moved from Building a multinomial regression model · Issue #265 · blei-lab/edward · GitHub.
@demodw
Hi,
I am trying to create a multinomial regression model using Edward. The equivalent Stan code can be seen at gist:20d95f0fdd54624d14aa2983e14d71f5 · GitHub.
In my very naive attempt, I just copied the logistic example, and tried re-formatting it a bit. The not so successful attempt can be seen at gist:a5acafb4025d53f4d3f80f559dcce775 · GitHub. Naively, I tried to just create a vector, theta, which has the linear sum of coefficients (except for the reference group, which I set to index 0). However, this gives me the error,
ValueError: Shape (3,) must have rank 2
Any help or pointers would be much appreciated!
Cheers.
Here are some tips:
-
Multinomial is a class. Similar to SciPy, I recommend calling multinomial which is an instantiated object of that class, e.g., multinomial.logpmf(). (It doesn’t actually matter in this example though.) It also requires specifying the number of trials. So you should do multinomial.logpmf(y, n=1, p=...).
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You use D=5 to generate the data but set D=1 for the rest of the code.
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You use tf.nn.softmax to convert from a real-vector to a probability vector. tf.nn.softmax requires a matrix as input, where the first dimension represents the batch size. For your example, you calculate the probability vectors one data point at a time. Therefore you should set theta = tf.expand_dims(theta, 0).
Alternatively, you can perform the vectorized operation of theta = tf.matmul(x, w) + b, and then call
theta = tf.matmul(x, w) + b
log_lik = tf.reduce_sum(multinomial.logpmf(y, n=1, p=tf.nn.softmax(theta)))
-
For your variational distribution, you specify distributions over C-1 dimensions rather than C dimensions. That makes sense because it’s a simplex, so you only need C-1 dimensions to get the last one. However, that doesn’t play well with tf.nn.softmax which assumes the input is not only a matrix of [batch_size, probabilities], but that each row sums to 1. Therefore you have to input the final column representing 1 - tf.reduce_sum(...) before passing it into tf.nn.softmax.
Alternatively, you can use the ed.to_simplex() utility function. It will convert a real-valued matrix of [N, K-1] to a matrix of probabilities [N, K], where each row sums to 1.
Here’s what the final script looks like:
class HierarchicalSoftmax:
...
def log_prob(self, xs, zs):
...
theta = tf.matmul(x, w) + b
log_lik = tf.reduce_sum(multinomial.logpmf(y, n=1, p=ed.to_simplex(theta)))
return log_lik + log_prior
...
C = 3
N = 40 # num data points
D = 5 # num features
...
qw_mu = tf.Variable(tf.random_normal([D, C-1]))
qw_sigma = tf.nn.softplus(tf.Variable(tf.random_normal([D, C-1])))
qb_mu = tf.Variable(tf.random_normal([C-1]))
qb_sigma = tf.nn.softplus(tf.Variable(tf.random_normal([])))
...
inference.run(n_print=5)
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