How to set parts of positive samples weight in Ten

I want to set the same weight for parts of positive samples. However,tf.nn.weighted_cross_entropy_with_logits can only set the weight for all positive samples in my opinion.

for example, in the ctr predicition, I want set 10 weights for the order samples, and the weight of click samples and the unclick sample is still 1.

Here is my unweighted code

def my_model(features, labels, mode, params):
    net = tf.feature_column.input_layer(features, params['feature_columns'])
    for units in params['hidden_units']:
       net = tf.layers.dense(net, units=units, activation=params["activation"])  
    logits = tf.layers.dense(net, params['n_classes'], activation=None)

    predicted_classes = tf.argmax(logits, 1)
    if mode == tf.estimator.ModeKeys.PREDICT:
       predictions = {
        'class_ids': predicted_classes, #predicted_classes[:, tf.newaxis],
        'probabilities': tf.nn.softmax(logits),
        'logits': logits,
       }
       return tf.estimator.EstimatorSpec(mode, predictions=predictions)

    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)

    metrics = {'auc': tf.metrics.auc(labels=labels, predictions=tf.nn.softmax(logits)[:,1])}

    if mode == tf.estimator.ModeKeys.EVAL:
       return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics)

    assert mode == tf.estimator.ModeKeys.TRAIN
    optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) 
    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)

Train

train_input_fn = tf.estimator.inputs.pandas_input_fn(x=data_train, y=data_train_click, batch_size = 1024, num_epochs=1, shuffle=False)
classifier.train(input_fn=train_input_fn)

Here data_train_click is a Series, which the click samples are 1 and the unclicked samples are 0. And I have a Series named data_train_order, which the order samples are 1 and the others are 0

回答1:

The easiest way to do this is by using keras

https://keras.io/models/model/

The fit function has a sample_weight parameter.

回答2:

You can weigh each samples differently by passing a weight parameter to the loss function which is a tensor of shape [batch_size] containing corresponding weights for each samples.

loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits, weights=weights)