# Function Differences with tf.compat.v1.train.cosine_decay

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## tf.compat.v1.train.cosine_decay

```text
tf.compat.v1.train.cosine_decay(
    learning_rate,
    global_step,
    decay_steps,
    alpha=0.0,
    name=None
) -> Tensor
```

For more information, see [tf.compat.v1.train.cosine_decay](https://tensorflow.google.cn/versions/r2.6/api_docs/python/tf/compat/v1/train/cosine_decay).

## mindspore.nn.CosineDecayLR

```text
class mindspore.nn.CosineDecayLR(
    min_lr,
    max_lr,
    decay_steps
)(global_step) -> Tensor
```

For more information, see [mindspore.nn.CosineDecayLR](https://mindspore.cn/docs/en/r2.1/api_python/nn/mindspore.nn.CosineDecayLR.html).

## Differences

TensorFlow: The learning rate is calculated based on the cosine decay function.

MindSpore: This API achieves basically the same function as TensorFlow's. After MindSpore max_lr is fixed to 1, TensorFlow outputs the decayed learning rate, while MindSpore outputs the decayed rate. That is, the MindSpore output is multiplied by the same learning_rate as TensorFlow, and the two yield the same result.

| Categories | Subcategories |TensorFlow | MindSpore | Differences |
| --- | --- | --- | --- |---|
|Parameters | Parameter 1 | learning_rate | - |Initial learning rate. MindSpore does not have this parameter |
| | Parameter 2 | global_step | global_step |- |
| | Parameter 3 | decay_steps | decay_steps |- |
| | Parameter 4 | alpha | min_lr |Same function, different parameter names|
| | Parameter 5 | name | - | Not involved |
| | Parameter 6 | - | max_lr |The maximum value of learning rate. TensorFlow doesn't have this parameter |

### Code Example

> The max_lr of MindSpore is fixed to 1, and its output is multiplied by the same learning_rate as TensorFlow. The two APIs achieve the same function.

```python
# TensorFlow
# TensorFlow
import tensorflow as tf

tf.compat.v1.disable_eager_execution()
learning_rate = 0.01
global_steps = 2
decay_steps = 4
output = tf.compat.v1.train.cosine_decay(learning_rate, global_steps, decay_steps)
ss = tf.compat.v1.Session()
print(ss.run(output))
# 0.0049999994

# MindSpore
import mindspore
from mindspore import Tensor, nn

min_lr = 0.01
max_lr = 1.0
decay_steps = 4
global_steps = Tensor(2, mindspore.int32)
cosine_decay_lr = nn.CosineDecayLR(min_lr, max_lr, decay_steps)
output = cosine_decay_lr(global_steps)
adapted_output = output * 0.01
print(adapted_output)
# 0.00505
```