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AttRec.py
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AttRec.py
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#!/usr/bin/env python
"""Implementation of Caser.
Reference: Next Item Recommendation with Self-Attentive Metric Learning, Shuai Zhang etc. , AAAI workshop'18.
"""
import tensorflow as tf
import time
import numpy as np
from utils.evaluation.SeqRecMetrics import *
import numpy as np
np.set_printoptions(threshold=np.inf)
__author__ = "Shuai Zhang"
__copyright__ = "Copyright 2018, The DeepRec Project"
__license__ = "GPL"
__version__ = "1.0.0"
__maintainer__ = "Shuai Zhang"
__email__ = "cheungdaven@gmail.com"
__status__ = "Development"
class AttRec():
def __init__(self, sess, num_user, num_item, learning_rate=0.05, reg_rate=1e-2, epoch=5000, batch_size=1000,
show_time=False, T=1, display_step=1000, verbose=False):
self.learning_rate = learning_rate
self.epochs = epoch
self.batch_size = batch_size
self.reg_rate = reg_rate
self.sess = sess
self.num_user = num_user
self.num_item = num_item
self.show_time = show_time
self.verbose = verbose
self.T = T
self.display_step = display_step
self.neg_items = dict()
print("AttSeqRec.")
def build_network(self, L, num_T, num_factor=150, num_neg=1):
self.L = L
self.num_T = num_T
self.num_factor = num_factor
self.num_neg = num_neg
self.user_id = tf.placeholder(dtype=tf.int32, shape=[None], name='user_id')
self.item_seq = tf.placeholder(dtype=tf.int32, shape=[None, L], name='item_seq')
self.item_id = tf.placeholder(dtype=tf.int32, shape=[None, self.num_T], name='item_id')
self.item_id_test = tf.placeholder(dtype=tf.int32, shape=[None, 1], name='item_id_test')
self.neg_item_id = tf.placeholder(dtype=tf.int32, shape=[None, self.num_T * self.num_neg], name='item_id_neg')
self.isTrain = tf.placeholder(tf.bool, shape=())
# self.y = tf.placeholder("float", [None], 'rating')
print(np.shape(self.user_id))
# initializer = tf.contrib.layers.xavier_initializer()
# self.P = tf.Variable(initializer([self.num_user, num_factor] ))
# self.V = tf.Variable(initializer([self.num_user, num_factor * 1] ))
# self.Q = tf.Variable(initializer([self.num_item, num_factor] ))
# self.X = tf.Variable(initializer([self.num_item, num_factor] ))
self.P = tf.Variable(tf.truncated_normal([self.num_user, num_factor], stddev=0.001))
self.V = tf.Variable(tf.truncated_normal([self.num_user, num_factor * 1], stddev=0.001))
self.Q = tf.Variable(tf.truncated_normal([self.num_item, num_factor], stddev=0.001))
self.X = tf.Variable(tf.truncated_normal([self.num_item, num_factor], stddev=0.001))
self.A = tf.Variable(tf.truncated_normal([self.num_item, num_factor], stddev=0.001))
item_latent_factor_neg = tf.nn.embedding_lookup(self.Q, self.neg_item_id)
self.W = tf.Variable(tf.random_normal([self.num_item, self.num_factor * (1 + 1)], stddev=0.01))
self.b = tf.Variable(tf.random_normal([self.num_item], stddev=0.01))
# vertical conv layer
# self.conv_v = tf.nn.conv2d(1, n_v, (L, 1))
self.target_prediction = self._distance_self_attention(self.item_seq, self.user_id, self.item_id)
self.negative_prediction = self._distance_self_attention(self.item_seq, self.user_id, self.neg_item_id)
self.test_prediction = self._distance_self_attention(self.item_seq, self.user_id, self.item_id_test, isTrain=False)
self.user_param = self.user_latent_factor
self.user_param_2 = self.user_specific_bias
self.seq_param = self.out
self.seq_weight = self.weights
self.item_param_1, self.item_param_2 = self._getItemParam(self.item_id_test)
# - tf.reduce_sum(tf.log(tf.sigmoid(- self.target_prediction + self.negative_prediction)) )
# - tf.reduce_mean(tf.log(tf.sigmoid(self.target_prediction) + 1e-10)) - tf.reduce_mean( tf.log( tf.sigmoid(1 - self.negative_prediction) + 1e-10))
# tf.reduce_mean(tf.square(1 - self.negative_prediction)) + tf.reduce_mean(tf.square(self.target_prediction))
# tf.reduce_sum(tf.maximum(self.target_prediction - self.negative_prediction + 0.5, 0))
self.loss = tf.reduce_sum(tf.maximum(self.target_prediction - self.negative_prediction + 0.5, 0)) \
+ tf.losses.get_regularization_loss() + 0.001 * (
tf.nn.l2_loss(self.P) + tf.nn.l2_loss(self.V) + tf.nn.l2_loss(self.X) + tf.nn.l2_loss(self.Q))
norm_clip_value = 1
self.clip_P = tf.assign(self.P, tf.clip_by_norm(self.P, norm_clip_value, axes=[1]))
self.clip_Q = tf.assign(self.Q, tf.clip_by_norm(self.Q, norm_clip_value, axes=[1]))
self.clip_V = tf.assign(self.V, tf.clip_by_norm(self.V, norm_clip_value, axes=[1]))
self.clip_X = tf.assign(self.X, tf.clip_by_norm(self.X, norm_clip_value, axes=[1]))
self.optimizer = tf.train.AdagradOptimizer(self.learning_rate, initial_accumulator_value=0.05).minimize(self.loss) # GradientDescentOptimizer
return self
def getUserParam(self, user_id):
params = self.sess.run([self.user_param, self.seq_param, self.user_param_2, self.seq_weight], feed_dict={self.user_id: user_id,
self.item_seq: self.test_sequences[user_id, :]})
return params[0], params[1], params[2], params[3]
def getItemParam(self, item_id):
params = self.sess.run([self.item_param_1, self.item_param_2, self.bias_item], feed_dict={self.item_id_test: item_id})
return np.squeeze(params[0]), np.squeeze(params[1]), params[2]
def _getItemParam(self, item_id):
w_items = tf.nn.embedding_lookup(self.X, item_id)
w_items_2 = tf.nn.embedding_lookup(self.Q, item_id)
return w_items, w_items_2
def _distance_self_attention(self, item_seq, user_id, item_id, isTrain=True):
# horizontal conv layer
lengths = [i + 1 for i in range(self.L)]
out, out_h, out_v = None, None, None
# print(np.shape(item_seq)[1])
# item_latent_factor = self.add_timing_signal(item_latent_factor)
item_latent_factor = tf.nn.embedding_lookup(self.Q, item_seq) #+ self.user_specific_bias
item_latent_factor_2 = tf.nn.embedding_lookup(self.X, item_seq)
query = key = value = self.add_timing_signal(item_latent_factor)
if isTrain:
query = tf.layers.dense(inputs=query, name="linear_project", units=self.num_factor, activation=tf.nn.relu,
use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(), reuse=tf.AUTO_REUSE,
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=self.reg_rate)
)
#query = tf.layers.dropout(query, rate=0.0)
key = tf.layers.dense(inputs=key, name="linear_project", units=self.num_factor, activation=tf.nn.relu,
use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(), reuse=tf.AUTO_REUSE,
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=self.reg_rate),
)
#key = tf.layers.dropout(key, rate=0.3)
else:
query = tf.layers.dense(inputs=query, name="linear_project", units=self.num_factor, activation=tf.nn.relu,
use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(), reuse=tf.AUTO_REUSE,
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=self.reg_rate)
)
key = tf.layers.dense(inputs=key, name="linear_project", units=self.num_factor, activation=tf.nn.relu,
use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(), reuse=tf.AUTO_REUSE,
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=self.reg_rate)
)
logits = tf.matmul(query, key, transpose_b=True) / np.sqrt(self.num_factor)
print(np.shape(logits))
weights = tf.nn.softmax(logits, dim=-1, name="attention_weights")
mask = tf.ones([self.L, self.L])
mask = tf.matrix_set_diag(mask, tf.zeros([self.L]))
weights = weights * mask
out = tf.matmul(weights, item_latent_factor )
self.weights = weights
print(np.shape(item_latent_factor))
self.out = tf.reduce_mean(out, 1)
print(np.shape(self.out))
w_items = tf.nn.embedding_lookup(self.X, item_id)
w_items_2 = tf.nn.embedding_lookup(self.Q, item_id)
w_items_3 = tf.nn.embedding_lookup(self.V, user_id)#tf.nn.embedding_lookup(self.A, item_id)
self.bias_item = tf.nn.embedding_lookup(self.b, item_id)
x_tmp = []
for i in range(np.shape(item_id)[1]):
x_tmp.append(self.out)
x = tf.stack(x_tmp)
print(np.shape(x))
print(np.shape(w_items))
x = tf.transpose(x, [1, 0, 2])
self.user_latent_factor = tf.nn.embedding_lookup(self.P, user_id)
u_tmp = []
for i in range(np.shape(item_id)[1]):
u_tmp.append(self.user_latent_factor)
u = tf.stack(u_tmp)
print(np.shape(u))
u = tf.transpose(u, [1, 0, 2])
self.user_specific_bias = tf.nn.embedding_lookup(self.V, user_id)
u_tmp_2 = []
for i in range(np.shape(item_id)[1]):
u_tmp_2.append(self.user_specific_bias)
u_2 = tf.stack(u_tmp_2)
print(np.shape(u_2))
u_2 = tf.transpose(u_2, [1, 0, 2])
self.alpha = 0.2
if isTrain:
res = self.alpha * tf.reduce_sum(tf.nn.dropout(tf.square(w_items - u), 1), 2) + (1-self.alpha) * tf.reduce_sum(tf.nn.dropout(tf.square(x -w_items_2 ),1),2) #+ 0.1 * tf.reduce_sum(tf.square(x - u), 2)
else:
res = self.alpha * tf.reduce_sum(tf.square(w_items - u), 2) + (1 - self.alpha) * tf.reduce_sum(
tf.square(x - w_items_2 ), 2)
print(np.shape(res))
return tf.squeeze(res)
def _distance_multihead(self, item_seq, user_latent_factor, item_id):
# horizontal conv layer
lengths = [i + 1 for i in range(self.L)]
out, out_h, out_v = None, None, None
# item_latent_factor = self.add_timing_signal(item_latent_factor)
item_latent_factor = tf.nn.embedding_lookup(self.Q, item_seq)
item_latent_factor_2 = tf.nn.embedding_lookup(self.X, item_seq)
query = key = self.add_timing_signal(item_latent_factor)
out = self.multihead_attention(queries=query, keys=key, value=item_latent_factor, reuse=tf.AUTO_REUSE)
out = tf.reduce_mean(out, 1)
query_2 = key_2 = out
query_2 = tf.layers.dense(inputs=query_2, name="linear_project1", units=self.num_factor, activation=None,
use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(), reuse=tf.AUTO_REUSE,
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=self.reg_rate)
)
key_2 = tf.layers.dense(inputs=key_2, name="linear_project1", units=self.num_factor, activation=None,
use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(), reuse=tf.AUTO_REUSE,
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=self.reg_rate)
)
# value = tf.layers.dense(inputs= key, name="linear_project", units = seq_len_item, activation = None, kernel_initializer=tf.random_normal_initializer, reuse=True)
# b = tf.Variable(tf.random_normal([seq_len_user], stddev=1))
logits_2 = tf.matmul(query_2, key_2, transpose_b=True) / np.sqrt(self.num_factor)
weights_2 = tf.nn.softmax(logits_2, name="attention_weights1")
mask_2 = tf.ones([self.L, self.L])
mask_2 = tf.matrix_set_diag(mask_2, tf.zeros([self.L]))
weights_2 = weights_2 * mask_2
out_2 = tf.reduce_mean(tf.matmul(weights_2, out) , 1)
print("--------------")
print(np.shape(out))
print(np.shape(out_2))
w_items = tf.nn.embedding_lookup(self.X, item_id)
w_items_2 = tf.nn.embedding_lookup(self.Q, item_id)
b_items = tf.nn.embedding_lookup(self.b, item_id)
item_specific_bias = tf.nn.embedding_lookup(self.X, item_id)
x_tmp = []
for i in range(np.shape(w_items)[1]):
x_tmp.append(out)
x = tf.stack(x_tmp)
print(np.shape(x))
print(np.shape(w_items))
x = tf.transpose(x, [1, 0, 2])
u_tmp = []
for i in range(np.shape(w_items)[1]):
u_tmp.append(user_latent_factor)
u = tf.stack(u_tmp)
print(np.shape(u))
u = tf.transpose(u, [1, 0, 2])
# res = tf.reduce_sum(tf.multiply(x, w_items), 2) + b_items
res = 0.2 * tf.reduce_sum(tf.square(w_items - u), 2) + 0.8 * tf.reduce_sum(tf.square(x- w_items_2),2) # + 0.1 * tf.reduce_sum(tf.square(x - u), 2)
print(np.shape(res))
return tf.squeeze(res)
def execute(self, train_data, test_data):
self.prepare_data(train_data, test_data)
init = tf.global_variables_initializer()
self.sess.run(init)
for epoch in range(self.epochs):
if self.verbose:
print("Epoch: %04d;" % (epoch))
self.train(train_data)
if (epoch) % self.T == 0 and epoch >= 5:
print("Epoch: %04d; " % (epoch), end='')
self.test(test_data)
def prepare_data(self, train_data, test_data):
self.sequences = train_data.sequences.sequences
# print(self.sequences)
self.targets = train_data.sequences.targets
self.users = train_data.sequences.user_ids.reshape(-1, 1)
all_items = set(np.arange(self.num_item - 1) + 1)
self.all_items = all_items
# print(all_items) # from 1 to 1679
self.test_data = dict()
test = test_data.tocsr()
for user, row in enumerate(test):
self.test_data[user] = list(set(row.indices))
self.x = []
for i, u in enumerate(self.users.squeeze()):
tar = set([int(t) for t in self.targets[i]])
# print(tar)
seq = set([int(t) for t in self.sequences[i]])
self.x.append(list(all_items - tar))
# print(self.test_data[u][0] in self.x[i])
self.x = np.array(self.x)
if not self.neg_items:
# all_items = set(np.arange(self.num_item - 1) + 1)
train = train_data.tocsr()
for user, row in enumerate(train):
# print(user)
# print(row.indices)
# print(0 in row.indices)
self.neg_items[user] = list(all_items - set(row.indices))
# print(self.test_data[user][0] in self.neg_items[user])
print("Data Preparation Finish.")
def train(self, train_data):
# print(users)
self.num_training = len(self.sequences)
self.total_batch = int(self.num_training / self.batch_size)
L, T = train_data.sequences.L, train_data.sequences.T
self.test_sequences = train_data.test_sequences.sequences
# print(self.test_sequences)
idxs = np.random.permutation(
self.num_training) # shuffled ordering np.random.choice(self.num_training, self.num_training, replace=True) #
sequences_random = [i.tolist() for i in list(self.sequences[idxs])]
targets_random = list(self.targets[idxs])
users_random = [i[0] for i in list(self.users[idxs])]
self.x_random = list(self.x[idxs])
item_random_neg = self._get_neg_items_sbpr(self.users.squeeze(), train_data, self.num_neg * self.num_T)
# # train
for i in range(self.total_batch):
start_time = time.time()
batch_user = users_random[i * self.batch_size:(i + 1) * self.batch_size]
batch_seq = sequences_random[i * self.batch_size:(i + 1) * self.batch_size]
batch_item = targets_random[i * self.batch_size:(i + 1) * self.batch_size]
batch_item_neg = item_random_neg[i * self.batch_size:(i + 1) * self.batch_size]
# print(batch_item_neg)
#, self.clip_P, self.clip_Q, self.clip_X
_, loss = self.sess.run((self.optimizer, self.loss), feed_dict={self.user_id: batch_user,
self.item_seq: batch_seq,
self.item_id: batch_item,
self.neg_item_id: batch_item_neg,
self.isTrain: True})
#
if i % self.display_step == 0:
if self.verbose:
print("Index: %04d; cost= %.9f" % (i + 1, np.mean(loss)))
# print("one iteration: %s seconds." % (time.time() - start_time))
def test(self, test_data):
# print(test_data.user_map)
# print(self.test_data)
self.test_users = []
for i in range(self.num_user):
self.test_users.append(i)
# print(self.test_users)
evaluate1(self)
def save(self, path):
saver = tf.train.Saver()
saver.save(self.sess, path)
def predict(self, user_id, item_id):
# print(user_id)
# print(len(self.test_sequences))
# print(self.test_sequences[user_id, :])
# user_id_2 = [i+1 for i in user_id]
item_id = [[i] for i in item_id]
# print(len(item_id))
return -self.sess.run([self.test_prediction], feed_dict={self.user_id: user_id,
self.item_seq: self.test_sequences[user_id, :],
self.item_id_test: item_id})[0]
def _weight_variable(self, shape):
initial = tf.random_normal(shape, stddev=0.1)
return tf.Variable(initial)
def _bias_variable(self, shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def _get_neg_items(self, users, interactions, n):
# users = users.squeeze()
print(users.shape[0])
neg_items_samples = np.zeros((users.shape[0], n))
# if not self.neg_items:
# all_items = set(np.arange(self.num_item - 1) + 1)
# train = interactions.tocsr()
#
# for user, row in enumerate(train):
# self.neg_items[user] = list(all_items - set(row.indices))
for i, u in enumerate(users):
for j in range(n):
x = self.neg_items[u]
neg_items_samples[i, j] = x[np.random.randint(len(x))]
return neg_items_samples
def _get_neg_items_sbpr(self, users, interactions, n):
# print("start sampling")
# print(targets)
# users = users.squeeze()
neg_items_samples = np.zeros((users.shape[0], n))
# all_items = None
# if not self.neg_items:
# all_items = set(np.arange(self.num_item - 1) + 1)
# train = interactions.tocsr()
#
# for user, row in enumerate(train):
# self.neg_items[user] = list(all_items - set(row.indices))
print(len(users))
for i, u in enumerate(users):
for j in range(n):
# print(int(targets[i][0]))
neg_items_samples[i, j] = self.x_random[i][np.random.randint(len(self.x_random[i]))]
# print("end sampling")
return neg_items_samples
def add_timing_signal(self, x, min_timescale=1.0, max_timescale=1.0e4):
"""Adds a bunch of sinusoids of different frequencies to a Tensor.
Each channel of the input Tensor is incremented by a sinusoid of a
different frequency and phase.
This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
memory inputs to attention.
The use of relative position is possible because sin(x+y) and cos(x+y) can
be experessed in terms of y, sin(x) and cos(x).
In particular, we use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels / 2. For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
Args:
x: a Tensor with shape [batch, length, channels]
min_timescale: a float
max_timescale: a float
Returns:
a Tensor the same shape as x.
"""
with tf.name_scope("add_timing_signal", values=[x]):
length = tf.shape(x)[1]
channels = tf.shape(x)[2]
position = tf.to_float(tf.range(length))
num_timescales = channels // 2
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
(tf.to_float(num_timescales) - 1)
)
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment
)
scaled_time = (tf.expand_dims(position, 1) *
tf.expand_dims(inv_timescales, 0))
signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
signal = tf.pad(signal, [[0, 0], [0, tf.mod(channels, 2)]])
signal = tf.reshape(signal, [1, length, channels])
return x + signal
def normalize(self, inputs,
epsilon=1e-8,
scope="ln",
reuse=None):
'''Applies layer normalization.
Args:
inputs: A tensor with 2 or more dimensions, where the first dimension has
`batch_size`.
epsilon: A floating number. A very small number for preventing ZeroDivision Error.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A tensor with the same shape and data dtype as `inputs`.
'''
with tf.variable_scope(scope, reuse=reuse):
inputs_shape = inputs.get_shape()
params_shape = inputs_shape[-1:]
mean, variance = tf.nn.moments(inputs, [-1], keep_dims=True)
beta = tf.Variable(tf.zeros(params_shape))
gamma = tf.Variable(tf.ones(params_shape))
normalized = (inputs - mean) / ((variance + epsilon) ** (.5))
outputs = gamma * normalized + beta
return outputs
def multihead_attention(self, queries, keys, value, num_units=None, num_heads=2, dropout_rate=0, is_training=True, causality=False, scope="multihead_attention", reuse=None):
with tf.variable_scope(scope, reuse=reuse):
if num_units is None:
num_units = queries.get_shape().as_list()[-1]
Q = tf.layers.dense(queries, num_units, name="project_q", activation=tf.nn.relu,
use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=self.reg_rate)) # (batch size, sequence length, dim)
K = tf.layers.dense(keys, num_units, name="project_k", activation=tf.nn.relu,
use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=self.reg_rate))
V = tf.layers.dense(value, num_units, name="project_v",activation=tf.nn.relu,
use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=self.reg_rate))
Q_ = tf.concat(tf.split(Q, num_heads, axis=2),axis=0) #( h * batch size, seq len, dim/h)
K_ = tf.concat(tf.split(K, num_heads, axis=2),axis=0)
V_ = tf.concat(tf.split(value, num_heads, axis=2),axis=0)
outputs = tf.matmul(Q_, tf.transpose(K_, [0, 2, 1]))
# scale
outputs = outputs / ( K_.get_shape().as_list()[-1] ** 0.5)
# key masking
key_masks = tf.sign(tf.abs(tf.reduce_sum(keys, axis=-1))) # (batch size, seq len)
key_masks = tf.tile(key_masks, [num_heads, 1])
key_masks = tf.tile(tf.expand_dims(key_masks, 1), [ 1, tf.shape(queries)[1], 1])
paddings = tf.ones_like(outputs) * (-2**32 + 1)
outputs = tf.where(tf.equal(key_masks, 0), paddings, outputs)
if causality:
diag_vals = tf.ones_like(outputs[0,:,:])
tril = tf.contrib.linalg.LinearOperatorTriL(diag_vals).to_dense()
masks = tf.tile(tf.expand_dims(tril, 0), [tf.shape(outputs)[0], 1, 1])
paddings = tf.ones_like(masks) * ( -2**32 +1 )
outputs = tf.where(tf.equal(key_masks, 0), paddings, outputs)
outputs = tf.nn.sigmoid(outputs)
query_masks = tf.sign(tf.abs(tf.reduce_sum(queries, axis=-1)))
query_masks = tf.tile(query_masks, [num_heads, 1])
query_masks = tf.tile(tf.expand_dims(query_masks, -1), [1,1, tf.shape(keys)[1]])
outputs *= query_masks
outputs = tf.layers.dropout(outputs, rate=dropout_rate, training=tf.convert_to_tensor(is_training))
outputs = tf.matmul(outputs, V_)
outputs = tf.concat(tf.split(outputs, num_heads, axis=0), axis=2)
#outputs += value
#outputs = self.normalize(outputs, reuse=tf.AUTO_REUSE)
return outputs
def feedforward(self, inputs,
num_units=[400, 100],
scope="multihead_attention",
reuse=None):
'''Point-wise feed forward net.
Args:
inputs: A 3d tensor with shape of [N, T, C].
num_units: A list of two integers.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A 3d tensor with the same shape and dtype as inputs
'''
with tf.variable_scope(scope, reuse=reuse):
# Inner layer
params = {"inputs": inputs, "filters": num_units[0], "kernel_size": 1,
"activation": tf.nn.relu, "use_bias": True}
outputs = tf.layers.conv1d(**params,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=self.reg_rate))
# Readout layer
params = {"inputs": outputs, "filters": num_units[1], "kernel_size": 1,
"activation": None, "use_bias": True}
outputs = tf.layers.conv1d(**params, kernel_initializer=tf.contrib.layers.xavier_initializer(),
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=self.reg_rate))
# Residual connection
outputs += inputs
# Normalize
outputs = self.normalize(outputs, reuse=tf.AUTO_REUSE)
return outputs
def add_timing_signal_nd(self, x, min_timescale=1.0, max_timescale=1.0e4):
""" Adds a bunch of sinusoids of different frequencies to a Tensor.
Each channel of the input Tensor is incremented by a sinusoid of a
different frequency and phase in one of the positional dimensions.
This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query and
the memory inputs to attention.
The use of relative position is possible because sin(a+b) and cos(a+b)
can be experessed in terms of b, sin(a) and cos(a).
x is a Tensor with n "positional" dimensions, e.g. one dimension for a
sequence or two dimensions for an image
We use a geometric sequence of timescales starting with min_timescale
and ending with max_timescale. The number of different timescales is
equal to channels // (n * 2). For each timescale, we generate the two
sinusoidal signals sin(timestep/timescale) and cos(timestep/timescale).
All of these sinusoids are concatenated in the channels dimension.
Args:
x: a Tensor with shape [batch, d1 ... dn, channels]
min_timescale: a float
max_timescale: a float
Returns:
a Tensor the same shape as x.
"""
static_shape = x.get_shape().as_list()
num_dims = len(static_shape) - 2
channels = tf.shape(x)[-1]
num_timescales = channels // (num_dims * 2)
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
(tf.to_float(num_timescales) - 1)
)
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment
)
for dim in range(num_dims):
length = tf.shape(x)[dim + 1]
position = tf.to_float(tf.range(length))
scaled_time = tf.expand_dims(position, 1) * tf.expand_dims(
inv_timescales, 0)
signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
prepad = dim * 2 * num_timescales
postpad = channels - (dim + 1) * 2 * num_timescales
signal = tf.pad(signal, [[0, 0], [prepad, postpad]])
for _ in range(1 + dim):
signal = tf.expand_dims(signal, 0)
for _ in range(num_dims - 1 - dim):
signal = tf.expand_dims(signal, -2)
x += signal
return x