numpy random choice in Tensorflow

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-probelems[tf.cast(samples[0][0], tf.int32)].eval()Out: 1elems[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.

In tensorflow 2.0 tf.compat.v1.multinomial is deprecated instead use tf.random.categorical

My team and I had the same problem with the requirement of keeping all operations as tensorflow ops and implementing a 'without replacement' version.

Solution:

def tf_random_choice_no_replacement_v1(one_dim_input, num_indices_to_drop=3):    input_length = tf.shape(one_dim_input)[0]    # create uniform distribution over the sequence    # for tf.__version__<1.11 use tf.random_uniform - no underscore in function name    uniform_distribution = tf.random.uniform(        shape=[input_length],        minval=0,        maxval=None,        dtype=tf.float32,        seed=None,        name=None    )    # grab the indices of the greatest num_words_to_drop values from the distibution    _, indices_to_keep = tf.nn.top_k(uniform_distribution, input_length - num_indices_to_drop)    sorted_indices_to_keep = tf.contrib.framework.sort(indices_to_keep)    # gather indices from the input array using the filtered actual array    result = tf.gather(one_dim_input, sorted_indices_to_keep)    return result

The idea behind this code is to produce a random uniform distribution with a dimensionality that is equal to the dimension of the vector over which you'd like to perform the choice selection. Since the distribution will produce a sequence of numbers that will be unique and able to be ranked, you can take the indices of the top k positions, and use those as your choices. Since the position of the top k will be as random as the uniform distribution, it equates to performing a random choice without replacement.

This can perform the choice operation on any 1-d sequence in tensorflow.