Yanjun Peng
5 years ago
1 changed files with 6 additions and 15 deletions
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+ 6
- 15
model_zoo/official/nlp/textcnn/src/textcnn.py
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| @@ -14,7 +14,6 @@ | |||||
| # ============================================================================ | # ============================================================================ | ||||
| """TextCNN""" | """TextCNN""" | ||||
| import numpy as np | |||||
| import mindspore.nn as nn | import mindspore.nn as nn | ||||
| import mindspore.ops.operations as P | import mindspore.ops.operations as P | ||||
| from mindspore import Tensor | from mindspore import Tensor | ||||
| @@ -89,17 +88,9 @@ class SoftmaxCrossEntropyExpand(Cell): | |||||
| return loss | return loss | ||||
| def _weight_variable(shape, factor=0.01): | |||||
| init_value = np.random.randn(*shape).astype(np.float32) * factor | |||||
| return Tensor(init_value) | |||||
| def make_conv_layer(kernel_size): | def make_conv_layer(kernel_size): | ||||
| weight_shape = (96, 1, *kernel_size) | |||||
| weight = _weight_variable(weight_shape) | |||||
| return nn.Conv2d(in_channels=1, out_channels=96, kernel_size=kernel_size, padding=1, | return nn.Conv2d(in_channels=1, out_channels=96, kernel_size=kernel_size, padding=1, | ||||
| pad_mode="pad", weight_init=weight, has_bias=True) | |||||
| pad_mode="pad", weight_init='uniform', has_bias=True) | |||||
| class TextCNN(nn.Cell): | class TextCNN(nn.Cell): | ||||
| @@ -113,7 +104,7 @@ class TextCNN(nn.Cell): | |||||
| self.num_classes = num_classes | self.num_classes = num_classes | ||||
| self.unsqueeze = P.ExpandDims() | self.unsqueeze = P.ExpandDims() | ||||
| self.embedding = nn.Embedding(vocab_len, self.vec_length, embedding_table='normal') | |||||
| self.embedding = nn.Embedding(vocab_len, self.vec_length, embedding_table='uniform') | |||||
| self.slice = P.Slice() | self.slice = P.Slice() | ||||
| self.layer1 = self.make_layer(kernel_height=3) | self.layer1 = self.make_layer(kernel_height=3) | ||||
| @@ -125,7 +116,7 @@ class TextCNN(nn.Cell): | |||||
| self.fc = nn.Dense(96*3, self.num_classes) | self.fc = nn.Dense(96*3, self.num_classes) | ||||
| self.drop = nn.Dropout(keep_prob=0.5) | self.drop = nn.Dropout(keep_prob=0.5) | ||||
| self.print = P.Print() | self.print = P.Print() | ||||
| self.reducemean = P.ReduceMax(keep_dims=False) | |||||
| self.reducemax = P.ReduceMax(keep_dims=False) | |||||
| def make_layer(self, kernel_height): | def make_layer(self, kernel_height): | ||||
| return nn.SequentialCell( | return nn.SequentialCell( | ||||
| @@ -145,9 +136,9 @@ class TextCNN(nn.Cell): | |||||
| x2 = self.layer2(x) | x2 = self.layer2(x) | ||||
| x3 = self.layer3(x) | x3 = self.layer3(x) | ||||
| x1 = self.reducemean(x1, (2, 3)) | |||||
| x2 = self.reducemean(x2, (2, 3)) | |||||
| x3 = self.reducemean(x3, (2, 3)) | |||||
| x1 = self.reducemax(x1, (2, 3)) | |||||
| x2 = self.reducemax(x2, (2, 3)) | |||||
| x3 = self.reducemax(x3, (2, 3)) | |||||
| x = self.concat((x1, x2, x3)) | x = self.concat((x1, x2, x3)) | ||||
| x = self.drop(x) | x = self.drop(x) | ||||