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']
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], 'tnormal', dtype),
('b', [output_dim], 'zero', dtype)]
# TFはグラフ初期化
self.graph = tf.Graph()
with self.graph.as_default():
if random_seed is not None:
# set_random_seed -> 初期化图级和操作级的参数
# -> グラフレベルとオペレーションレベルのパラメータを初期化する
# https://www.tensorflow.org/api_docs/python/tf/set_random_seed
tf.set_random_seed(random_seed)
# Inserts a placeholder for a sparse tensor that will be always fed.
# https://www.tensorflow.org/api_docs/python/tf/sparse_placeholder
self.X = tf.sparse_placeholder(dtype)
# Inserts a placeholder for a tensor that will be always fed.
# https://www.tensorflow.org/api_docs/python/tf/placeholder
self.y = tf.placeholder(dtype)
self.vars = utils.init_var_map(init_vars, init_path)
#
w = self.vars['w']
b = self.vars['b']
# https://www.tensorflow.org/api_docs/python/tf/sparse_tensor_dense_matmul
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, 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, layer_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(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.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)]
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]),
self.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]),
self.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]),
self.layer_keeps[2])
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))
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)