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 np.asarray(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 = NoOutputLstm(len(index_to_char), len(index_to_word))
for i in range(1000):
for char_vectors, word_vector in training_data:
intermediate_results = {}
h_last = n.forward_prop(char_vectors,
np.zeros(len(index_to_word)),
intermediate_results)
n.back_prop(ce_err_prime(h_last, word_vector),
intermediate_results)
n.train(0.1, intermediate_results)
if i % 200 == 0:
total_err = 0
for char_vectors, word_vector in training_data:
h = n.activate(char_vectors, np.zeros(len(index_to_word)))
total_err += mathutils.mean_squared_error(h, word_vector)
print((total_err / len(training_data)))
result = n.activate(to_char_vector_sequence("infer"),
np.zeros(len(index_to_word)))
self.assertEquals("infer", index_to_word[np.argmax(result)])
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 np.asarray(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 = NoOutputLstm(len(index_to_char), len(index_to_word))
for i in range(1000):
for char_vectors, word_vector in training_data:
intermediate_results = {}
h_last = n.forward_prop(char_vectors, np.zeros(len(index_to_word)), intermediate_results)
n.back_prop(ce_err_prime(h_last, word_vector), intermediate_results)
n.train(0.1, intermediate_results)
if i % 200 == 0:
total_err = 0
for char_vectors, word_vector in training_data:
h = n.activate(char_vectors, np.zeros(len(index_to_word)))
total_err += mathutils.mean_squared_error(h, word_vector)
print(total_err/len(training_data))
result = n.activate(to_char_vector_sequence("infer"), np.zeros(len(index_to_word)))
self.assertEquals("infer", index_to_word[np.argmax(result)])
def test_learn_word_vectors_from_char_vector_sequence_2(self):
text = "please learn how to infer word vectors from sequences of character vectors" \
"giving it more words to try and confuse it" \
"how evil" \
"much diabolical" \
"many genius" \
"the doge of venice gives his regards"
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 np.asarray(sequence)
def to_word_vector(word):
vector = np.ones(len(word_to_index)) * -1
vector[word_to_index[word]] = 1
return vector
hidden_size = 50
training_data = [(to_char_vector_sequence(word), to_word_vector(word)) for word in text.split()]
lstm = NoOutputLstm(len(index_to_char), hidden_size)
ffn = FeedForwardNetwork([hidden_size, len(index_to_word)])
h0 = np.random.uniform(-1, 1, size=hidden_size)
learning_rate = 5
for i in range(2000):
for char_vectors, word_vector in training_data:
hs, f_gs, i_gs, cs, h = lstm.forward_prop(char_vectors, h0)
res = {}
y = ffn.forward_prop(h, res)
dy = mathutils.mean_squared_error(y, word_vector)
dx = ffn.dx(h, dy, res)
ffn.train(learning_rate, h, dy, res)
dh = dx
dw_xf_g, dw_hf_g, db_f_g, dw_xi_g, dw_hi_g, db_i_g, dw_xc, dw_hc, db_c = lstm.back_prop(char_vectors, hs, f_gs, i_gs, cs, dh)
lstm.w_xf_g -= dw_xf_g * learning_rate
lstm.w_hf_g -= dw_hf_g * learning_rate
lstm.b_f_g -= db_f_g * learning_rate
lstm.w_xi_g -= dw_xi_g * learning_rate
lstm.w_hi_g -= dw_hi_g * learning_rate
lstm.b_i_g -= db_i_g * learning_rate
lstm.w_xc -= dw_xc * learning_rate
lstm.w_hc -= dw_hc * learning_rate
lstm.b_c -= db_c * learning_rate
if i % 200 == 0:
total_err = 0
for char_vectors, word_vector in training_data:
h = lstm.activate(char_vectors, h0)
y = ffn.forward_prop(h, {})
total_err += mathutils.mean_squared_error(y, word_vector)
print(total_err/len(training_data))
h = lstm.activate(to_char_vector_sequence("infer"), h0)
y = ffn.forward_prop(h, {})
self.assertEquals("infer", index_to_word[np.argmax(y)])
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 np.asarray(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()]
# hidden_size = 100
hidden_size = len(index_to_word)
lstm = NoOutputLstm(len(index_to_char), hidden_size)
ffn = FeedForwardNetwork([hidden_size, 50, 20, len(index_to_word)])
h0 = np.random.uniform(-1, 1, size=hidden_size)
learning_rate = 0.5
for i in range(1000):
for char_vectors, word_vector in training_data:
hs, f_gs, i_gs, cs, lstm_output = lstm.forward_prop(char_vectors, h0)
res = {}
y = ffn.forward_prop(lstm_output, res)
# dy = mathutils.mean_squared_error_prime(y, word_vector)
dy = mathutils.mean_squared_error_prime(lstm_output, word_vector)
dx = ffn.dx(lstm_output, dy, res)
ffn.train(learning_rate, lstm_output, dy, res)
# dw_xf_g, dw_hf_g, db_f_g, dw_xi_g, dw_hi_g, db_i_g, dw_xc, dw_hc, db_c = lstm.back_prop(char_vectors, hs, f_gs, i_gs, cs, dx)
dw_xf_g, dw_hf_g, db_f_g, dw_xi_g, dw_hi_g, db_i_g, dw_xc, dw_hc, db_c = lstm.back_prop(char_vectors, hs, f_gs, i_gs, cs, dy)
lstm.w_xf_g -= dw_xf_g * learning_rate
lstm.w_hf_g -= dw_hf_g * learning_rate
lstm.b_f_g -= db_f_g * learning_rate
lstm.w_xi_g -= dw_xi_g * learning_rate
lstm.w_hi_g -= dw_hi_g * learning_rate
lstm.b_i_g -= db_i_g * learning_rate
lstm.w_xc -= dw_xc * learning_rate
lstm.w_hc -= dw_hc * learning_rate
lstm.b_c -= db_c * learning_rate
if i % 200 == 0:
total_err = 0
for char_vectors, word_vector in training_data:
h = lstm.activate(char_vectors, h0)
output_vector = ffn.forward_prop(h[-1], {})
total_err += mathutils.mean_squared_error(output_vector, word_vector)
print(total_err/len(training_data))
lstm_out = lstm.activate(to_char_vector_sequence("infer"), h0)
result = ffn.forward_prop(lstm_out, {})
self.assertEquals("infer", index_to_word[np.argmax(result)])
def test_learn_word_vectors_from_char_vector_sequence_2(self):
text = "please learn how to infer word vectors from sequences of character vectors" \
"giving it more words to try and confuse it" \
"how evil" \
"much diabolical" \
"many genius" \
"the doge of venice gives his regards"
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 np.asarray(sequence)
def to_word_vector(word):
vector = np.ones(len(word_to_index)) * -1
vector[word_to_index[word]] = 1
return vector
hidden_size = 50
training_data = [(to_char_vector_sequence(word), to_word_vector(word))
for word in text.split()]
lstm = NoOutputLstm(len(index_to_char), hidden_size)
ffn = FeedForwardNetwork([hidden_size, len(index_to_word)])
h0 = np.random.uniform(-1, 1, size=hidden_size)
learning_rate = 5
for i in range(2000):
for char_vectors, word_vector in training_data:
hs, f_gs, i_gs, cs, h = lstm.forward_prop(char_vectors, h0)
res = {}
y = ffn.forward_prop(h, res)
dy = mathutils.mean_squared_error(y, word_vector)
dx = ffn.dx(h, dy, res)
ffn.train(learning_rate, h, dy, res)
dh = dx
dw_xf_g, dw_hf_g, db_f_g, dw_xi_g, dw_hi_g, db_i_g, dw_xc, dw_hc, db_c = lstm.back_prop(
char_vectors, hs, f_gs, i_gs, cs, dh)
lstm.w_xf_g -= dw_xf_g * learning_rate
lstm.w_hf_g -= dw_hf_g * learning_rate
lstm.b_f_g -= db_f_g * learning_rate
lstm.w_xi_g -= dw_xi_g * learning_rate
lstm.w_hi_g -= dw_hi_g * learning_rate
lstm.b_i_g -= db_i_g * learning_rate
lstm.w_xc -= dw_xc * learning_rate
lstm.w_hc -= dw_hc * learning_rate
lstm.b_c -= db_c * learning_rate
if i % 200 == 0:
total_err = 0
for char_vectors, word_vector in training_data:
h = lstm.activate(char_vectors, h0)
y = ffn.forward_prop(h, {})
total_err += mathutils.mean_squared_error(y, word_vector)
print(total_err / len(training_data))
h = lstm.activate(to_char_vector_sequence("infer"), h0)
y = ffn.forward_prop(h, {})
self.assertEquals("infer", index_to_word[np.argmax(y)])
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 np.asarray(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()]
# hidden_size = 100
hidden_size = len(index_to_word)
lstm = NoOutputLstm(len(index_to_char), hidden_size)
ffn = FeedForwardNetwork([hidden_size, 50, 20, len(index_to_word)])
h0 = np.random.uniform(-1, 1, size=hidden_size)
learning_rate = 0.5
for i in range(1000):
for char_vectors, word_vector in training_data:
hs, f_gs, i_gs, cs, lstm_output = lstm.forward_prop(
char_vectors, h0)
res = {}
y = ffn.forward_prop(lstm_output, res)
# dy = mathutils.mean_squared_error_prime(y, word_vector)
dy = mathutils.mean_squared_error_prime(
lstm_output, word_vector)
dx = ffn.dx(lstm_output, dy, res)
ffn.train(learning_rate, lstm_output, dy, res)
# dw_xf_g, dw_hf_g, db_f_g, dw_xi_g, dw_hi_g, db_i_g, dw_xc, dw_hc, db_c = lstm.back_prop(char_vectors, hs, f_gs, i_gs, cs, dx)
dw_xf_g, dw_hf_g, db_f_g, dw_xi_g, dw_hi_g, db_i_g, dw_xc, dw_hc, db_c = lstm.back_prop(
char_vectors, hs, f_gs, i_gs, cs, dy)
lstm.w_xf_g -= dw_xf_g * learning_rate
lstm.w_hf_g -= dw_hf_g * learning_rate
lstm.b_f_g -= db_f_g * learning_rate
lstm.w_xi_g -= dw_xi_g * learning_rate
lstm.w_hi_g -= dw_hi_g * learning_rate
lstm.b_i_g -= db_i_g * learning_rate
lstm.w_xc -= dw_xc * learning_rate
lstm.w_hc -= dw_hc * learning_rate
lstm.b_c -= db_c * learning_rate
if i % 200 == 0:
total_err = 0
for char_vectors, word_vector in training_data:
h = lstm.activate(char_vectors, h0)
output_vector = ffn.forward_prop(h[-1], {})
total_err += mathutils.mean_squared_error(
output_vector, word_vector)
print(total_err / len(training_data))
lstm_out = lstm.activate(to_char_vector_sequence("infer"), h0)
result = ffn.forward_prop(lstm_out, {})
self.assertEquals("infer", index_to_word[np.argmax(result)])