- Mastering TensorFlow 1.x
- Armando Fandango
- 167字
- 2021-06-25 22:51:00
TFLearn estimator layers
TFLearn offers only one layer in the tflearn.layers.estimator module:

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
You can create custom metrics by extending the tflearn.metrics.Metric base class.
TFLearn provides the following loss functions, known as objectives, in the tflearn.objectives module:
- softymax_categorical_crossentropy
- categorical_crossentropy
- binary_crossentropy
- weighted_crossentropy
- mean_square
- hinge_loss
- roc_auc_score
- weak_cross_entropy_2d
While specifying the input, hidden, and output layers, you can specify the activation functions to be applied to the output. TFLearn provides the following activation functions in the tflearn.activations module:
- linear
- tanh
- sigmoid
- softmax
- softplus
- softsign
- relu
- relu6
- leaky_relu
- prelu
- elu
- crelu
- selu
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