model: learning_rate_E_G: 4e-4 learning_rate_D: 4e-4 beta1: 0。99 beta2: 0.999 batch_size: 16 num_workers: 16 grad_clip: 0.0
时间: 2023-04-06 19:02:30 浏览: 69
这个问题涉及到模型的超参数设置,我可以回答。这个模型使用了学习率为4e-4的Adam优化器,其中生成器和编码器的学习率相同,为4e-4。beta1和beta2分别为0.99和0.999,用于Adam优化器的参数。batch_size为16,num_workers为16,用于数据加载器的设置。grad_clip为0.0,表示没有进行梯度裁剪。
相关问题
用代码实现:使用Nadam时使用learning_rate参数来设置学习率的值
在使用Nadam优化器时,可以通过设置learning_rate参数来调整学习率的值。以下是一个示例代码:
```
from keras.optimizers import Nadam
nadam = Nadam(learning_rate=0.001) # 设置学习率为0.001
model.compile(loss='categorical_crossentropy', optimizer=nadam)
```
在以上代码中,我们首先导入了Keras中的Nadam优化器,然后通过在实例化Nadam时传递learning_rate参数来设置学习率的值。最后,我们将Nadam作为优化器传递给模型.compile()函数中。这样,在训练模型时,Nadam优化器将使用设置的学习率来更新模型参数。
TypeError: plot_learning_curve() got an unexpected keyword argument 'figsize'
This error occurs when the `plot_learning_curve()` function is called with an additional argument `figsize`, which is not defined in the function. This means that the function does not accept the `figsize` argument, and it is causing a TypeError.
To resolve this error, you can remove the `figsize` argument from the function call or modify the `plot_learning_curve()` function to accept the `figsize` argument. If you want to modify the function, you can do so by adding the `figsize` argument to the function definition and using it to set the size of the plot in the function code.
For example:
```python
import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5), figsize=None):
if figsize:
plt.figure(figsize=figsize)
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score")
plt.legend(loc="best")
return plt
```
Now, you can call the `plot_learning_curve()` function with the `figsize` argument:
```python
plot_learning_curve(estimator, 'Learning Curve', X_train, y_train, figsize=(8, 6))
```