tensorflow indexing through multidimensional array

Issue

I’ve got this matrix of probabilities here and I’m trying to index them to get one of the probabilities in each row so I can log them.

p_matrix = 
[[0.5        0.5      ]
 [0.45384845 0.5461515 ]
 [0.45384845 0.5461515 ]
 [0.45384845 0.5461515 ]
 [0.48519668 0.51480335]
 [0.48257706 0.517423  ]
 [0.48257706 0.517423  ]
 [0.48257706 0.517423  ]
 [0.4807878  0.5192122 ]
 [0.45384845 0.5461515 ]
 [0.48257703 0.517423  ]]

The indexes are stored in a placeholder a = tf.placeholder(shape=None, dtype=tf.int32)

Normally I would simply do p_matrix[np.arange(a.shape[0], dtype=np.int32), a]

in order to grab the corresponding results but this gives me an error

IndexError: arrays used as indices must be of integer (or boolean) type

Using a standard numpy array in place of a gives me the desired result. I thought it might be something specific about using dtype=tf.int32 but I get the same result if I change the dtype of the placeholder to np.int32.

Also when I get the type of a it returns <class 'numpy.ndarray'> and for a[0] it returns <class 'numpy.int32'>.

Any ideas?

To summarize:

x = np.arange(a.shape[0])
y = np.array(list(a))

print(action_prob[x,y])  # This works.
print(action_prob[x,a])  # This does not work.

type(a) = <class 'numpy.ndarray'>
type(y) = <class 'numpy.ndarray'>

I can only assume it’s because one is a tf.placeholder and as a result I can’t specify this in the graph initialization?

EDIT:

Sample code:

class Model():
    def __init__(self, sess, s_size, game, lr=0.001):
        f_size = 12
        self.input = tf.placeholder(shape=[None, f_size], dtype=tf.float32)
        self.action = tf.placeholder(shape=None, dtype=tf.int32)

        self.p_matrix = tf.contrib.layers.fully_connected(self.state,
            20, activation_fn=tf.nn.softmax, biases_initializer=None)

        # Here I need to select the correct p_values
        self.log_prob = tf.log(self.action_prob[p_selected])

        self.train = tf.train.AdamOptimizer(lr).minimize(loss=-log_prob)

    def learn(self, s, a, td):
        # a = a.reshape(a.shape[0], 1)  # necessary for the episodes
        feed_dict = {self.input: s, self.action: a}
        p_matrix = self.sess.run(self.p_matrix, feed_dict)

        log_prob, p_matrix = self.sess.run([self.log_prob, self.p_matrix], feed_dict)

        _ = self.sess.run(self.train, feed_dict)

Solution

You can do that with tf.gather_nd:

idx = tf.stack([tf.range(tf.shape(a)[0], dtype=a.dtype), a], axis=1)
p_selected = tf.gather_nd(p_matrix, idx)

Each row in idx contains the "coordinates" of each element to retrieve, like [[0, a[0]], [1, a[1]], ...].

Alternatively batch_dims argument lets you omit those leading location dimensions from the idx

idx = tf.expand_dims(a, axis=1)
p_selected = tf.gather_nd(batch_dims=p_matrix, indices=idx, batch_dims=1)

Answered By – jdehesa

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

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