Given a rank 3 tensor:
sentence_max_length = 5
batch_size = 3
n_hidden = 10
n_classes = 2
x = tf.constant(np.reshape(np.arange(150),(batch_size,sentence_max_length, n_hidden)), dtype = tf.float32)
And a rank 2 tensor:
W = tf.constant(np.reshape(np.arange(20), (n_hidden, n_classes)), dtype = tf.float32)
And a rank 1 bias tensor:
b = tf.constant(np.reshape(np.arange(5), (n_classes), dtype = tf.float32))
I was wondering how one would the last two axis of x by W such that the resulting vector Z would be of shape (batch_size, max_length, n_classes) though batch_size would not be known during graph creation I've just given it a value here for demonstration purposes
So to clarify:
Z[0] = tf.matmul(x[0,:,:], W) + b
So that the W and b are shared across all the batches. The reason for this is that I am trying to use the output of tf.dynamic_rnn whereby the output is of shape (batch_size, sentence_max_length, n_hidden) and build another layer ontop of output which has shared weights W and b.