Advances, Systems and Applications
Table 3 Structure of our proposed model
From: ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing
Layers | Type | #of feature map | Feature map size | Window size | #of parameters |
|---|---|---|---|---|---|
E1 | Embedding | 200 | — | — | 8,855,400 |
C2 | Convolution | 32 | 32 ×32 | 4 ×4 | 25,632 |
L3 | Bi-LSTM | 200 | — | — | 106,400 |
C4 | Convolution | 16 | 16 ×16 | 4 ×4 | 12,816 |
P5 | Max pooling | 16 | — | 2 ×2 | 0 |
D6 | Dropout | 16 | — | — | 0 |
F7 | Flatten | — | — | — | 0 |
D8 | Dense | 15 | — | — | 164,895 |
D9 | Dense | 1 | — | — | 16 |
Total parameters | 9,165,159 | ||||
Trainable | 9,165,159 | ||||
Non-trainable | 0 |