January 2018
Intermediate to advanced
474 pages
10h 23m
English
Content preview from Mastering TensorFlow 1.x
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TFLearn estimator layers
TFLearn offers only one layer in the tflearn.layers.estimator module:
| Layer class | Description |
| regression | This layer implements the linear or logistic regression |
While creating the regression layer, you can specify the optimizer and the loss and metric functions.
TFLearn offers the following optimizer functions as classes in the tflearn.optimizers module:
- SGD
- RMSprop
- Adam
- Momentum
- AdaGrad
- Ftrl
- AdaDelta
- ProximalAdaGrad
- Nesterov
You can create custom optimizers by extending the tflearn.optimizers.Optimizer base class.
TFLearn offers the following metric functions as classes or ops in the tflearn.metrics module:
- Accuracy or accuracy_op
- Top_k or top_k_op
- R2 or r2_op
- WeightedR2 or weighted_r2_op
- binary_accuracy_op ...
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