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net.py
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net.py
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import paddle.fluid as fluid
def all_vocab_network(vocab_size,
hid_size=100,
init_low_bound=-0.04,
init_high_bound=0.04):
""" network definition """
emb_lr_x = 10.0
gru_lr_x = 1.0
fc_lr_x = 1.0
# Input data
src_wordseq = fluid.layers.data(
name="src_wordseq", shape=[1], dtype="int64", lod_level=1)
dst_wordseq = fluid.layers.data(
name="dst_wordseq", shape=[1], dtype="int64", lod_level=1)
emb = fluid.layers.embedding(
input=src_wordseq,
size=[vocab_size, hid_size],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound),
learning_rate=emb_lr_x),
is_sparse=True)
fc0 = fluid.layers.fc(input=emb,
size=hid_size * 3,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound),
learning_rate=gru_lr_x))
gru_h0 = fluid.layers.dynamic_gru(
input=fc0,
size=hid_size,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound),
learning_rate=gru_lr_x))
fc = fluid.layers.fc(input=gru_h0,
size=vocab_size,
act='softmax',
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound),
learning_rate=fc_lr_x))
cost = fluid.layers.cross_entropy(input=fc, label=dst_wordseq)
acc = fluid.layers.accuracy(input=fc, label=dst_wordseq, k=20)
avg_cost = fluid.layers.mean(x=cost)
return src_wordseq, dst_wordseq, avg_cost, acc
def train_bpr_network(vocab_size, neg_size, hid_size, drop_out=0.2):
""" network definition """
emb_lr_x = 1.0
gru_lr_x = 1.0
fc_lr_x = 1.0
# Input data
src = fluid.layers.data(name="src", shape=[1], dtype="int64", lod_level=1)
pos_label = fluid.layers.data(
name="pos_label", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(
name="label", shape=[neg_size + 1], dtype="int64", lod_level=1)
emb_src = fluid.layers.embedding(
input=src,
size=[vocab_size, hid_size],
param_attr=fluid.ParamAttr(
name="emb",
initializer=fluid.initializer.XavierInitializer(),
learning_rate=emb_lr_x))
emb_src_drop = fluid.layers.dropout(emb_src, dropout_prob=drop_out)
fc0 = fluid.layers.fc(input=emb_src_drop,
size=hid_size * 3,
param_attr=fluid.ParamAttr(
name="gru_fc",
initializer=fluid.initializer.XavierInitializer(),
learning_rate=gru_lr_x),
bias_attr=False)
gru_h0 = fluid.layers.dynamic_gru(
input=fc0,
size=hid_size,
param_attr=fluid.ParamAttr(
name="dy_gru.param",
initializer=fluid.initializer.XavierInitializer(),
learning_rate=gru_lr_x),
bias_attr="dy_gru.bias")
gru_h0_drop = fluid.layers.dropout(gru_h0, dropout_prob=drop_out)
label_re = fluid.layers.sequence_reshape(input=label, new_dim=1)
emb_label = fluid.layers.embedding(
input=label_re,
size=[vocab_size, hid_size],
param_attr=fluid.ParamAttr(
name="emb",
initializer=fluid.initializer.XavierInitializer(),
learning_rate=emb_lr_x))
emb_label_drop = fluid.layers.dropout(emb_label, dropout_prob=drop_out)
gru_exp = fluid.layers.expand(
x=gru_h0_drop, expand_times=[1, (neg_size + 1)])
gru = fluid.layers.sequence_reshape(input=gru_exp, new_dim=hid_size)
ele_mul = fluid.layers.elementwise_mul(emb_label_drop, gru)
red_sum = fluid.layers.reduce_sum(input=ele_mul, dim=1, keep_dim=True)
pre = fluid.layers.sequence_reshape(input=red_sum, new_dim=(neg_size + 1))
cost = fluid.layers.bpr_loss(input=pre, label=pos_label)
cost_sum = fluid.layers.reduce_sum(input=cost)
return src, pos_label, label, cost_sum
def train_cross_entropy_network(vocab_size, neg_size, hid_size, drop_out=0.2):
""" network definition """
emb_lr_x = 1.0
gru_lr_x = 1.0
fc_lr_x = 1.0
# Input data
src = fluid.layers.data(name="src", shape=[1], dtype="int64", lod_level=1)
pos_label = fluid.layers.data(
name="pos_label", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(
name="label", shape=[neg_size + 1], dtype="int64", lod_level=1)
emb_src = fluid.layers.embedding(
input=src,
size=[vocab_size, hid_size],
param_attr=fluid.ParamAttr(
name="emb",
initializer=fluid.initializer.XavierInitializer(),
learning_rate=emb_lr_x))
emb_src_drop = fluid.layers.dropout(emb_src, dropout_prob=drop_out)
fc0 = fluid.layers.fc(input=emb_src_drop,
size=hid_size * 3,
param_attr=fluid.ParamAttr(
name="gru_fc",
initializer=fluid.initializer.XavierInitializer(),
learning_rate=gru_lr_x),
bias_attr=False)
gru_h0 = fluid.layers.dynamic_gru(
input=fc0,
size=hid_size,
param_attr=fluid.ParamAttr(
name="dy_gru.param",
initializer=fluid.initializer.XavierInitializer(),
learning_rate=gru_lr_x),
bias_attr="dy_gru.bias")
gru_h0_drop = fluid.layers.dropout(gru_h0, dropout_prob=drop_out)
label_re = fluid.layers.sequence_reshape(input=label, new_dim=1)
emb_label = fluid.layers.embedding(
input=label_re,
size=[vocab_size, hid_size],
param_attr=fluid.ParamAttr(
name="emb",
initializer=fluid.initializer.XavierInitializer(),
learning_rate=emb_lr_x))
emb_label_drop = fluid.layers.dropout(emb_label, dropout_prob=drop_out)
gru_exp = fluid.layers.expand(
x=gru_h0_drop, expand_times=[1, (neg_size + 1)])
gru = fluid.layers.sequence_reshape(input=gru_exp, new_dim=hid_size)
ele_mul = fluid.layers.elementwise_mul(emb_label_drop, gru)
red_sum = fluid.layers.reduce_sum(input=ele_mul, dim=1, keep_dim=True)
pre_ = fluid.layers.sequence_reshape(input=red_sum, new_dim=(neg_size + 1))
pre = fluid.layers.softmax(input=pre_)
cost = fluid.layers.cross_entropy(input=pre, label=pos_label)
cost_sum = fluid.layers.reduce_sum(input=cost)
return src, pos_label, label, cost_sum
def infer_network(vocab_size, batch_size, hid_size, dropout=0.2):
src = fluid.layers.data(name="src", shape=[1], dtype="int64", lod_level=1)
emb_src = fluid.layers.embedding(
input=src, size=[vocab_size, hid_size], param_attr="emb")
emb_src_drop = fluid.layers.dropout(
emb_src, dropout_prob=dropout, is_test=True)
fc0 = fluid.layers.fc(input=emb_src_drop,
size=hid_size * 3,
param_attr="gru_fc",
bias_attr=False)
gru_h0 = fluid.layers.dynamic_gru(
input=fc0,
size=hid_size,
param_attr="dy_gru.param",
bias_attr="dy_gru.bias")
gru_h0_drop = fluid.layers.dropout(
gru_h0, dropout_prob=dropout, is_test=True)
all_label = fluid.layers.data(
name="all_label",
shape=[vocab_size, 1],
dtype="int64",
append_batch_size=False)
emb_all_label = fluid.layers.embedding(
input=all_label, size=[vocab_size, hid_size], param_attr="emb")
emb_all_label_drop = fluid.layers.dropout(
emb_all_label, dropout_prob=dropout, is_test=True)
all_pre = fluid.layers.matmul(
gru_h0_drop, emb_all_label_drop, transpose_y=True)
pos_label = fluid.layers.data(
name="pos_label", shape=[1], dtype="int64", lod_level=1)
acc = fluid.layers.accuracy(input=all_pre, label=pos_label, k=20)
return acc