如果上述代码中的probabilities.begins()缺省了会怎么样
时间: 2024-12-29 17:33:31 浏览: 24
如果`probabilities.begin()`在创建`std::discrete_distribution<int>`时没有提供,那么编译器会在尝试访问`begin()`方法时遇到错误,因为`probabilities`是一个`vector<double>`,而`begin()`通常需要一个迭代器或者指向容器起始位置的指针。
`probabilities.begin()`的作用是返回存储概率值的`double`序列的开始迭代器,这是为了将概率数据与离散值关联起来。如果没有显式地指定这个迭代器,你需要保证`probabilities`列表有正确的初始化,并且它的大小与`distribution`列表一致,这样才能正确反映每个离散值对应的确切概率。
在实际应用中,一般会按照以下形式传递:
```cpp
std::discrete_distribution<int> dist(distribution.begin(), distribution.end(), probabilities.begin());
```
如果`probabilities.begin()`被遗漏,编译器会报错,提示需要提供一个迭代器来表示概率值的源。如果你没有明确指定,你应该检查`probabilities`数组是否已经初始化并且长度匹配`distribution`。
相关问题
上述代码中出现报错probabilities do not sum to 1
这个错误通常出现在使用概率分布时,因为概率分布的所有可能结果的概率总和应该等于1。如果出现了这个错误,那么说明你的概率分布的结果并没有加起来等于1。
要解决这个问题,你需要检查你的代码,确保你计算的概率分布的所有可能结果的概率总和确实等于1。另外,你也可以尝试使用一些Python库,比如NumPy或SciPy,来计算概率分布并确保它们的总和等于1。
按照以下要求仅在两个#之间编译该代码As described in the lecture and the optional softmax lab, numerical stability is improved if the softmax is grouped with the loss function rather than the output layer during training. This has implications when building the model and using the model. Building: The final Dense layer should use a 'linear' activation. This is effectively no activation. The model.compile statement will indicate this by including from_logits=True. loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) This does not impact the form of the target. In the case of SparseCategorialCrossentropy, the target is the expected digit, 0-9. Using the model: The outputs are not probabilities. If output probabilities are desired, apply a softmax function.
Sure, I can help you with that! Here's the code you provided, compiled with the required modifications:
```python
import tensorflow as tf
# Building the model
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(input_dim,)),
tf.keras.layers.Dense(10, activation='linear')
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# Using the model
output = model.predict(input_data)
# Apply softmax for obtaining probabilities
probabilities = tf.nn.softmax(output)
```
Please note that `input_dim` and `input_data` in the code should be replaced with the appropriate values for your specific case. Let me know if you need any further assistance!
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