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NTM2.py
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NTM2.py
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__author__ = 'Rybkin & Kravchenko'
import numpy as np
import theano
import theano.tensor as T
floatX = theano.config.floatX
print(floatX)
from keras.layers.recurrent import Recurrent, LSTM
from keras import backend
def update_controller(self, inp, h_tm1, M):
x = T.concatenate([inp, M], axis=-1)
if len(h_tm1) == 2:
if hasattr(self.lstm, "get_constants"):
BW, BU = self.lstm.get_constants(x)
h_tm1 += (BW, BU)
_, h = self.lstm.step(x, h_tm1)
return h
def circulant(leng, n_shifts):
# wicked tensor
eye = np.eye(leng)
shifts = range(n_shifts // 2, -n_shifts // 2, -1)
C = np.asarray([np.roll(eye, s, axis=1) for s in shifts])
return theano.shared(C.astype(theano.config.floatX))
def re_norm(x):
return x / (x.sum(axis=1, keepdims=True))
def soft_max(x):
wt = x.flatten(ndim=2)
w = T.nnet.softmax(wt)
return w.reshape(x.shape)
def cosine_similarity(M, k):
dot = (M * k[:, None, :]).sum(axis=-1)
nM = T.sqrt((M ** 2).sum(axis=-1))
nk = T.sqrt((k ** 2).sum(axis=-1, keepdims=True))
return dot / (nM * nk)
class NeuralTuringMachine(Recurrent):
def __init__(self, output_dim, memory_size, shift_range=3,
init='glorot_uniform', inner_init='orthogonal',
input_dim=None, input_length=None, **kwargs):
self.output_dim = output_dim
self.n_slots = memory_size[1]
self.m_length = memory_size[0]
self.shift_range = shift_range
self.init = init
self.inner_init = inner_init
self.input_dim = input_dim
self.input_length = input_length
if self.input_dim:
kwargs['input_shape'] = (self.input_length, self.input_dim)
super(NeuralTuringMachine, self).__init__(**kwargs)
def build(self, input_shape):
input_leng, input_dim = input_shape[1:]
# self.input = T.tensor3()
self.lstm = LSTM(
input_dim=input_dim + self.m_length,
input_length=input_leng,
output_dim=self.output_dim, init=self.init,
forget_bias_init='zero',
inner_init=self.inner_init)
self.lstm.build(input_shape)
# initial memory, state, read and write vecotrs
self.M = theano.shared((.001 * np.ones((1,)).astype(floatX)))
self.init_h = backend.zeros((self.output_dim))
self.init_wr = self.lstm.init((self.n_slots,))
self.init_ww = self.lstm.init((self.n_slots,))
# write
self.W_e = self.lstm.init((self.output_dim, self.m_length)) # erase
self.b_e = backend.zeros((self.m_length))
self.W_a = self.lstm.init((self.output_dim, self.m_length)) # add
self.b_a = backend.zeros((self.m_length))
# get_w parameters for reading operation
self.W_k_read = self.lstm.init((self.output_dim, self.m_length))
self.b_k_read = self.lstm.init((self.m_length,))
self.W_c_read = self.lstm.init((self.output_dim, 3))
self.b_c_read = backend.zeros((3))
self.W_s_read = self.lstm.init((self.output_dim, self.shift_range))
self.b_s_read = backend.zeros((self.shift_range)) # b_s lol! not intentional
# get_w parameters for writing operation
self.W_k_write = self.lstm.init((self.output_dim, self.m_length))
self.b_k_write = self.lstm.init((self.m_length,))
self.W_c_write = self.lstm.init((self.output_dim, 3)) # 3 = beta, g, gamma see eq. 5, 7, 9
self.b_c_write = backend.zeros((3))
self.W_s_write = self.lstm.init((self.output_dim, self.shift_range))
self.b_s_write = backend.zeros((self.shift_range))
self.C = circulant(self.n_slots, self.shift_range)
self.trainable_weights = self.lstm.trainable_weights + [
self.W_e, self.b_e,
self.W_a, self.b_a,
self.W_k_read, self.b_k_read,
self.W_c_read, self.b_c_read,
self.W_s_read, self.b_s_read,
self.W_k_write, self.b_k_write,
self.W_s_write, self.b_s_write,
self.W_c_write, self.b_c_write,
self.M,
self.init_h, self.init_wr, self.init_ww]
self.init_c = backend.zeros((self.output_dim))
self.trainable_weights = self.trainable_weights + [self.init_c, ]
def read(self, w, M):
return (w[:, :, None] * M).sum(axis=1)
def write(self, w, e, a, M):
Mtilda = M * (1 - w[:, :, None] * e[:, None, :])
Mout = Mtilda + w[:, :, None] * a[:, None, :]
return Mout
def get_content_w(self, beta, k, M):
num = beta[:, None] * cosine_similarity(M, k)
return soft_max(num)
def get_location_w(self, g, s, C, gamma, wc, w_tm1):
wg = g[:, None] * wc + (1 - g[:, None]) * w_tm1
Cs = (C[None, :, :, :] * wg[:, None, None, :]).sum(axis=3)
wtilda = (Cs * s[:, :, None]).sum(axis=1)
wout = re_norm(wtilda ** gamma[:, None])
return wout
def get_controller_output(self, h, W_k, b_k, W_c, b_c, W_s, b_s):
k = T.tanh(T.dot(h, W_k) + b_k) # + 1e-6
c = T.dot(h, W_c) + b_c
beta = T.nnet.relu(c[:, 0]) + 1e-4
g = T.nnet.sigmoid(c[:, 1])
gamma = T.nnet.relu(c[:, 2]) + 1.0001
s = T.nnet.softmax(T.dot(h, W_s) + b_s)
return k, beta, g, gamma, s
def get_output_shape_for(self, input_shape):
if self.return_sequences:
return input_shape[0], input_shape[1], self.output_dim
else:
return input_shape[0], self.output_dim
def call(self, x, mask = None):
M_tm1, wr_tm1, ww_tm1 = mask[:3]
# reshape
M_tm1 = M_tm1.reshape((x.shape[0], self.n_slots, self.m_length))
# read
h_tm1 = mask[3:]
k_read, beta_read, g_read, gamma_read, s_read = self.get_controller_output(
h_tm1[0], self.W_k_read, self.b_k_read, self.W_c_read, self.b_c_read,
self.W_s_read, self.b_s_read)
wc_read = self.get_content_w(beta_read, k_read, M_tm1)
wr_t = self.get_location_w(g_read, s_read, self.C, gamma_read,
wc_read, wr_tm1)
M_read = self.read(wr_t, M_tm1)
# update controller
h_t = update_controller(self, x, h_tm1, M_read)
# write
k_write, beta_write, g_write, gamma_write, s_write = self.get_controller_output(
h_t[0], self.W_k_write, self.b_k_write, self.W_c_write,
self.b_c_write, self.W_s_write, self.b_s_write)
wc_write = self.get_content_w(beta_write, k_write, M_tm1)
ww_t = self.get_location_w(g_write, s_write, self.C, gamma_write,
wc_write, ww_tm1)
e = T.nnet.sigmoid(T.dot(h_t[0], self.W_e) + self.b_e)
a = T.tanh(T.dot(h_t[0], self.W_a) + self.b_a)
M_t = self.write(ww_t, e, a, M_tm1)
M_t = M_t.flatten(ndim=2)
return h_t[0], [M_t, wr_t, ww_t] + h_t