-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathmodel.py
174 lines (72 loc) · 4.75 KB
/
model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
import tensorflow as tf
from tensorflow.contrib import layers
from tensorflow.python.layers.core import Dense
from tensorflow.python.ops import embedding_ops
import numpy as np
slim=tf.contrib.slim
image = tf.placeholder(tf.float32, shape=(None,cfg.IMAGE_WIDTH,cfg.IMAGE_HEIGHT, 1), name='img_data')
train_output = tf.placeholder(tf.int64, shape=[None, None], name='train_output')
target_output = tf.placeholder(tf.int64, shape=[None, None], name='target_output')
sample_rate=tf.placeholder(tf.float32, shape=[], name='sample_rate')
train_length=np.array([27]*cfg.BATCH_SIZE,dtype=np.int32)
def encoder_net(_image, scope,is_training,reuse=None):
with tf.variable_scope(scope, reuse=reuse):
net = tf.layers.batch_normalization(_image, training=is_training)
net = slim.conv2d(net, 64, [3, 3], scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.conv2d(net, 128, [3, 3], scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.conv2d(net, 256, [3, 3], activation_fn=None, scope='conv3')
net = tf.layers.batch_normalization(net, training=is_training)
net = tf.nn.relu(net)
net = slim.conv2d(net, 256, [3, 3], scope='conv4')
net = slim.max_pool2d(net, [2, 2], [1, 2], scope='pool3')
net = slim.conv2d(net, 512, [3, 3], activation_fn=None, scope='conv5')
net = tf.layers.batch_normalization(net, training=is_training)
net = tf.nn.relu(net)
net = slim.conv2d(net, 512, [3, 3], scope='conv6')
net = slim.max_pool2d(net, [2, 2], [1, 2], scope='pool4')
net = slim.conv2d(net, 512, [2, 2], padding='VALID', activation_fn=None, scope='conv7')
net = tf.layers.batch_normalization(net, training=is_training)
net = tf.nn.relu(net)#CRNN
cnn_out = tf.squeeze(net,axis=2)
cell = tf.contrib.rnn.GRUCell(num_units=256)
enc_outputs, enc_state = tf.nn.bidirectional_dynamic_rnn(cell_fw=cell,cell_bw=cell,inputs=cnn_out,dtype=tf.float32)#双向LSTM
encoder_outputs = tf.concat(enc_outputs, -1)
return encoder_outputs,enc_state
def decode(helper, memory, scope, enc_state,reuse=None):
with tf.variable_scope(scope, reuse=reuse):
attention_mechanism = tf.contrib.seq2seq.LuongAttention(num_units=256, memory=memory)
cell = tf.contrib.rnn.GRUCell(num_units=256)
attn_cell = tf.contrib.seq2seq.AttentionWrapper(cell, attention_mechanism, attention_layer_size=256, output_attention=True)
output_layer = Dense(units=4)
decoder = tf.contrib.seq2seq.BasicDecoder(
cell=attn_cell, helper=helper,
initial_state=attn_cell.zero_state(dtype=tf.float32, batch_size=40).clone(cell_state=enc_state[0]),
output_layer=output_layer)
outputs = tf.contrib.seq2seq.dynamic_decode(
decoder=decoder, output_time_major=False,
impute_finished=True, maximum_iterations=27)
return outputs
def build_network(is_training):
train_output_embed,enc_state= encoder_net(image, 'encode_features',is_training)
#vocab_size: 输入数据的总词汇量，指的是总共有多少类词汇，不是总个数，embed_dim：想要得到的嵌入矩阵的维度
embeddings = tf.get_variable(name='embed_matrix',shape=[4, 4])
output_embed=embedding_ops.embedding_lookup(embeddings,train_output)
start_tokens = tf.zeros([40], dtype=tf.int64)
train_helper = tf.contrib.seq2seq.ScheduledEmbeddingTrainingHelper(output_embed, train_length,
embeddings, sample_rate)
#用于inference阶段的helper，将output输出后的logits使用argmax获得id再经过embedding layer来获取下一时刻的输入。
#start_tokens： batch中每个序列起始输入的token_id end_token：序列终止的token_id
#start_tokens: int32 vector shaped [batch_size], the start tokens.
#end_token: int32 scalar, the token that marks end of decoding.
pred_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(embeddings, start_tokens=tf.to_int32(start_tokens), end_token=1)#GO,EOS的序号
train_outputs = decode(train_helper, train_output_embed,'decode',enc_state)
pred_outputs = decode(pred_helper, train_output_embed, 'decode',enc_state, reuse=True)
train_decode_result = train_outputs[0].rnn_output[:, :-1, :]
pred_decode_result = pred_outputs[0].rnn_output
mask = tf.cast(tf.sequence_mask(40 * [train_length[0] - 1], train_length[0]),
tf.float32)
att_loss = tf.contrib.seq2seq.sequence_loss(train_outputs[0].rnn_output, target_output,weights=mask)
loss = tf.reduce_mean(att_loss)
return loss,train_decode_result, pred_decode_result