold = KFold(n_splits=hparam.kfold, shuffle=True, random_state=712)
for i, (train_index, dev_index) in enumerate(kfold.split(train)):
print('Fold', i)
model = ctrNet.build_model(hparam)
model.train(train_data=(train.iloc[train_index][features],
train.iloc[train_index]['HasDetections']),
dev_data=(train.iloc[dev_index][features],
train.iloc[dev_index]['HasDetections']))
print("Training Done! Inference...")
k=8,
hash_ids=int(2e5),
batch_size=1024,
optimizer="adam",
learning_rate=0.001,
num_display_steps=1000,
num_eval_steps=1000,
epoch=1,
metric='auc',
activation=['relu', 'relu', 'relu'],
cross_activation='identity',
init_method='uniform',
init_value=0.1,
feature_nums=train.shape[1],
kfold=5)
misc_utils.print_hparams(hparam)
#========================================================================
#========================================================================
# Result Box
model_list = []
result_list = []
score_list = []
val_pred_list = []
oof_pred = np.zeros(train.shape[0])
y_test = np.zeros(x_test.shape[0])
#========================================================================
model_type = 'NN'