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test_gru.py
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test_gru.py
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import unittest
import numpy as np
from pydl import Gru, mathutils
def frand(size=None):
return np.random.uniform(-1, 1, size=size)
def err(y):
return np.sum(0.5 * np.square(y))
def derr(y):
return y
def ce_err_prime(y, t):
return y - t
def gru_arrays(gru: Gru):
return [
gru.w_rx,
gru.w_rh,
gru.b_r,
gru.w_zx,
gru.w_zh,
gru.b_z,
gru.w_hx,
gru.w_hh,
gru.b_h
]
def gru_array_names(gru: Gru):
return [
"w_rx",
"w_rh",
"b_r",
"w_zx",
"w_zh",
"b_z",
"w_hx",
"w_hh",
"b_h"
]
def gru_results_arrays(gru_results: dict):
return [
gru_results["dw_rx"],
gru_results["dw_rh"],
gru_results["db_r"],
gru_results["dw_zx"],
gru_results["dw_zh"],
gru_results["db_z"],
gru_results["dw_hx"],
gru_results["dw_hh"],
gru_results["db_h"]
]
def scalar_indices(gru: Gru):
for i, array in enumerate(gru_arrays(gru)):
for j in np.ndindex(array.shape):
yield i, j
def clone(gru: Gru):
gru_clone = Gru(0, 0)
gru_clone.w_rx = np.copy(gru.w_rx)
gru_clone.w_rh = np.copy(gru.w_rh)
gru_clone.b_r = np.copy(gru.b_r)
gru_clone.w_zx = np.copy(gru.w_zx)
gru_clone.w_zh = np.copy(gru.w_zh)
gru_clone.b_z = np.copy(gru.b_z)
gru_clone.w_hx = np.copy(gru.w_hx)
gru_clone.w_hh = np.copy(gru.w_hh)
gru_clone.b_h = np.copy(gru.b_h)
return gru_clone
class TestGru(unittest.TestCase):
def test_single_step_gradient(self):
input_size = 5
hidden_size = 6
n = Gru(input_size, hidden_size)
xs = [frand(size=input_size)]
h0 = frand(hidden_size)
intermediate_results = {}
hs = n.forward_prop(xs, h0, intermediate_results)
dh0 = n.back_prop([derr(hs[-1])], intermediate_results)
delta = 1e-4
for index in scalar_indices(n):
array_name = gru_array_names(n)[index[0]]
slightly_less = clone(n)
gru_arrays(slightly_less)[index[0]][index[1]] -= delta
err_slightly_less = err(slightly_less.forward_prop(xs, h0, {})[-1])
slightly_more = clone(n)
gru_arrays(slightly_more)[index[0]][index[1]] += delta
err_slightly_more = err(slightly_more.forward_prop(xs, h0, {})[-1])
expected_grad = (err_slightly_more - err_slightly_less) / (2 * delta)
numerical_grad = gru_results_arrays(intermediate_results)[index[0]][index[1]]
self.assertTrue(abs(expected_grad - numerical_grad) < 0.01,
"{}: {} not within threshold of {}".format(array_name, numerical_grad, expected_grad))
for index in np.ndindex(h0.shape):
slightly_less_h0 = np.copy(h0)
slightly_less_h0[index] -= delta
err_slightly_less_h0 = err(n.forward_prop(xs, slightly_less_h0, {})[-1])
slightly_more_h0 = np.copy(h0)
slightly_more_h0[index] += delta
err_slightly_more_h0 = err(n.forward_prop(xs, slightly_more_h0, {})[-1])
expected_grad = (err_slightly_more_h0 - err_slightly_less_h0) / (2 * delta)
numerical_grad = dh0[index]
self.assertTrue(abs(expected_grad - numerical_grad) < 0.01,
"h0: {} not within threshold of {}".format(numerical_grad, expected_grad))
def test_multi_step_gradient(self):
input_size = 5
hidden_size = 6
n = Gru(input_size, hidden_size)
xs = [frand(size=input_size) for _ in range(10)]
h0 = frand(hidden_size)
intermediate_results = {}
hs = n.forward_prop(xs, h0, intermediate_results)
n.back_prop([derr(h) for h in hs], intermediate_results)
for index in scalar_indices(n):
array_name = gru_array_names(n)[index[0]]
delta = 1e-4
slightly_less = clone(n)
gru_arrays(slightly_less)[index[0]][index[1]] -= delta
slightly_less_hs = slightly_less.forward_prop(xs, h0, {})
err_slightly_less = sum([err(h) for h in slightly_less_hs])
slightly_more = clone(n)
gru_arrays(slightly_more)[index[0]][index[1]] += delta
slightly_more_hs = slightly_more.forward_prop(xs, h0, {})
err_slightly_more = sum([err(h) for h in slightly_more_hs])
expected_grad = (err_slightly_more - err_slightly_less) / (2 * delta)
numerical_grad = gru_results_arrays(intermediate_results)[index[0]][index[1]]
self.assertTrue(abs(expected_grad - numerical_grad) < 0.01,
"{}: {} not within threshold of {}".format(array_name, numerical_grad, expected_grad))
def test_learn_word_vectors_from_char_vector_sequence(self):
text = "please learn how to infer word vectors from sequences of character vectors"
index_to_word = list(set(text.split()))
index_to_char = list(set(text))
word_to_index = {word: index for index, word in enumerate(index_to_word)}
char_to_index = {word: index for index, word in enumerate(index_to_char)}
def to_char_vector_sequence(word):
sequence = []
for char in word:
vector = np.ones(len(char_to_index)) * -1
vector[char_to_index[char]] = 1
sequence.append(vector)
sequence.append(np.zeros(len(char_to_index)))
return sequence
def to_word_vector(word):
vector = np.ones(len(word_to_index)) * -1
vector[word_to_index[word]] = 1
return vector
training_data = [(to_char_vector_sequence(word), to_word_vector(word)) for word in text.split()]
n = Gru(len(index_to_char), len(index_to_word))
for i in range(1000):
for char_vectors, word_vector in training_data:
intermediate_results = {}
hs = n.forward_prop(char_vectors, np.zeros(len(index_to_word)), intermediate_results)
dhs = [np.zeros(shape=word_vector.shape) for _ in range(len(hs))]
dhs[-1] = ce_err_prime(hs[-1], word_vector)
n.back_prop(dhs, intermediate_results)
n.train(0.1, intermediate_results)
if i % 200 == 0:
total_err = 0
for char_vectors, word_vector in training_data:
hs = n.forward_prop(char_vectors, np.zeros(len(index_to_word)), {})
total_err += mathutils.mse(hs[-1], word_vector)
print(total_err/len(training_data))
result = n.forward_prop(to_char_vector_sequence("infer"), np.zeros(len(index_to_word)), {})[-1]
self.assertEquals("infer", index_to_word[np.argmax(result)])