numpy random choice in Tensorflow

问题:

Is there an equivalent function to numpy random choice in Tensorflow. In numpy we can get an item randomly from the given list with its weights.

 np.random.choice([1,2,3,5], 1, p=[0.1, 0, 0.3, 0.6, 0])

This code will select an item from the given list with p weights.

回答1:

No, but you can achieve the same result using tf.multinomial:

elems = tf.convert_to_tensor([1,2,3,5])
samples = tf.multinomial(tf.log([[1, 0, 0.3, 0.6]]), 1) # note log-prob
elems[tf.cast(samples[0][0], tf.int32)].eval()
Out: 1
elems[tf.cast(samples[0][0], tf.int32)].eval()
Out: 5

The [0][0] part is here, as multinomial expects a row of unnormalized log-probabilities for each element of the batch and also has another dimension for the number of samples.

回答2:

If instead of sampling random elements from a 1-dimensional Tensor, you want to randomly sample rows from an n-dimensional Tensor, you can combine tf.multinomial and tf.gather.

def _random_choice(inputs, n_samples):
    """
    With replacement.
    Params:
      inputs (Tensor): Shape [n_states, n_features]
      n_samples (int): The number of random samples to take.
    Returns:
      sampled_inputs (Tensor): Shape [n_samples, n_features]
    """
    # (1, n_states) since multinomial requires 2D logits.
    uniform_log_prob = tf.expand_dims(tf.zeros(tf.shape(inputs)[0]), 0)

    ind = tf.multinomial(uniform_log_prob, n_samples)
    ind = tf.squeeze(ind, 0, name="random_choice_ind")  # (n_samples,)

    return tf.gather(inputs, ind, name="random_choice")