测试数据生成

最新推荐文章于 2023-03-07 14:15:38 发布

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该博客展示了如何利用TensorFlow的`tf.convert_to_tensor`函数和Numpy API来创建测试用例，对一个基于GRU的编码器模型进行结构测试。首先，生成随机整数数组作为输入，然后定义并初始化Encoder类，该类包含一个嵌入层和一个GRU层。在主函数中，创建了测试输入，并通过模型进行前向传播，验证了输出和隐藏状态的形状。

通过tf.convert_to_tensor 函数和 numpy api，创建测试用例对模型结构进行测试

```python
np.random.randint(2, size=10)
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0]) # random
np.random.randint(1, size=10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

numpy.random.randint¶


```python
import matplotlib as mpl
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf

from tensorflow import keras

print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)


## 定义模型结构
class Encoder(keras.Model):
    def __init__(self, vocab_size, embedding_units, encoding_units, batch_size):
        super().__init__()
        self.batch_size = batch_size
        self.encoding_units = encoding_units
        self.embedding = keras.layers.Embedding(vocab_size, embedding_units)

        self.gru = keras.layers.GRU(self.encoding_units,
                                    return_sequences=True,
                                    return_state=True,
                                    recurrent_initializer='glorot_uniform')

    def call(self, x, hidden):
        x = self.embedding(x)
        output, state = self.gru(x, initial_state=hidden)
        return output, state

    def initialize_hidden_state(self):
        return tf.zeros((self.batch_size, self.encoding_units))


if __name__ == "__main__":
	input_vocab_size = 9394
	embedding_units = 256
	units = 1024
	batch_size = 64
	epochs = 20

    x = tf.convert_to_tensor(np.random.randint(0,9393,(64, 16)), dtype=tf.int32)
    print(x.shape)
    encoder = Encoder(input_vocab_size, embedding_units, units, batch_size)
    sample_hidden = encoder.initialize_hidden_state()
    sample_output, sample_hidden = encoder(x, sample_hidden)

    print(sample_output.shape, sample_hidden.shape)

Numpy数组创建