def train(batch_size, epochs, learning_rate, num_factors, hidden_factors,
num_att_head, num_att_layers):
ret = parse_input_v2('../datasets/train.csv',
'../datasets/test.csv',
use_cross=False)
labels = ret['train'][0]
dense_indices = ret['train'][1]
dense_values = ret['train'][2]
sparse_indices = ret['train'][3]
sparse_values = ret['train'][4]
num_dense_fields = ret['num_fields'][0]
num_sparse_fields = ret['num_fields'][1]
num_dense_features = ret['num_features'][0]
num_sparse_features = ret['num_features'][1]
def input_fn(batch_size, epochs, is_training):
ds = tf.data.Dataset.from_tensor_slices(({
'dense_indices': dense_indices,
'dense_values': dense_values,
'sparse_indices': sparse_indices,
'sparse_values': sparse_values
}, labels))
if is_training:
ds = ds.shuffle(100).batch(batch_size).repeat(epochs)
return ds
# iterator = ds.make_one_shot_iterator()
# batch_features, batch_labels = iterator.get_next()
# return tf.reshape(batch_ids,shape=[-1,field_size]), tf.reshape(batch_vals,shape=[-1,field_size]), batch_labels
# return batch_features, batch_labels
def train_input_fn():
return input_fn(batch_size, epochs, is_training=True)
def eval_input_fn():
return input_fn(batch_size, epochs, is_training=False)
estimator = tf.estimator.Estimator(model_fn=autoint_model.model_fn,
model_dir='./model',
params={
'num_dense_features':
num_dense_features,
'num_sparse_features':
num_sparse_features,
'num_dense_fields':
num_dense_fields,
'num_sparse_fields':
num_sparse_fields,
'num_factors': num_factors,
'learning_rate': learning_rate,
'hidden_factors': hidden_factors,
'num_att_head': num_att_head,
'num_att_layers': num_att_layers
})
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn,
max_steps=1000)
eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, steps=10)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
def predict(num_factors, hidden_factors, num_att_head, num_att_layers):
ret = parse_input_v2('../datasets/train.csv',
'../datasets/test.csv',
use_cross=False)
ids = ret['test'][0]
dense_indices = ret['test'][1]
dense_values = ret['test'][2]
sparse_indices = ret['test'][3]
sparse_values = ret['test'][4]
num_dense_fields = ret['num_fields'][0]
num_sparse_fields = ret['num_fields'][1]
num_dense_features = ret['num_features'][0]
num_sparse_features = ret['num_features'][1]
def input_fn():
ds = tf.data.Dataset.from_tensor_slices(({
'dense_indices': dense_indices,
'dense_values': dense_values,
'sparse_indices': sparse_indices,
'sparse_values': sparse_values
}, ids))
return ds
def predict_input_fn():
return input_fn()
estimator = tf.estimator.Estimator(model_fn=autoint_model.model_fn,
model_dir='./model',
params={
'num_dense_features':
num_dense_features,
'num_sparse_features':
num_sparse_features,
'num_dense_fields':
num_dense_fields,
'num_sparse_fields':
num_sparse_fields,
'num_factors': num_factors,
'hidden_factors': hidden_factors,
'num_att_head': num_att_head,
'num_att_layers': num_att_layers
})
preds = estimator.predict(predict_input_fn)
return list(zip(ids, preds))
def predict(num_factors, deep_layers, reduce_ratio):
ret = parse_input_v2('../datasets/train.csv',
'../datasets/test.csv',
use_cross=True)
ids = ret['test'][0]
dense_indices = ret['test'][1]
dense_values = ret['test'][2]
sparse_indices = ret['test'][3]
sparse_values = ret['test'][4]
num_dense_fields = ret['num_fields'][0]
num_sparse_fields = ret['num_fields'][1]
num_dense_features = ret['num_features'][0]
num_sparse_features = ret['num_features'][1]
def input_fn():
ds = tf.data.Dataset.from_tensor_slices(({
'dense_indices': dense_indices,
'dense_values': dense_values,
'sparse_indices': sparse_indices,
'sparse_values': sparse_values
}, ids))
return ds
def predict_input_fn():
return input_fn()
estimator = tf.estimator.Estimator(model_fn=fibinet_model.model_fn,
model_dir='./model',
params={
'num_dense_features':
num_dense_features,
'num_sparse_features':
num_sparse_features,
'num_dense_fields':
num_dense_fields,
'num_sparse_fields':
num_sparse_fields,
'num_factors': num_factors,
'deep_layers': deep_layers,
'reduce_ratio': reduce_ratio
})
preds = estimator.predict(predict_input_fn)
return list(zip(ids, preds))