def __init__(self, input_dim=None, output_dim=1, init_path=None, opt_algo='gd', learning_rate=1e-2, l2_weight=0,
random_seed=None):
Model.__init__(self)
# 声明参数
init_vars = [('w', [input_dim, output_dim], 'xavier', dtype),
('b', [output_dim], 'zero', dtype)]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
# 用稀疏的placeholder
self.X = tf.sparse_placeholder(dtype)
self.y = tf.placeholder(dtype)
# init参数
self.vars = init_var_map(init_vars, init_path)
w = self.vars['w']
b = self.vars['b']
# sigmoid(wx+b)
xw = tf.sparse_tensor_dense_matmul(self.X, w)
logits = tf.reshape(xw + b, [-1])
self.y_prob = tf.sigmoid(logits)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=self.y, logits=logits)) + \
l2_weight * tf.nn.l2_loss(xw)
self.optimizer = get_optimizer(opt_algo, learning_rate, self.loss)
# GPU设定
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
# 初始化图里的参数
tf.global_variables_initializer().run(session=self.sess)
def __init__(self, field_sizes=None, embed_size=10, filter_sizes=None, layer_acts=None, drop_out=None,
init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype))
init_vars.append(('f1', [embed_size, filter_sizes[0], 1, 2], 'xavier', dtype))
init_vars.append(('f2', [embed_size, filter_sizes[1], 2, 2], 'xavier', dtype))
init_vars.append(('w1', [2 * 3 * embed_size, 1], 'xavier', dtype))
init_vars.append(('b1', [1], 'zero', dtype))
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)], 1)
l = xw
l = tf.transpose(tf.reshape(l, [-1, num_inputs, embed_size, 1]), [0, 2, 1, 3])
f1 = self.vars['f1']
l = tf.nn.conv2d(l, f1, [1, 1, 1, 1], 'SAME')
l = tf.transpose(
max_pool_4d(
tf.transpose(l, [0, 1, 3, 2]),
int(num_inputs / 2)),
[0, 1, 3, 2])
f2 = self.vars['f2']
l = tf.nn.conv2d(l, f2, [1, 1, 1, 1], 'SAME')
l = tf.transpose(
max_pool_4d(
tf.transpose(l, [0, 1, 3, 2]), 3),
[0, 1, 3, 2])
l = tf.nn.dropout(
activate(
tf.reshape(l, [-1, embed_size * 3 * 2]),
layer_acts[0]),
self.layer_keeps[0])
w1 = self.vars['w1']
b1 = self.vars['b1']
l = tf.matmul(l, w1) + b1
l = tf.squeeze(l)
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
self.optimizer = get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self, input_dim=None, output_dim=1, init_path=None, opt_algo='gd', learning_rate=1e-2, l2_weight=0,
random_seed=None):
Model.__init__(self)
init_vars = [('w', [input_dim, output_dim], 'xavier', dtype),
('b', [output_dim], 'zero', dtype)]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = tf.sparse_placeholder(dtype)
self.y = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path) # 初始化变量w, b
w = self.vars['w']
b = self.vars['b']
xw = tf.sparse_tensor_dense_matmul(self.X, w)
logits = tf.reshape(xw + b, [-1])
self.y_prob = tf.sigmoid(logits)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=self.y, logits=logits)) + \
l2_weight * tf.nn.l2_loss(xw)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
input_dim=None,
output_dim=1,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
l2_weight=0,
random_seed=None):
init_vars = [('w', [input_dim, output_dim], 'tnormal', dtype),
('b', [output_dim], 'zero', dtype)]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = tf.sparse_placeholder(dtype)
self.y = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w = self.vars['w']
b = self.vars['b']
logits = tf.sparse_tensor_dense_matmul(self.X, w) + b
self.y_prob = tf.sigmoid(logits)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits, self.y)) + \
l2_weight * tf.nn.l2_loss(w)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.initialize_all_variables().run(session=self.sess)
def __init__(self, layer_sizes=None, layer_acts=None, layer_keeps=None, layer_l2=None, init_path=None,
opt_algo='gd', learning_rate=1e-2, random_seed=None):
init_vars = []
num_inputs = len(layer_sizes[0])
factor_order = layer_sizes[1]
for i in range(num_inputs):
layer_input = layer_sizes[0][i]
layer_output = factor_order
init_vars.append(('w0_%d' % i, [layer_input, layer_output], 'tnormal', dtype))
init_vars.append(('b0_%d' % i, [layer_output], 'zero', dtype))
init_vars.append(('w1', [num_inputs * factor_order, layer_sizes[2]], 'tnormal', dtype))
init_vars.append(('b1', [layer_sizes[2]], 'zero', dtype))
for i in range(2, len(layer_sizes) - 1):
layer_input = layer_sizes[i]
layer_output = layer_sizes[i + 1]
init_vars.append(('w%d' % i, [layer_input, layer_output], 'tnormal', dtype))
init_vars.append(('b%d' % i, [layer_output], 'zero', dtype))
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['w0_%d' % i] for i in range(num_inputs)]
b0 = [self.vars['b0_%d' % i] for i in range(num_inputs)]
l = tf.nn.dropout(
utils.activate(
tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) + b0[i]
for i in range(num_inputs)], 1),
layer_acts[0]),
layer_keeps[0])
for i in range(1, len(layer_sizes) - 1):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
l = tf.nn.dropout(
utils.activate(
tf.matmul(l, wi) + bi,
layer_acts[i]),
layer_keeps[i])
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
if layer_l2 is not None:
for i in range(num_inputs):
self.loss += layer_l2[0] * tf.nn.l2_loss(w0[i])
for i in range(1, len(layer_sizes) - 1):
wi = self.vars['w%d' % i]
# bi = self.vars['b%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
input_dim=None,
output_dim=1,
factor_order=10,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
l2_w=0,
l2_v=0,
random_seed=None):
Model.__init__(self)
init_vars = [('w', [input_dim, output_dim], 'tnormal', dtype),
('v', [input_dim, factor_order], 'tnormal', dtype),
('b', [output_dim], 'zero', dtype)]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = tf.sparse_placeholder(dtype)
self.y = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w = self.vars['w']
v = self.vars['v']
b = self.vars['b']
"""
SparseTensor(values=[1, 2], indices=[[0, 0], [1, 2]], shape=[3, 4])
[[1, 0, 0, 0]
[0, 0, 2, 0]
[0, 0, 0, 0]]
http://www.jianshu.com/p/c233e09d2f5f
"""
# 得到x*x的张量
X_square = tf.SparseTensor(self.X.indices,
tf.square(self.X.values),
tf.to_int64(tf.shape(self.X)))
xv = tf.square(tf.sparse_tensor_dense_matmul(self.X, v))
p = 0.5 * tf.reshape(
tf.reduce_sum(
xv - tf.sparse_tensor_dense_matmul(X_square, tf.square(v)),
1), [-1, output_dim])
xw = tf.sparse_tensor_dense_matmul(self.X, w)
logits = tf.reshape(xw + b + p, [-1]) # 预测出的目标值
self.y_prob = tf.sigmoid(logits) #
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y)) + \
l2_w * tf.nn.l2_loss(xw) + \
l2_v * tf.nn.l2_loss(xv)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self, field_sizes=None, embed_size=10, layer_sizes=None, layer_acts=None, drop_out=None,
embed_l2=None, layer_l2=None, init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
print('num_inputs:{0}\\t\tlayer_size:{1}'.format(num_inputs, layer_sizes))
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype)) # 为每个特征值初始化一个长度为10的向量
node_in = num_inputs * embed_size # 将每个特征embeding 为10维的向量， 总共16个特征，所以是160个输入 网络为[160,500,1]
for i in range(len(layer_sizes)):
init_vars.append(('w%d' % i, [node_in, layer_sizes[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
node_in = layer_sizes[i]
print('init_vars:', init_vars)
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)],
1) # 将每个特征的隐含向量连起来，组成网络的输入，160维
l = xw
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
print('第{0}个隐藏层l.shape, wi.shape, bi.shape'.format(i), l.shape, wi.shape, bi.shape)
l = tf.nn.dropout(
utils.activate(
tf.matmul(l, wi) + bi,
layer_acts[i]),
self.layer_keeps[i])
l = tf.squeeze(l) # 从tensor中删除所有大小是1的维度
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
if layer_l2 is not None:
self.loss += embed_l2 * tf.nn.l2_loss(xw)
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self, field_sizes=None, embed_size=10, layer_sizes=None, layer_acts=None, drop_out=None,
embed_l2=None, layer_l2=None, init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
print('num_inputs:{0}\\t\tlayer_size:{1}'.format(num_inputs, layer_sizes))
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype)) # 为每个特征值初始化一个长度为10的向量
node_in = num_inputs * embed_size # 将每个特征embeding 为10维的向量， 总共16个特征，所以是160个输入 网络为[160， 500， 1]
for i in range(len(layer_sizes)):
init_vars.append(('w%d' % i, [node_in, layer_sizes[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
node_in = layer_sizes[i]
print('init_vars:', init_vars)
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)], 1) # 将每个特征的隐含向量连起来，组成网络的输入，160维
l = xw
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
print('第{0}个隐藏层l.shape, wi.shape, bi.shape'.format(i), l.shape, wi.shape, bi.shape)
l = tf.nn.dropout(
utils.activate(
tf.matmul(l, wi) + bi,
layer_acts[i]),
self.layer_keeps[i])
l = tf.squeeze(l) # 从tensor中删除所有大小是1的维度
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
if layer_l2 is not None:
self.loss += embed_l2 * tf.nn.l2_loss(xw)
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
feature_size,
field_size,
optimizer_type='gd',
learning_rate=1e-2,
l2_reg=0,
verbose=False,
random_seed=None,
eval_metric=roc_auc_score,
greater_is_better=True,
epoch=10,
batch_size=1024):
Model.__init__(self, eval_metric, greater_is_better, epoch, batch_size,
verbose)
init_vars = [('w', [feature_size, 1], 'zero', tf.float32),
('b', [1], 'zero', tf.float32)]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.feat_index = tf.placeholder(tf.int32,
shape=[None, None],
name="feat_index") # None * F
self.feat_value = tf.placeholder(tf.float32,
shape=[None, None],
name="feat_value") # None * F
self.label = tf.placeholder(tf.float32,
shape=[None, 1],
name="label") # None * 1
self.vars = utils.init_var_map(init_vars)
w = self.vars['w']
b = self.vars['b']
self.embeddings = tf.nn.embedding_lookup(
w, self.feat_index) # None * F * K
feat_value = tf.reshape(self.feat_value, shape=[-1, field_size, 1])
self.embeddings = tf.multiply(self.embeddings, feat_value)
logits = tf.reduce_sum(self.embeddings, 1) + b
self.y_prob = tf.sigmoid(logits)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=self.label, logits=logits)) + \
l2_reg * tf.nn.l2_loss(self.embeddings)
self.optimizer = utils.get_optimizer(optimizer_type, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
input_dim=None,
output_dim=1,
factor_order=10,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
l2_w=0,
l2_v=0,
random_seed=None):
Model.__init__(self)
# 一次、二次交叉、偏置项
init_vars = [('w', [input_dim, output_dim], 'xavier', dtype),
('v', [input_dim, factor_order], 'xavier', dtype),
('b', [output_dim], 'zero', dtype)]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = tf.sparse_placeholder(dtype)
self.y = tf.placeholder(dtype)
self.vars = init_var_map(init_vars, init_path)
w = self.vars['w']
v = self.vars['v']
b = self.vars['b']
# [(x1+x2+x3)^2 - (x1^2+x2^2+x3^2)]/2
# 先计算所有的交叉项，再减去平方项(自己和自己相乘)
X_square = tf.SparseTensor(self.X.indices,
tf.square(self.X.values),
tf.to_int64(tf.shape(self.X)))
xv = tf.square(tf.sparse_tensor_dense_matmul(self.X, v))
p = 0.5 * tf.reshape(
tf.reduce_sum(
xv - tf.sparse_tensor_dense_matmul(X_square, tf.square(v)),
1), [-1, output_dim])
xw = tf.sparse_tensor_dense_matmul(self.X, w)
logits = tf.reshape(xw + b + p, [-1])
self.y_prob = tf.sigmoid(logits)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y)) + \
l2_w * tf.nn.l2_loss(xw) + \
l2_v * tf.nn.l2_loss(xv)
self.optimizer = get_optimizer(opt_algo, learning_rate, self.loss)
#GPU设定
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
# 图中所有variable初始化
tf.global_variables_initializer().run(session=self.sess)
def __init__(self, net_type, net_argv, init_path, init_argv, dim_argv,
batch_size, opt_argv):
if net_type == "nn":
self.graph = tf.Graph()
nb_dim = dim_argv[0]
depth, h_dims, act_func = net_argv
with self.graph.as_default():
var_init = []
if not init_path:
_j = 0
for _i in range(depth - 1):
var_init.extend([
("W{}".format(_i), [h_dims[_i], h_dims[_i + 1]],
init_argv[_j][0], init_argv[_j][1:]),
("b{}".format(_i), [1, h_dims[_i + 1]],
init_argv[_j + 1][0], init_argv[_j + 1][1:])
])
_j += 2
var_map = init_var_map(init_path, var_init)
self.W = [0] * (depth - 1)
self.b = [0] * (depth - 1)
for _i in range(depth - 1):
self.W[_i] = tf.Variable(var_map["W{}".format(_i)])
self.b[_i] = tf.Variable(var_map["b{}".format(_i)])
self.x_vec = tf.placeholder(tf.float32, shape=[1, nb_dim])
self.batch_x_vecs = tf.placeholder(tf.float32,
shape=[batch_size, nb_dim])
self.batch_value_labels = tf.placeholder(tf.float32,
shape=[batch_size, 1])
self.value_prediction = self.forward(net_type, depth, act_func,
self.x_vec,
[self.W, self.b])
self.batch_value_predictions = self.forward(
net_type, depth, act_func, self.batch_x_vecs,
[self.W, self.b])
square_loss_value = tf.square(self.batch_value_labels -
self.batch_value_predictions)
if opt_argv[-1] == "sum":
self.loss_value = tf.reduce_sum(square_loss_value)
elif opt_argv[-1] == "mean":
self.loss_value = tf.reduce_mean(square_loss_value)
self.opt_value = build_optimizer(opt_argv, self.loss_value)
#self.init = tf.initialize_all_variables()
self.init = tf.global_variables_initializer()
#self.log = "net_type={}\tnet_argv={}\tinit_path={}\tinit_argv={}\tdim_argv={}\tbatch_size={}\topt_argv={}" \
# .format(net_type, net_argv, init_path, init_argv, dim_argv, batch_size, opt_argv)
self.log = "net_type={}\tnet_argv={}\tinit_path={}\tinit_argv={}\tdim_argv={}\tbatch_size={}\topt_argv={}" \
.format(net_type, net_argv, init_path, init_argv, dim_argv, batch_size, opt_argv)
def __init__(self, field_sizes=None, embed_size=10, layer_sizes=None, layer_acts=None, drop_out=None,
embed_l2=None, layer_l2=None, init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype))
node_in = num_inputs * embed_size
for i in range(len(layer_sizes)):
init_vars.append(('w%d' % i, [node_in, layer_sizes[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
node_in = layer_sizes[i]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)], 1)
l = xw
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
print(l.shape, wi.shape, bi.shape)
l = tf.nn.dropout(
activate(
tf.matmul(l, wi) + bi,
layer_acts[i]),
self.layer_keeps[i])
l = tf.squeeze(l)
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
if layer_l2 is not None:
self.loss += embed_l2 * tf.nn.l2_loss(xw)
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.optimizer = get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
input_dim=None,
output_dim=1,
factor_dim=10,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
l2_weight=0,
l2_v=0,
random_seed=None):
Model.__init__(self)
init_vars = [('w', [input_dim, output_dim], 'xavier', dtype),
('v', [input_dim, factor_dim], 'xavier', dtype),
('b', [output_dim], 'zero', dtype)]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = tf.sparse_placeholder(dtype) # n * input_dim
self.y = tf.placeholder(dtype) # n * 1
self.vars = utils.init_var_map(init_vars, init_path)
w = self.vars['w'] # input_dim * output_dim
v = self.vars['v'] # input_dim * factor_dim
b = self.vars['b'] # input_dim
# n * input_dim
X_square = tf.SparseTensor(self.X.indices,
tf.square(self.X.values),
tf.to_int64(tf.shape(self.X)))
# n * input_dim * input_dim * factor_dim => n * factor_dim
xv = tf.square(tf.sparse_tensor_dense_matmul(self.X, v))
# 二次项 n * factor_dim-n * factor_dim , 再按factor_dim求和, n * output_dim
p = 0.5 * tf.reshape(
tf.reduce_sum(
xv - tf.sparse_tensor_dense_matmul(X_square, tf.square(v)),
1), [-1, output_dim])
xw = tf.sparse_tensor_dense_matmul(self.X, w) # n * output_dim
l = tf.reshape(xw + b + p, [-1]) # n
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l,labels=self.y)) \
+ l2_weight * tf.nn.l2_loss(xw) + l2_v * tf.nn.l2_loss(xv)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
input_dim=None,
output_dim=1,
factor_order=10,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
l2_w=0,
l2_v=0,
random_seed=None):
init_vars = [('w', [input_dim, output_dim], 'tnormal', dtype),
('v', [input_dim, factor_order], 'tnormal', dtype),
('b', [output_dim], 'zero', dtype)]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = tf.sparse_placeholder(dtype)
self.y = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w = self.vars['w']
v = self.vars['v']
b = self.vars['b']
X_square = tf.SparseTensor(self.X.indices,
tf.square(self.X.values), self.X.shape)
p = 0.5 * tf.reshape(
tf.reduce_sum(
tf.square(tf.sparse_tensor_dense_matmul(self.X, v)) -
tf.sparse_tensor_dense_matmul(X_square, tf.square(v)), 1),
[-1, output_dim])
logits = tf.sparse_tensor_dense_matmul(self.X, w) + b + p
self.y_prob = tf.sigmoid(logits)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits, self.y)) + \
l2_w * tf.nn.l2_loss(w) + \
l2_v * tf.nn.l2_loss(v)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.initialize_all_variables().run(session=self.sess)
def __init__(self,
input_dim=None,
output_dim=1,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
l2_weight=0,
sync=False,
workers=20):
Model.__init__(self)
#self.graph = tf.Graph()
#with self.graph.as_default():
with tf.device('/cpu:0'):
self.X = tf.sparse_placeholder(dtype)
self.y = tf.placeholder(dtype)
init_vars = [('w', [input_dim, output_dim], 'xavier', dtype),
('b', [output_dim], 'zero', dtype)]
self.vars = utils.init_var_map(init_vars, init_path)
w = self.vars['w']
b = self.vars['b']
xw = tf.sparse_tensor_dense_matmul(self.X, w)
logits = tf.reshape(xw + b, [-1])
self.y_prob = tf.sigmoid(logits)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=self.y, logits=logits)) + \
l2_weight * tf.nn.l2_loss(xw)
self.global_step = _variable_on_cpu(
'global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
if sync:
self.optimizer = utils.get_sync_optimizer(opt_algo, learning_rate,
workers)
else:
self.optimizer = utils.get_optimizer(opt_algo, learning_rate)
self.train_op = self.optimizer.minimize(self.loss,
global_step=self.global_step)
def __init__(self, input_dim=None, output_dim=1, factor_order=10, init_path=None, opt_algo='gd', learning_rate=1e-2,
l2_w=0, l2_v=0, random_seed=None):
Model.__init__(self)
init_vars = [('w', [input_dim, output_dim], 'xavier', dtype),
('v', [input_dim, factor_order], 'xavier', dtype),
('b', [output_dim], 'zero', dtype)]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = tf.sparse_placeholder(dtype)
self.y = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w = self.vars['w']
v = self.vars['v']
b = self.vars['b']
X_square = tf.SparseTensor(self.X.indices, tf.square(self.X.values), tf.to_int64(tf.shape(self.X)))
xv = tf.square(tf.sparse_tensor_dense_matmul(self.X, v))
p = 0.5 * tf.reshape(
tf.reduce_sum(xv - tf.sparse_tensor_dense_matmul(X_square, tf.square(v)), 1),
[-1, output_dim])
xw = tf.sparse_tensor_dense_matmul(self.X, w)
logits = tf.reshape(xw + b + p, [-1])
self.y_prob = tf.sigmoid(logits)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y)) + \
l2_w * tf.nn.l2_loss(xw) + \
l2_v * tf.nn.l2_loss(xv)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
input_dim=None,
output_dim=1,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
l2_weight=0,
random_seed=None):
Model.__init__(self)
init_vars = [('w', [input_dim, output_dim], 'xavier', dtype),
('b', [output_dim], 'zero', dtype)]
self.graph = tf.Graph()
# 设置新的默认图
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = tf.sparse_placeholder(dtype)
self.y = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w = self.vars['w']
b = self.vars['b']
xw = tf.sparse_tensor_dense_matmul(self.X, w)
logits = tf.reshape(xw + b, [-1])
self.y_prob = tf.sigmoid(logits)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=self.y, logits = logits))\
+ l2_weight*tf.nn.l2_loss(xw)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True #允许显存增长。如果设置为 True，分配器不会预先分配一定量 GPU 显存，而是先分配一小块，必要时增加显存分配
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(
session=self.sess) # 前者为变量初始化
def __init__(self, field_sizes=None, embed_size=10, filter_sizes=None, layer_acts=None, drop_out=None,
init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype))
init_vars.append(('f1', [embed_size, filter_sizes[0], 1, 2], 'xavier', dtype))
init_vars.append(('f2', [embed_size, filter_sizes[1], 2, 2], 'xavier', dtype))
init_vars.append(('w1', [2 * 3 * embed_size, 1], 'xavier', dtype))
init_vars.append(('b1', [1], 'zero', dtype))
print('init_vars: ', init_vars)
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)], 1)
l = xw
l = tf.transpose(tf.reshape(l, [-1, num_inputs, embed_size, 1]), [0, 2, 1, 3]) # 变为 16 x 10 矩阵
f1 = self.vars['f1']
l = tf.nn.conv2d(l, f1, [1, 1, 1, 1], 'SAME')
l = tf.transpose(
utils.max_pool_4d(
tf.transpose(l, [0, 1, 3, 2]),
int(num_inputs / 2)),
[0, 1, 3, 2])
f2 = self.vars['f2']
l = tf.nn.conv2d(l, f2, [1, 1, 1, 1], 'SAME')
l = tf.transpose(
utils.max_pool_4d(
tf.transpose(l, [0, 1, 3, 2]), 3),
[0, 1, 3, 2])
l = tf.nn.dropout(
utils.activate(
tf.reshape(l, [-1, embed_size * 3 * 2]),
layer_acts[0]),
self.layer_keeps[0])
w1 = self.vars['w1']
b1 = self.vars['b1']
l = tf.matmul(l, w1) + b1
l = tf.squeeze(l)
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
field_size=None,
embed_size=10,
layer_sizes=None,
layer_acts=None,
drop_out=None,
embed_l2=None,
layer_l2=None,
init_path=None,
opt_algo='gd',
learning_rate=1e-3,
random_seed=None):
Model.__init__(self)
init_vars = []
num_inputs = len(field_size)
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_size[i],
embed_size], 'xavier', dtype))
num_pairs = int(num_inputs * (num_inputs - 1) / 2)
node_in = num_inputs * embed_size + num_pairs
for i in range(len(layer_sizes)):
init_vars.append(('w%d' % i, [node_in,
layer_sizes[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
node_in = layer_sizes[i]
self.graph = tf.Graph()
with self.graph.as_default():
if (random_seed is not None):
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i]
for i in range(num_inputs)] # [num_inputs, field_size[i], k]
xw = tf.concat([
tf.sparse_tensor_dense_matmul(self.X[i], w0[i])
for i in range(num_inputs)
], 1) # [num_inputs*k]
xw3d = tf.reshape(
xw, [-1, num_inputs, embed_size]) # [batch, num_inputs, k]
row = [] # num_pairs
col = [] # num_pairs
for i in range(num_inputs - 1):
for j in range(i + 1, num_inputs):
row.append(i)
col.append(j)
p = tf.transpose(
tf.gather(
tf.transpose(xw3d, [1, 0, 2]), # [num_inputs, batch, k]
row), # [num_pairs, batch, k]
[1, 0, 2]) # [batch, num_pairs, k]
q = tf.transpose(
tf.gather(
tf.transpose(xw3d, [1, 0, 2]), # [num_inputs, batch, k]
col), # [num_pairs, batch, k]
[1, 0, 2]) # [batch, num_pairs, k]
p = tf.reshape(
p, [-1, num_pairs, embed_size]) # [batch, num_pairs, k]
q = tf.reshape(
q, [-1, num_pairs, embed_size]) # [batch, num_pairs, k]
ip = tf.reshape(tf.reduce_sum(p * q, [-1]), [-1, num_pairs])
l = tf.concat([xw, ip], 1) # [num_inputs*k + num_pairs]
for i in range(len(layer_sizes)):
w = self.vars['w%d' % i]
b = self.vars['b%d' % i]
l = utils.activate(tf.matmul(l, w) + b, layer_acts[i])
l = tf.nn.dropout(l, self.layer_keeps[i])
print('l', l)
l = tf.squeeze(l)
self.y_prob = tf.sigmoid(l)
print('l', l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l,
labels=self.y))
if (layer_l2 is not None):
self.loss += embed_l2 * tf.nn.l2_loss(xw)
for i in range(len(layer_sizes)):
w = self.vars['w%d' % i]
self.loss += layer_l2 * tf.nn.l2_loss(w)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
field_sizes=None,
embed_size=10,
layer_sizes=None,
layer_acts=None,
drop_out=None,
embed_l2=None,
layer_l2=None,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
random_seed=None):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
# for i in range(num_inputs):
# init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype))
node_in = num_inputs * embed_size
print('node_in', node_in)
for i in range(len(layer_sizes)):
init_vars.append(('w%d' % i, [node_in,
layer_sizes[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
node_in = layer_sizes[i]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
##################################################
# todo: restore w,v,b parameters from fm model
feature_size = sum(field_sizes)
init_vars.append(('w', [feature_size, 1], 'fm', dtype))
init_vars.append(('v', [feature_size, embed_size], 'fm', dtype))
init_vars.append(('b', [
1,
], 'fm', dtype))
self.vars = utils.init_var_map(init_vars, init_path)
##################################################
# use fm paraeters to fit original interface
init_w0 = tf.concat([self.vars['w'], self.vars['v']], 1)
lower, upper = 0, field_sizes[0]
for i in range(num_inputs):
if (i != 0):
lower, upper = upper, upper + field_sizes[i]
self.vars['embed_%d' % i] = init_w0[lower:upper]
##################################################
print('init_vars, init_path', init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
print('X[0].shape', self.X[0].shape)
print('w0[0].shape', w0[0].shape)
xw = tf.concat([
tf.sparse_tensor_dense_matmul(self.X[i], w0[i])
for i in range(num_inputs)
], 1)
##################################################
l = xw
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
print('l.shape', 'wi.shape', 'bi.shape', l.shape, wi.shape,
bi.shape)
l = tf.nn.dropout(
utils.activate(tf.matmul(l, wi) + bi, layer_acts[i]),
self.layer_keeps[i])
l = tf.squeeze(l)
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l,
labels=self.y))
if layer_l2 is not None:
self.loss += embed_l2 * tf.nn.l2_loss(xw)
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
field_sizes=None,
embed_size=10,
layer_sizes=None,
layer_acts=None,
drop_out=None,
embed_l2=None,
layer_l2=None,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
random_seed=None,
layer_norm=True):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_sizes[i],
embed_size], 'xavier', dtype))
node_in = num_inputs * embed_size + embed_size * embed_size
for i in range(len(layer_sizes)):
init_vars.append(('w%d' % i, [node_in,
layer_sizes[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
node_in = layer_sizes[i]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
xw = tf.concat([
tf.sparse_tensor_dense_matmul(self.X[i], w0[i])
for i in range(num_inputs)
], 1)
z = tf.reduce_sum(tf.reshape(xw, [-1, num_inputs, embed_size]), 1)
op = tf.reshape(
tf.matmul(tf.reshape(z, [-1, embed_size, 1]),
tf.reshape(z, [-1, 1, embed_size])),
[-1, embed_size * embed_size])
if layer_norm:
# x_mean, x_var = tf.nn.moments(xw, [1], keep_dims=True)
# xw = (xw - x_mean) / tf.sqrt(x_var)
# x_g = tf.Variable(tf.ones([num_inputs * embed_size]), name='x_g')
# x_b = tf.Variable(tf.zeros([num_inputs * embed_size]), name='x_b')
# x_g = tf.Print(x_g, [x_g[:10], x_b])
# xw = xw * x_g + x_b
p_mean, p_var = tf.nn.moments(op, [1], keep_dims=True)
op = (op - p_mean) / tf.sqrt(p_var)
p_g = tf.Variable(tf.ones([embed_size**2]), name='p_g')
p_b = tf.Variable(tf.zeros([embed_size**2]), name='p_b')
# p_g = tf.Print(p_g, [p_g[:10], p_b])
op = op * p_g + p_b
l = tf.concat([xw, op], 1)
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
l = tf.nn.dropout(
utils.activate(tf.matmul(l, wi) + bi, layer_acts[i]),
self.layer_keeps[i])
l = tf.squeeze(l)
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l,
labels=self.y))
if layer_l2 is not None:
self.loss += embed_l2 * tf.nn.l2_loss(tf.concat(w0, 0))
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate)
self.train_op = self.optimizer.minimize(self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
data_dir=None,
summary_dir=None,
eval_dir=None,
batch_size=None,
input_dim=None,
output_dim=1,
layer_sizes=None,
layer_acts=None,
drop_out=None,
layer_l2=None,
kernel_l2=None,
l2_w=0,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
sync=False,
workers=20):
Model.__init__(self)
eprint("------- create graph ---------------")
init_vars = []
num_inputs = len(layer_sizes[0])
factor_order = layer_sizes[1]
for i in range(num_inputs):
layer_input = layer_sizes[0][i]
layer_output = factor_order
init_vars.append(('w0_%d' % i, [layer_input,
layer_output], 'tnormal', dtype))
init_vars.append(('b0_%d' % i, [layer_output], 'zero', dtype))
init_vars.append(('w1', [num_inputs * factor_order,
layer_sizes[2]], 'tnormal', dtype))
init_vars.append(('k1', [num_inputs,
layer_sizes[2]], 'tnormal', dtype))
init_vars.append(('b1', [layer_sizes[2]], 'zero', dtype))
for i in range(2, len(layer_sizes) - 1):
layer_input = layer_sizes[i]
layer_output = layer_sizes[i + 1]
init_vars.append((
'w%d' % i,
[layer_input, layer_output],
'tnormal',
))
init_vars.append(('b%d' % i, [layer_output], 'zero', dtype))
with tf.name_scope('input_%d' % FLAGS.task_index) as scope:
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.B = tf.sparse_placeholder(tf.float32, name='B')
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['w0_%d' % i] for i in range(num_inputs)]
b0 = [self.vars['b0_%d' % i] for i in range(num_inputs)]
xw = [
tf.sparse_tensor_dense_matmul(self.X[i], w0[i])
for i in range(num_inputs)
]
x = tf.concat([xw[i] + b0[i] for i in range(num_inputs)], 1)
l = tf.nn.dropout(utils.activate(x, layer_acts[0]),
self.layer_keeps[0])
w1 = self.vars['w1']
k1 = self.vars['k1']
b1 = self.vars['b1']
p = tf.reduce_sum(
tf.reshape(
tf.matmul(
tf.reshape(
tf.transpose(
tf.reshape(l, [-1, num_inputs, factor_order]),
[0, 2, 1]), [-1, num_inputs]), k1),
[-1, factor_order, layer_sizes[2]]), 1)
l = tf.nn.dropout(
utils.activate(tf.matmul(l, w1) + b1 + p, layer_acts[1]),
self.layer_keeps[1])
for i in range(2, len(layer_sizes) - 1):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
l = tf.nn.dropout(
utils.activate(tf.matmul(l, wi) + bi, layer_acts[i]),
self.layer_keeps[i])
## logits
l = tf.reshape(l, [-1])
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
if layer_l2 is not None:
self.loss += layer_l2[0] * tf.nn.l2_loss(tf.concat(xw, 1))
for i in range(1, len(layer_sizes) - 1):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
if kernel_l2 is not None:
self.loss += kernel_l2 * tf.nn.l2_loss(k1)
self.global_step = _variable_on_cpu(
'global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
if sync:
self.optimizer = utils.get_sync_optimizer(opt_algo, learning_rate,
workers)
else:
self.optimizer = utils.get_optimizer(opt_algo, learning_rate)
self.train_op = self.optimizer.minimize(self.loss,
global_step=self.global_step)
self.summary_op = tf.summary.merge_all()
def __init__(self,
layer_sizes=None,
layer_acts=None,
layer_keeps=None,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
random_seed=None):
init_vars = []
num_inputs = len(layer_sizes[0])
embedding_order = layer_sizes[1]
for i in range(num_inputs):
layer_input = layer_sizes[0][i]
layer_output = embedding_order
init_vars.append(('w0_%d' % i, [layer_input,
layer_output], 'tnormal', dtype))
init_vars.append(('b0_%d' % i, [layer_output], 'zero', dtype))
init_vars.append(('f1', [embedding_order, layer_sizes[2], 1,
2], 'tnormal', dtype))
init_vars.append(('f2', [embedding_order, layer_sizes[3], 2,
2], 'tnormal', dtype))
init_vars.append(('w1', [2 * 3 * embedding_order,
1], 'tnormal', dtype))
init_vars.append(('b1', [1], 'zero', dtype))
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['w0_%d' % i] for i in range(num_inputs)]
b0 = [self.vars['b0_%d' % i] for i in range(num_inputs)]
l = tf.nn.dropout(
utils.activate(
tf.concat(1, [
tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) + b0[i]
for i in range(num_inputs)
]), layer_acts[0]), layer_keeps[0])
l = tf.transpose(
tf.reshape(l, [-1, num_inputs, embedding_order, 1]),
[0, 2, 1, 3])
f1 = self.vars['f1']
l = tf.nn.conv2d(l, f1, [1, 1, 1, 1], 'SAME')
l = tf.transpose(
utils.max_pool_4d(tf.transpose(l, [0, 1, 3, 2]),
num_inputs / 2), [0, 1, 3, 2])
f2 = self.vars['f2']
l = tf.nn.conv2d(l, f2, [1, 1, 1, 1], 'SAME')
l = tf.transpose(
utils.max_pool_4d(tf.transpose(l, [0, 1, 3, 2]), 3),
[0, 1, 3, 2])
l = tf.nn.dropout(
utils.activate(tf.reshape(l, [-1, embedding_order * 3 * 2]),
layer_acts[1]), layer_keeps[1])
w1 = self.vars['w1']
b1 = self.vars['b1']
l = tf.nn.dropout(
utils.activate(tf.matmul(l, w1) + b1, layer_acts[2]),
layer_keeps[2])
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(l, self.y))
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.initialize_all_variables().run(session=self.sess)
def __init__(self,
field_sizes=None,
embed_size=10,
layer_sizes=None,
layer_acts=None,
drop_out=None,
embed_l2=None,
layer_l2=None,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
random_seed=None):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_sizes[i],
embed_size], 'xavier', dtype))
node_in = num_inputs * embed_size
for i in range(len(layer_sizes) - 1):
init_vars.append(('w%d' % i, [node_in,
layer_sizes[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
node_in = layer_sizes[i]
init_vars.append(('w_final', [2 * node_in,
layer_sizes[-1]], 'xavier', dtype))
init_vars.append(('b_final', [layer_sizes[-1]], 'zero', dtype))
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
# 38个field--一个大的系数矩阵（6086维），其中按照每个field的维数进行分别embedding，最后再拼接
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
xw = tf.concat([
tf.sparse_tensor_dense_matmul(self.X[i], w0[i])
for i in range(num_inputs)
], 1)
l = xw
la = []
for i in range(len(layer_sizes) - 2):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
print(l.shape, wi.shape, bi.shape)
l = tf.nn.dropout(
utils.activate(tf.matmul(l, wi) + bi, layer_acts[i]),
self.layer_keeps[i])
la.append(l)
l_final = tf.nn.dropout(
utils.activate(
tf.matmul(l, self.vars['w%d' % (len(layer_sizes) - 2)]) +
self.vars['b%d' % (len(layer_sizes) - 2)],
layer_acts[len(layer_sizes) - 2]),
self.layer_keeps[len(layer_sizes) - 2])
la_new = tf.concat([x for x in la], 0)
H = tf.reshape(la_new,
[-1, len(layer_sizes) - 2, layer_sizes[0]
]) # shape = [batch_size,3,128]
H_T = tf.transpose(H, [0, 2, 1]) # shape=[batch_size,128,3]
S_0 = tf.matmul(H, H_T)
mask = [x for x in range(len(layer_sizes) - 2)]
mask_zero = tf.ones([len(layer_sizes) - 2,
len(layer_sizes) - 2]) - tf.one_hot(
mask,
len(layer_sizes) - 2)
S = tf.multiply(S_0, mask_zero)
print(S.shape)
A = tf.nn.softmax(S, name='attention') # shape = batch_size *3*3
G = tf.reduce_sum(tf.matmul(A, H), 1) # shape = batch_size * 128
print(G.shape)
M = tf.concat([l_final, G], 1)
w_final = self.vars['w_final']
b_final = self.vars['b_final']
l_final = tf.matmul(M, w_final) + b_final
l_final = tf.squeeze(l_final)
self.y_prob = l_final
self.loss = tf.reduce_sum(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l_final,
labels=self.y))
if layer_l2 is not None:
self.loss += embed_l2 * tf.nn.l2_loss(xw)
for i in range(len(layer_sizes) - 1):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
self.saver = tf.train.Saver(max_to_keep=3)
def __init__(self,
data_dir=None,
eval_dir=None,
summary_dir=None,
num_epochs=1,
batch_size=None,
input_dim=None,
output_dim=1,
factor_order=10,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
l2_w=0,
l2_v=0,
sync=False,
workers=20):
Model.__init__(self)
data_file_list = tf.gfile.ListDirectory(data_dir)
data_file_list = [x for x in data_file_list if '.tf' in x]
data_file_list = [os.path.join(data_dir, x) for x in data_file_list]
data_file_list.sort()
eprint("input files:", data_file_list)
input_files = data_file_list
eprint("-------- create graph ----------")
#self.graph = tf.Graph()
#with self.graph.as_default():
with tf.device('/cpu:0'):
self.X = tf.sparse_placeholder(tf.float32, name='X')
self.B = tf.sparse_placeholder(tf.float32, name='B')
self.y = tf.placeholder(tf.float32, shape=[None], name='y')
init_vars = [('linear', [input_dim, output_dim], 'xavier', dtype),
('V', [input_dim, factor_order], 'xavier', dtype),
('bias', [output_dim], 'zero', dtype)]
self.vars = utils.init_var_map(init_vars, None)
w = self.vars['linear']
V = self.vars['V']
b = self.vars['bias']
## linear term
Xw = tf.sparse_tensor_dense_matmul(self.B, w)
## cross term
# X^2
X_square = tf.SparseTensor(self.X.indices, tf.square(self.X.values),
tf.to_int64(tf.shape(self.X)))
# XV, shape: input_dim*k
XV_square = tf.square(tf.sparse_tensor_dense_matmul(self.X, V))
# X^2 * V^2, shape: input_dim*k
X2V2 = tf.sparse_tensor_dense_matmul(X_square, tf.square(V))
## normalize
Xnorm = tf.reshape(1.0 / tf.sparse_reduce_sum(self.X, 1),
[-1, output_dim])
# 1/2 * row_sum(XV_square - X2V2), shape: input_dim*1
p = 0.5 * Xnorm * tf.reshape(tf.reduce_sum(XV_square - X2V2, 1),
[-1, output_dim])
logits = tf.reshape(b + Xw + p, [-1])
self.y_prob = tf.sigmoid(logits)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y)) + \
l2_w * tf.nn.l2_loss(Xw)
self.global_step = _variable_on_cpu(
'global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
if sync:
self.optimizer = utils.get_sync_optimizer(opt_algo, learning_rate,
workers)
else:
self.optimizer = utils.get_optimizer(opt_algo, learning_rate)
self.train_op = self.optimizer.minimize(self.loss,
global_step=self.global_step)
self.summary_op = tf.summary.merge_all()
def __init__(self,
feature_size,
field_size,
embedding_size=8,
optimizer_type='gd',
learning_rate=1e-2,
verbose=False,
random_seed=None,
eval_metric=roc_auc_score,
greater_is_better=True,
epoch=10,
batch_size=1024,
l2_reg=0,
deep_layers=[32, 32],
batch_norm=True,
dropout_deep=[],
cross_layer_num=3):
Model.__init__(self, eval_metric, greater_is_better, epoch, batch_size,
verbose, batch_norm, dropout_deep)
init_vars = [('weight', [feature_size, 1], 'uniform', tf.float32),
('bias', [1], 'uniform', tf.float32),
('feature_embed', [feature_size,
embedding_size], 'normal', tf.float32)]
node_in = embedding_size * field_size
for i in range(len(deep_layers)):
init_vars.extend([('layer_%d' % i, [node_in, deep_layers[i]],
'glorot_normal', tf.float32)])
init_vars.extend([('bias_%d' % i, [1, deep_layers[i]],
'glorot_normal', tf.float32)])
node_in = deep_layers[i]
for i in range(cross_layer_num):
init_vars.extend([('cross_layer_%d' % i,
[1, embedding_size * field_size
], 'glorot_normal', tf.float32)])
init_vars.extend([('cross_bias_%d' % i,
[1, embedding_size * field_size
], 'glorot_normal', tf.float32)])
node_in = embedding_size * field_size + deep_layers[-1]
init_vars.extend([('concat_projection', [node_in, 1], 'glorot_normal',
tf.float32)])
init_vars.extend([('concat_bias', [1,
1], 'glorot_normal', tf.float32)])
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.feat_index = tf.placeholder(tf.int32,
shape=[None, None],
name="feat_index") # None * F
self.feat_value = tf.placeholder(tf.float32,
shape=[None, None],
name="feat_value") # None * F
self.label = tf.placeholder(tf.float32,
shape=[None, 1],
name="label") # None * 1
self.dropout_keep_deep = tf.placeholder(tf.float32,
shape=[None],
name="dropout_keep_deep")
self.train_phase = tf.placeholder(tf.bool, name="train_phase")
self.vars = utils.init_var_map(init_vars)
self.embeddings = tf.nn.embedding_lookup(
self.vars["feature_embed"], self.feat_index) # None * F * K
feat_value = tf.reshape(self.feat_value, shape=[-1, field_size, 1])
self.embeddings = tf.reshape(
tf.multiply(self.embeddings, feat_value),
shape=[-1, embedding_size * field_size])
# ---------- cross layer ----------
self.deep_cross_input = tf.nn.dropout(self.embeddings,
self.dropout_keep_deep[0])
self.cross_layer_out = self.deep_cross_input
for i in range(1, cross_layer_num):
self.x0xiT = self.deep_cross_input * self.cross_layer_out
self.x0xiT = tf.reduce_sum(self.x0xiT, 1,
keep_dims=True) # None * 1
self.cross_layer_out = tf.add(tf.matmul(self.x0xiT, self.vars['cross_layer_%d' % i]) \
, self.vars['cross_bias_%d' % i]) + self.cross_layer_out
# ---------- deep component --------
self.y_deep = self.deep_cross_input
for i in range(len(deep_layers)):
self.y_deep = tf.add(
tf.matmul(self.y_deep, self.vars['layer_%s' % i]),
self.vars['bias_%s' % i])
if self.batch_norm:
self.y_deep = self.batch_norm_layer(
self.y_deep,
train_phase=self.train_phase,
scope_bn="bn_%s" % i)
self.y_deep = tf.nn.dropout(
utils.activate(self.y_deep, 'relu'),
self.dropout_keep_deep[i + 1])
concat_projection = self.vars['concat_projection']
concat_bias = self.vars['concat_bias']
self.out = tf.concat([self.y_deep, self.cross_layer_out], 1)
self.out = tf.add(tf.matmul(self.out, concat_projection),
concat_bias)
self.y_prob = tf.sigmoid(self.out)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=self.label, logits=self.out)) + \
tf.contrib.layers.l2_regularizer(
l2_reg)(self.vars["concat_projection"])
for i in range(len(deep_layers)):
self.loss += tf.contrib.layers.l2_regularizer(l2_reg)(
self.vars["layer_%d" % i])
self.optimizer = utils.get_optimizer(optimizer_type, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.sess.run(tf.global_variables_initializer())
def __init__(self,
field_sizes=None,
embed_size=10,
layer_size=None,
layer_acts=None,
drop_out=None,
embed_l2=None,
layer_l2=None,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
random_seed=None,
layer_norm=True):
Model.__init__(self)
init_vars = []
num_input = len(field_sizes)
for i in range(num_input):
init_vars.append(('embed_%d' % i, [field_sizes[i],
embed_size], 'xavier', dtype))
num_pairs = int(num_input * (num_input - 1) / 2) # field对的个数
node_in = num_input * embed_size + num_pairs # 此处为设计的关键
init_vars.append(('kernel', [embed_size, num_pairs,
embed_size], 'xavier', dtype))
for i in range(len(layer_size)):
init_vars.append(('w%d' % i, [node_in,
layer_size[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_size[i]], 'zero', dtype))
node_in = layer_size[i]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_input)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w_0 = [self.vars['embed_%d' % i] for i in range(num_input)]
# batch * (embed_size*num_field)
xw = tf.concat([
tf.sparse_tensor_dense_matmul(self.X[i], w_0[i])
for i in range(num_input)
], 1)
xw3d = tf.reshape(xw, [-1, num_input, embed_size])
row = []
col = []
# 构造pair
for i in range(num_input - 1):
for j in range(i + 1, num_input):
row.append(i)
col.append(j)
# batch * pair * embed_size
p = tf.transpose(
# pair * batch * embed_size
tf.gather(
# num_field * batch * embed_size
tf.transpose(xw3d, [1, 0, 2]),
row),
[1, 0, 2])
q = tf.transpose(
# pair * batch * embed_size
tf.gather(
# num_field * batch * embed_size
tf.transpose(xw3d, [1, 0, 2]),
col),
[1, 0, 2])
p = tf.reshape(p, [-1, num_pairs, embed_size])
q = tf.reshape(q, [-1, num_pairs, embed_size])
# embed_size*num_pairs*embed_size
k = self.vars['kernel']
p = tf.expand_dims(p, 1) # 增加维度 batch * 1 * pair * embed_size
# batch * num_pairs
# temp = tf.multiply(p,k)
# temp = tf.reduce_sum(temp,-1)
# temp = tf.transpose(temp,[0,2,1])
# temp = tf.multiply(temp,q)
# temp = tf.reduce_sum(temp,-1)
kp = tf.reduce_sum(
# batch * num_pairs * embed_size
tf.multiply(
# 置换位置 batch * num_pairs * embed_size
tf.transpose(
# 按最后一个维度求和 batch * embed_size * num_pairs
tf.reduce_sum(
# 点乘 batch * embed_size*num_pairs*embed_size
tf.multiply(p, k),
-1),
[0, 2, 1]),
q),
-1)
l = tf.concat([xw, kp], 1)
for i in range(len(layer_size)):
w_i = self.vars['w%d' % i]
b_i = self.vars['b%d' % i]
l = tf.nn.dropout(
utils.activate(tf.matmul(l, w_i) + b_i, layer_acts[i]),
self.layer_keeps[i])
l = tf.squeeze(l)
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l,
labels=self.y))
if layer_l2 is not None:
self.loss += embed_l2 * tf.nn.l2_loss(xw)
for i in range(len(layer_size)):
w_i = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(w_i)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
layer_sizes=None,
layer_acts=None,
drop_out=None,
layer_l2=None,
kernel_l2=None,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
random_seed=None):
Model.__init__(self)
init_vars = []
num_inputs = len(layer_sizes[0])
factor_order = layer_sizes[1]
for i in range(num_inputs):
layer_input = layer_sizes[0][i]
layer_output = factor_order
init_vars.append(('w0_%d' % i, [layer_input,
layer_output], 'tnormal', dtype))
init_vars.append(('b0_%d' % i, [layer_output], 'zero', dtype))
init_vars.append(('w1', [num_inputs * factor_order,
layer_sizes[2]], 'tnormal', dtype))
init_vars.append(('k1', [factor_order * factor_order,
layer_sizes[2]], 'tnormal', dtype))
init_vars.append(('b1', [layer_sizes[2]], 'zero', dtype))
for i in range(2, len(layer_sizes) - 1):
layer_input = layer_sizes[i]
layer_output = layer_sizes[i + 1]
init_vars.append((
'w%d' % i,
[layer_input, layer_output],
'tnormal',
))
init_vars.append(('b%d' % i, [layer_output], 'zero', dtype))
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['w0_%d' % i] for i in range(num_inputs)]
b0 = [self.vars['b0_%d' % i] for i in range(num_inputs)]
xw = [
tf.sparse_tensor_dense_matmul(self.X[i], w0[i])
for i in range(num_inputs)
]
x = tf.concat([xw[i] + b0[i] for i in range(num_inputs)], 1)
l = tf.nn.dropout(utils.activate(x, layer_acts[0]),
self.layer_keeps[0])
w1 = self.vars['w1']
k1 = self.vars['k1']
b1 = self.vars['b1']
z = tf.reduce_sum(tf.reshape(l, [-1, num_inputs, factor_order]), 1)
p = tf.reshape(
tf.matmul(tf.reshape(z, [-1, factor_order, 1]),
tf.reshape(z, [-1, 1, factor_order])),
[-1, factor_order * factor_order])
l = tf.nn.dropout(
utils.activate(
tf.matmul(l, w1) + tf.matmul(p, k1) + b1, layer_acts[1]),
self.layer_keeps[1])
for i in range(2, len(layer_sizes) - 1):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
l = tf.nn.dropout(
utils.activate(tf.matmul(l, wi) + bi, layer_acts[i]),
self.layer_keeps[i])
l = tf.reshape(l, [-1])
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l,
labels=self.y))
if layer_l2 is not None:
# for i in range(num_inputs):
self.loss += layer_l2[0] * tf.nn.l2_loss(tf.concat(xw, 1))
for i in range(1, len(layer_sizes) - 1):
wi = self.vars['w%d' % i]
# bi = self.vars['b%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
if kernel_l2 is not None:
self.loss += kernel_l2 * tf.nn.l2_loss(k1)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self, field_sizes=None, embed_size=10, layer_sizes=None, layer_acts=None, drop_out=None,
embed_l2=None, layer_l2=None, init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None,
layer_norm=True):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype))
num_pairs = int(num_inputs * (num_inputs - 1) / 2)
node_in = num_inputs * embed_size + num_pairs
init_vars.append(('kernel', [embed_size, num_pairs, embed_size], 'xavier', dtype))
for i in range(len(layer_sizes)):
init_vars.append(('w%d' % i, [node_in, layer_sizes[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
node_in = layer_sizes[i]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)], 1)
xw3d = tf.reshape(xw, [-1, num_inputs, embed_size])
row = []
col = []
for i in range(num_inputs - 1):
for j in range(i + 1, num_inputs):
row.append(i)
col.append(j)
# batch * pair * k
p = tf.transpose(
# pair * batch * k
tf.gather(
# num * batch * k
tf.transpose(
xw3d, [1, 0, 2]),
row),
[1, 0, 2])
# batch * pair * k
q = tf.transpose(
tf.gather(
tf.transpose(
xw3d, [1, 0, 2]),
col),
[1, 0, 2])
# b * p * k
p = tf.reshape(p, [-1, num_pairs, embed_size])
# b * p * k
q = tf.reshape(q, [-1, num_pairs, embed_size])
# k * p * k
k = self.vars['kernel']
# batch * 1 * pair * k
p = tf.expand_dims(p, 1)
# batch * pair
kp = tf.reduce_sum(
# batch * pair * k
tf.multiply(
# batch * pair * k
tf.transpose(
# batch * k * pair
tf.reduce_sum(
# batch * k * pair * k
tf.multiply(
p, k),
-1),
[0, 2, 1]),
q),
-1)
#
# if layer_norm:
# # x_mean, x_var = tf.nn.moments(xw, [1], keep_dims=True)
# # xw = (xw - x_mean) / tf.sqrt(x_var)
# # x_g = tf.Variable(tf.ones([num_inputs * embed_size]), name='x_g')
# # x_b = tf.Variable(tf.zeros([num_inputs * embed_size]), name='x_b')
# # x_g = tf.Print(x_g, [x_g[:10], x_b])
# # xw = xw * x_g + x_b
# p_mean, p_var = tf.nn.moments(op, [1], keep_dims=True)
# op = (op - p_mean) / tf.sqrt(p_var)
# p_g = tf.Variable(tf.ones([embed_size**2]), name='p_g')
# p_b = tf.Variable(tf.zeros([embed_size**2]), name='p_b')
# # p_g = tf.Print(p_g, [p_g[:10], p_b])
# op = op * p_g + p_b
l = tf.concat([xw, kp], 1)
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
l = tf.nn.dropout(
activate(
tf.matmul(l, wi) + bi,
layer_acts[i]),
self.layer_keeps[i])
l = tf.squeeze(l)
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
if layer_l2 is not None:
self.loss += embed_l2 * tf.nn.l2_loss(xw)#tf.concat(w0, 0))
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.optimizer = get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
layer_sizes=None,
layer_acts=None,
layer_keeps=None,
layer_l2=None,
kernel_l2=None,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
random_seed=None):
"""
# Arguments:
layer_size: [num_fields, factor_layer, l_p size]
layer_acts: ["tanh", "none"]
layer_keep: [1, 1]
layer_l2: [0, 0]
kernel_l2: 0
"""
init_vars = []
num_inputs = len(layer_sizes[0])
factor_order = layer_sizes[1]
for i in range(num_inputs):
layer_input = layer_sizes[0][i]
layer_output = factor_order
# w0 store the embeddings for all features.
init_vars.append(('w0_%d' % i, [layer_input,
layer_output], 'tnormal', dtype))
init_vars.append(('b0_%d' % i, [layer_output], 'zero', dtype))
init_vars.append(('w_l', [num_inputs * factor_order,
layer_sizes[2]], 'tnormal', dtype))
init_vars.append(('w_p', [num_inputs * num_inputs,
layer_sizes[2]], 'tnormal', dtype))
#init_vars.append(('w1', [num_inputs * factor_order + num_inputs * num_inputs, layer_sizes[2]], 'tnormal', dtype))
init_vars.append(('b1', [layer_sizes[2]], 'zero', dtype))
for i in range(2, len(layer_sizes) - 1):
layer_input = layer_sizes[i]
layer_output = layer_sizes[i + 1]
init_vars.append(('w%d' % i, [layer_input,
layer_output], 'tnormal', dtype))
init_vars.append(('b%d' % i, [layer_output], 'zero', dtype))
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['w0_%d' % i] for i in range(num_inputs)]
b0 = [self.vars['b0_%d' % i] for i in range(num_inputs)]
# Multiply SparseTensor X[i] by dense matrix w0[i]
xw = [
tf.sparse_tensor_dense_matmul(self.X[i], w0[i])
for i in range(num_inputs)
]
x = tf.concat([xw[i] + b0[i] for i in range(num_inputs)], 1)
l = tf.nn.dropout(utils.activate(x, layer_acts[0]), layer_keeps[0])
w_l = self.vars['w_l']
w_p = self.vars['w_p']
b1 = self.vars['b1']
# This is where W_p \cdot p happens.
# k1 is \theta, which is the weight for each field(feature) vector
p = tf.matmul(
tf.reshape(l, [-1, num_inputs, factor_order]),
tf.transpose(tf.reshape(l, [-1, num_inputs, factor_order]),
[0, 2, 1]))
p = tf.nn.dropout(
utils.activate(
tf.matmul(tf.reshape(p, [-1, num_inputs * num_inputs]),
w_p), 'none'), 1.0)
l = tf.nn.dropout(
utils.activate(tf.matmul(l, w_l) + b1 + p, layer_acts[1]),
layer_keeps[1])
for i in range(2, len(layer_sizes) - 1):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
l = tf.nn.dropout(
utils.activate(tf.matmul(l, wi) + bi, layer_acts[i]),
layer_keeps[i])
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l,
labels=self.y))
if layer_l2 is not None:
# for i in range(num_inputs):
self.loss += layer_l2[0] * tf.nn.l2_loss(tf.concat(xw, 1))
for i in range(1, len(layer_sizes) - 1):
if i == 1:
self.loss += layer_l2[i] * tf.nn.l2_loss(w_l)
self.loss += layer_l2[i] * tf.nn.l2_loss(w_p)
else:
wi = self.vars['w%d' % i]
# bi = self.vars['b%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
if kernel_l2 is not None:
pass
self.optimizer = utils.get_optimizer(opt_algo, learning_rate,
self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self, field_sizes=None, embed_size=10, layer_sizes=None, layer_acts=None, drop_out=None,
embed_l2=None, layer_l2=None, init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype))
num_pairs = int(num_inputs * (num_inputs - 1) / 2)
node_in = num_inputs * embed_size + num_pairs
# node_in = num_inputs * (embed_size + num_inputs)
for i in range(len(layer_sizes)):
init_vars.append(('w%d' % i, [node_in, layer_sizes[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
node_in = layer_sizes[i]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)], 1)
xw3d = tf.reshape(xw, [-1, num_inputs, embed_size])
row = []
col = []
for i in range(num_inputs-1):
for j in range(i+1, num_inputs):
row.append(i)
col.append(j)
# batch * pair * k
p = tf.transpose(
# pair * batch * k
tf.gather(
# num * batch * k
tf.transpose(
xw3d, [1, 0, 2]),
row),
[1, 0, 2])
# batch * pair * k
q = tf.transpose(
tf.gather(
tf.transpose(
xw3d, [1, 0, 2]),
col),
[1, 0, 2])
p = tf.reshape(p, [-1, num_pairs, embed_size])
q = tf.reshape(q, [-1, num_pairs, embed_size])
ip = tf.reshape(tf.reduce_sum(p * q, [-1]), [-1, num_pairs])
# simple but redundant
# batch * n * 1 * k, batch * 1 * n * k
# ip = tf.reshape(
# tf.reduce_sum(
# tf.expand_dims(xw3d, 2) *
# tf.expand_dims(xw3d, 1),
# 3),
# [-1, num_inputs**2])
l = tf.concat([xw, ip], 1)
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
l = tf.nn.dropout(
utils.activate(
tf.matmul(l, wi) + bi,
layer_acts[i]),
self.layer_keeps[i])
l = tf.squeeze(l)
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
if layer_l2 is not None:
self.loss += embed_l2 * tf.nn.l2_loss(xw)
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
data_dir=None,
summary_dir=None,
eval_dir=None,
batch_size=None,
input_dim=None,
output_dim=1,
factor_order=10,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
l2_w=0,
sync=False,
workers=20):
Model.__init__(self)
eprint("-------- create graph ----------")
with tf.name_scope('input_%d' % FLAGS.task_index) as scope:
self.X = tf.sparse_placeholder(tf.float32, name='X')
self.B = tf.sparse_placeholder(tf.float32, name='B')
self.y = tf.placeholder(tf.float32, shape=[None], name='y')
init_vars = [('linear', [input_dim, output_dim], 'xavier', dtype),
('U', [input_dim, factor_order], 'xavier', dtype),
('V', [input_dim, factor_order], 'xavier', dtype),
('bias', [output_dim], 'zero', dtype)]
self.vars = utils.init_var_map(init_vars, None)
w = self.vars['linear']
U = self.vars['U']
V = self.vars['V']
b = self.vars['bias']
## normalize
Xnorm = tf.reshape(1.0 / tf.sparse_reduce_sum(self.X, 1),
[-1, output_dim])
## linear term
Xw = tf.sparse_tensor_dense_matmul(self.B, w, name="Xw")
## cross term
XU = tf.sparse_tensor_dense_matmul(self.X, U, name="XU")
XV = tf.sparse_tensor_dense_matmul(self.X, V, name="XV")
X_square = tf.SparseTensor(self.X.indices, tf.square(self.X.values),
tf.to_int64(tf.shape(self.X)))
p = 0.5 * Xnorm * tf.reshape(
tf.reduce_sum(
XU * XV - tf.sparse_tensor_dense_matmul(X_square, U * V), 1),
[-1, output_dim])
logits = tf.reshape(b + Xw + p, [-1])
self.y_prob = tf.sigmoid(logits)
#
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y)) + \
l2_w * tf.nn.l2_loss(Xw)
self.global_step = _variable_on_cpu(
'global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
if sync:
self.optimizer = utils.get_sync_optimizer(opt_algo, learning_rate,
workers)
else:
self.optimizer = utils.get_optimizer(opt_algo, learning_rate)
self.train_op = self.optimizer.minimize(self.loss,
global_step=self.global_step)
self.summary_op = tf.summary.merge_all()
def __init__(self, field_sizes=None, embed_size=10, layer_sizes=None, layer_acts=None, drop_out=None,
embed_l2=None, layer_l2=None, init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None,
layer_norm=True):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype))
node_in = num_inputs * embed_size + embed_size * embed_size
for i in range(len(layer_sizes)):
init_vars.append(('w%d' % i, [node_in, layer_sizes[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
node_in = layer_sizes[i]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)], 1)
z = tf.reduce_sum(tf.reshape(xw, [-1, num_inputs, embed_size]), 1)
op = tf.reshape(
tf.matmul(tf.reshape(z, [-1, embed_size, 1]),
tf.reshape(z, [-1, 1, embed_size])),
[-1, embed_size * embed_size])
if layer_norm:
# x_mean, x_var = tf.nn.moments(xw, [1], keep_dims=True)
# xw = (xw - x_mean) / tf.sqrt(x_var)
# x_g = tf.Variable(tf.ones([num_inputs * embed_size]), name='x_g')
# x_b = tf.Variable(tf.zeros([num_inputs * embed_size]), name='x_b')
# x_g = tf.Print(x_g, [x_g[:10], x_b])
# xw = xw * x_g + x_b
p_mean, p_var = tf.nn.moments(op, [1], keep_dims=True)
op = (op - p_mean) / tf.sqrt(p_var)
p_g = tf.Variable(tf.ones([embed_size**2]), name='p_g')
p_b = tf.Variable(tf.zeros([embed_size**2]), name='p_b')
# p_g = tf.Print(p_g, [p_g[:10], p_b])
op = op * p_g + p_b
l = tf.concat([xw, op], 1)
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
l = tf.nn.dropout(
utils.activate(
tf.matmul(l, wi) + bi,
layer_acts[i]),
self.layer_keeps[i])
l = tf.squeeze(l)
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
if layer_l2 is not None:
self.loss += embed_l2 * tf.nn.l2_loss(tf.concat(w0, 0))
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self,
data_dir=None,
summary_dir=None,
eval_dir=None,
batch_size=None,
input_dim=None,
output_dim=1,
layer_sizes=None,
layer_acts=None,
drop_out=None,
init_path=None,
opt_algo='gd',
learning_rate=1e-2,
l2_w=0,
layer_l2=None,
sync=False,
workers=20):
Model.__init__(self)
eprint("-------- create graph ----------")
init_vars = []
# linear part
init_vars.append(('linear', [input_dim, output_dim], 'xavier', dtype))
init_vars.append(('bias', [output_dim], 'zero', dtype))
num_inputs = len(layer_sizes[0])
factor_order = layer_sizes[1]
for i in range(num_inputs):
layer_input = layer_sizes[0][i]
layer_output = factor_order
# field_sizes[i] stores the i-th field feature number
init_vars.append(('w0_%d' % i, [layer_input,
layer_output], 'xavier', dtype))
init_vars.append(('b0_%d' % i, [layer_output], 'zero', dtype))
# full connection
node_in = num_inputs * factor_order
init_vars.append(('w1', [node_in, layer_sizes[2]], 'xavier', dtype))
init_vars.append(('b1', [layer_sizes[2]], 'zero', dtype))
for i in range(2, len(layer_sizes) - 1):
layer_input = layer_sizes[i]
layer_output = layer_sizes[i + 1]
init_vars.append(('w%d' % i, [layer_input,
layer_output], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_output], 'zero', dtype))
#self.graph = tf.Graph()
#with self.graph.as_default():
#with tf.device('/cpu:0'):
with tf.name_scope('input_%d' % FLAGS.task_index) as scope:
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.B = tf.sparse_placeholder(tf.float32, name='B')
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
w0 = [self.vars['w0_%d' % i] for i in range(num_inputs)]
b0 = [self.vars['b0_%d' % i] for i in range(num_inputs)]
xw = [
tf.sparse_tensor_dense_matmul(self.X[i], w0[i])
for i in range(num_inputs)
]
x = tf.concat([xw[i] + b0[i] for i in range(num_inputs)], 1)
## normalize
fmX = tf.sparse_add(self.X[0], self.X[1])
for i in range(2, num_inputs):
fmX = tf.sparse_add(fmX, self.X[i])
Xnorm = tf.reshape(1.0 / tf.sparse_reduce_sum(fmX, 1),
[-1, output_dim])
l = tf.nn.dropout(utils.activate(x, layer_acts[0]),
self.layer_keeps[0])
for i in range(1, len(layer_sizes) - 1):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
eprint(l.get_shape(), wi.get_shape(), bi.get_shape())
l = tf.nn.dropout(
utils.activate(tf.matmul(l, wi) + bi, layer_acts[i]),
self.layer_keeps[i])
## FM linear part
fmb = self.vars['bias']
fmw = self.vars['linear']
Xw = tf.sparse_tensor_dense_matmul(self.B, fmw)
## cross term
# XV, shape: input_dim*k
fmXV = tf.add_n(xw)
XV_square = tf.square(fmXV)
eprint(XV_square.get_shape())
# X^2 * V^2, shape: input_dim*k
fmX2 = [
tf.SparseTensor(self.X[i].indices, tf.square(self.X[i].values),
tf.to_int64(tf.shape(self.X[i])))
for i in range(num_inputs)
]
fmV2 = [tf.square(w0[i]) for i in range(num_inputs)]
fmX2V2 = [
tf.sparse_tensor_dense_matmul(fmX2[i], fmV2[i])
for i in range(num_inputs)
]
X2V2 = tf.add_n(fmX2V2)
eprint(X2V2.get_shape())
# 1/2 * row_sum(XV_square - X2V2), shape: input_dim*1
p = 0.5 * Xnorm * tf.reshape(tf.reduce_sum(XV_square - X2V2, 1),
[-1, output_dim])
## logits
logits = tf.reshape(l + Xw + fmb + p, [-1])
## predict
self.y_prob = tf.sigmoid(logits)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y)) + \
l2_w * tf.nn.l2_loss(Xw)
if layer_l2 is not None:
self.loss += layer_l2[0] * tf.nn.l2_loss(tf.concat(xw, 1))
for i in range(1, len(layer_sizes) - 1):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.global_step = _variable_on_cpu(
'global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
if sync:
self.optimizer = utils.get_sync_optimizer(opt_algo, learning_rate,
workers)
else:
self.optimizer = utils.get_optimizer(opt_algo, learning_rate)
self.train_op = self.optimizer.minimize(self.loss,
global_step=self.global_step)
self.summary_op = tf.summary.merge_all()
def __init__(self, field_sizes=None, embed_size=10, layer_sizes=None, layer_acts=None, drop_out=None,
embed_l2=None, layer_l2=None, init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None):
Model.__init__(self)
init_vars = []
num_inputs = len(field_sizes)
for i in range(num_inputs):
init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype))
num_pairs = int(num_inputs * (num_inputs - 1) / 2)
node_in = num_inputs * embed_size + num_pairs
# node_in = num_inputs * (embed_size + num_inputs)
for i in range(len(layer_sizes)):
init_vars.append(('w%d' % i, [node_in, layer_sizes[i]], 'xavier', dtype))
init_vars.append(('b%d' % i, [layer_sizes[i]], 'zero', dtype))
node_in = layer_sizes[i]
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
tf.set_random_seed(random_seed)
self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)]
self.y = tf.placeholder(dtype)
self.keep_prob_train = 1 - np.array(drop_out)
self.keep_prob_test = np.ones_like(drop_out)
self.layer_keeps = tf.placeholder(dtype)
self.vars = init_var_map(init_vars, init_path)
w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)]
xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)], 1)
xw3d = tf.reshape(xw, [-1, num_inputs, embed_size])
row = []
col = []
for i in range(num_inputs-1):
for j in range(i+1, num_inputs):
row.append(i)
col.append(j)
# batch * pair * k
p = tf.transpose(
# pair * batch * k
tf.gather(
# num * batch * k
tf.transpose(
xw3d, [1, 0, 2]),
row),
[1, 0, 2])
# batch * pair * k
q = tf.transpose(
tf.gather(
tf.transpose(
xw3d, [1, 0, 2]),
col),
[1, 0, 2])
p = tf.reshape(p, [-1, num_pairs, embed_size])
q = tf.reshape(q, [-1, num_pairs, embed_size])
ip = tf.reshape(tf.reduce_sum(p * q, [-1]), [-1, num_pairs])
# simple but redundant
# batch * n * 1 * k, batch * 1 * n * k
# ip = tf.reshape(
# tf.reduce_sum(
# tf.expand_dims(xw3d, 2) *
# tf.expand_dims(xw3d, 1),
# 3),
# [-1, num_inputs**2])
l = tf.concat([xw, ip], 1)
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
bi = self.vars['b%d' % i]
l = tf.nn.dropout(
activate(
tf.matmul(l, wi) + bi,
layer_acts[i]),
self.layer_keeps[i])
l = tf.squeeze(l)
self.y_prob = tf.sigmoid(l)
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y))
if layer_l2 is not None:
self.loss += embed_l2 * tf.nn.l2_loss(xw)
for i in range(len(layer_sizes)):
wi = self.vars['w%d' % i]
self.loss += layer_l2[i] * tf.nn.l2_loss(wi)
self.optimizer = get_optimizer(opt_algo, learning_rate, self.loss)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=self.sess)
def __init__(self, feature2field , layer_sizes, layer_acts, drop_out, learning_rate=1e-2, l2_reg = 0,
feature_size = None, field_size = None, k = 40, opt_algo = 'adam', train = True):
# feature_size : feature number # field_size : field number # k : latent vector dimension
Model.__init__(self)
layers = [int(e) for e in layer_sizes.split(',')]
dropout = [float(e) for e in drop_out.split(',')]
layer_active_func = [e for e in layer_acts.split(',')]
self.X = tf.placeholder(dtype=dtype, shape=[None, feature_size], name='input')
self.y = tf.placeholder(dtype=dtype, shape=[None, ], name='label')
self.keep_prob = tf.placeholder(dtype=dtype)
with tf.variable_scope('linear_layer'): # w .* x + b
self.b = tf.get_variable(name='bias', initializer=tf.constant(0.5), dtype=dtype) # tf.zeros_initializer()
self.w1 = tf.get_variable(name='w1', shape=[feature_size], initializer=tf.truncated_normal_initializer(mean=0, stddev=1e-2), dtype=dtype)
self.linear_terms = tf.reduce_sum(tf.multiply(self.w1, self.X), 1) + self.b
with tf.variable_scope('field_aware_interaction_layer'): # sum(<vi_fj, vj_fi> * x_i * x_j)
self.nfk = tf.get_variable('nfk', shape=[feature_size, field_size, k], dtype=dtype, initializer=tf.truncated_normal_initializer(mean=0, stddev=0.01))
self.field_aware_interaction_terms = tf.constant(0, dtype=dtype)
for i in range(feature_size):
for j in range(i + 1, feature_size):
self.field_aware_interaction_terms += tf.multiply(
tf.reduce_sum(tf.multiply(self.nfk[i, feature2field[j]], self.nfk[j, feature2field[i]])),
tf.multiply(self.X[:, i], self.X[:, j])
)
init_deep_layer_vars = []
input_dim = feature_size
for i in range(len(layers)):
output_dim = layers[i]
init_deep_layer_vars.append(('deepW_%d' % i, [input_dim, output_dim], 'xavier', dtype))
init_deep_layer_vars.append(('deepB_%d' % i, [output_dim], 'zero', dtype))
input_dim = layers[i]
init_deep_layer_vars.append(('outW', [layers[-1], 1], 'xavier', dtype))
init_deep_layer_vars.append(('outB', [1], 'zero', dtype))
self.deepVars = init_var_map(init_deep_layer_vars)
with tf.variable_scope("Deep-part"):
hidden = self.X
for i in range(len(layers)):
if train:
hidden = tf.nn.dropout( # h_i = W_i * x + b_i
activate(tf.matmul(hidden, self.deepVars['deepW_%d' % i]) + self.deepVars['deepB_%d' % i], layer_active_func[i]),
dropout[i])
else:
hidden = activate(tf.matmul(hidden, self.deepVars['deepW_%d' % i]) + self.deepVars['deepB_%d' % i], layer_active_func[i])
#self.a=hidden;self.aa=self.deepVars['outW'];self.aaa=tf.matmul(hidden, self.deepVars['outW']);self.aaaa=self.deepVars['outB']
self.deepOut = tf.matmul(hidden, self.deepVars['outW']) + self.deepVars['outB']
self.deepOut = tf.reshape(self.deepOut, shape=[-1]) # tf.reshape() 防止 python 自带的广播机制算出奇怪的值
with tf.variable_scope("DeepFM-out"):
self.out_sum = self.linear_terms + self.field_aware_interaction_terms + self.deepOut
self.pred_prob = tf.sigmoid(self.out_sum)
# ------bulid loss------
self.loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.out_sum, labels=self.y)) + \
l2_reg * tf.nn.l2_loss(self.w1) + \
l2_reg * tf.nn.l2_loss(self.nfk)
# ------bulid optimizer------
self.optimizer = get_optimizer(opt_algo, learning_rate, self.loss)
# 保存模型的参数
self.saver = tf.train.Saver(tf.global_variables())
# GPU设定
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
