open journal (24/5/217): orthogonal initializer for lstm with tensorflow 1.10.

orthogonal_initialize_demo_code.py

import numpy as np
def orthogonal_initializer(scale=1.0, seed=None, dtype=tf.float32):
    def _initializer(shape, dtype=dtype, partition_info=None):
        flat = (shape[0], np.prod(shape[1:]))
        a = np.random.normal(0.0, 1.0, flat)
        u, _, v = np.linalg.svd(a, full_matrices=False)
        q = (u if u.shape == flat else v).reshape(shape)
        return tf.constant(scale * q[:shape[0], :shape[1]], dtype=dtype)
    return _initializer

tf.reset_default_graph()
ortho_initializer = orthogonal_initializer()
with tf.variable_scope('e',reuse=None):
    encode_cell = tf.contrib.rnn.LSTMCell(num_units= 10, initializer=ortho_initializer)
    inpt = tf.constant([[1,2,3,4,5,6,7,8,9,10]],dtype=tf.float32)
    zero = encode_cell.zero_state(1,tf.float32)
    outp = encode_cell(inpt,zero)
with tf.Session() as sess:
    tf.global_variables_initializer().run()
    print sess.run(outp)