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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""LSTM."""
import numpy as np
from mindspore import Tensor, nn, context
from mindspore.ops import operations as P
# Initialize short-term memory (h) and long-term memory (c) to 0
def lstm_default_state(batch_size, hidden_size, num_layers, bidirectional):
"""init default input."""
num_directions = 1
if bidirectional:
num_directions = 2
if context.get_context("device_target") == "CPU":
h_list = []
c_list = []
i = 0
while i < num_layers:
hi = Tensor(np.zeros((num_directions, batch_size, hidden_size)).astype(np.float32))
h_list.append(hi)
ci = Tensor(np.zeros((num_directions, batch_size, hidden_size)).astype(np.float32))
c_list.append(ci)
i = i + 1
h = tuple(h_list)
c = tuple(c_list)
return h, c
h = Tensor(
np.zeros((num_layers * num_directions, batch_size, hidden_size)).astype(np.float32))
c = Tensor(
np.zeros((num_layers * num_directions, batch_size, hidden_size)).astype(np.float32))
return h, c
class SentimentNet(nn.Cell):
"""Sentiment network structure."""
def __init__(self,
vocab_size,
embed_size,
num_hiddens,
num_layers,
bidirectional,
num_classes,
weight,
batch_size):
super(SentimentNet, self).__init__()
# Mapp words to vectors
self.embedding = nn.Embedding(vocab_size,
embed_size,
embedding_table=weight)
self.embedding.embedding_table.requires_grad = False
self.trans = P.Transpose()
self.perm = (1, 0, 2)
self.encoder = nn.LSTM(input_size=embed_size,
hidden_size=num_hiddens,
num_layers=num_layers,
has_bias=True,
bidirectional=bidirectional,
dropout=0.0)
self.h, self.c = lstm_default_state(batch_size, num_hiddens, num_layers, bidirectional)
self.concat = P.Concat(1)
if bidirectional:
self.decoder = nn.Dense(num_hiddens * 4, num_classes)
else:
self.decoder = nn.Dense(num_hiddens * 2, num_classes)
def construct(self, inputs):
# input:(64,500,300)
embeddings = self.embedding(inputs)
embeddings = self.trans(embeddings, self.perm)
output, _ = self.encoder(embeddings, (self.h, self.c))
# states[i] size(64,200) -> encoding.size(64,400)
encoding = self.concat((output[0], output[499]))
outputs = self.decoder(encoding)
return outputs
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