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, 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,
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,
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,
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)