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@ -550,9 +550,10 @@ for i in range(1, len(weights_bias), 2):
counter_layer += 1
```
## Type of layers
## [Type of layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers#classes_2)
Reduced list with the most relevant network layers
|||
|---|---|
|[Activation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Activation)| Applies an activation function to an output.|
@ -577,77 +578,89 @@ Reduced list with the most relevant network layers
|[ZeroPadding3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding3D) |Zero-padding layer for 3D data (spatial or spatio-temporal).|
Preprocessing layers
## [Preprocessing layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers#classes_2)
Reduced list with the most relevant preprocessing layers
CenterCrop A preprocessing layer which crops images.
RandomContrast A preprocessing layer which randomly adjusts contrast during training.
RandomCrop A preprocessing layer which randomly crops images during training.
RandomFlip A preprocessing layer which randomly flips images during training.
RandomHeight A preprocessing layer which randomly varies image height during training.
RandomRotation A preprocessing layer which randomly rotates images during training.
RandomTranslation A preprocessing layer which randomly translates images during training.
RandomWidth A preprocessing layer which randomly varies image width during training.
RandomZoom A preprocessing layer which randomly zooms images during training.
Rescaling A preprocessing layer which rescales input values to a new range.
Resizing A preprocessing layer which resizes images.
|||
|---|---|
|[CenterCrop](https://www.tensorflow.org/api_docs/python/tf/keras/layers/CenterCrop) |A preprocessing layer which crops images.|
|[RandomContrast](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomContrast) |A preprocessing layer which randomly adjusts contrast during training.|
|[RandomCrop](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomCrop) |A preprocessing layer which randomly crops images during training.|
|[RandomFlip](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomFlip) |A preprocessing layer which randomly flips images during training.|
|[RandomHeight](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomHeight)| A preprocessing layer which randomly varies image height during training.|
|[RandomRotation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomRotation) |A preprocessing layer which randomly rotates images during training.|
|[RandomTranslation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomTranslation) |A preprocessing layer which randomly translates images during training.|
|[RandomWidth](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomWidth) |A preprocessing layer which randomly varies image width during training.|
|[RandomZoom](https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomZoom) |A preprocessing layer which randomly zooms images during training.|
|[Rescaling](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Rescaling) |A preprocessing layer which rescales input values to a new range.|
|[Resizing](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Resizing) |A preprocessing layer which resizes images.|
Activation functions
## [Activation functions](https://www.tensorflow.org/api_docs/python/tf/keras/activations)
Reduced list with the most relevant activation functions
hard_sigmoid(...) Hard sigmoid activation function.
relu(...) Applies the rectified linear unit activation function.
sigmoid(...) Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x)).
softmax(...) Softmax converts a vector of values to a probability distribution.
softplus(...) Softplus activation function, softplus(x) = log(exp(x) + 1).
softsign(...) Softsign activation function, softsign(x) = x / (abs(x) + 1).
tanh(...) Hyperbolic tangent activation function.
|||
|---|---|
|[hard_sigmoid(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/hard_sigmoid)| Hard sigmoid activation function.|
|[relu(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/relu) |Applies the rectified linear unit activation function.|
|[sigmoid(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/sigmoid) |Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x)).|
|[softmax(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax) |Softmax converts a vector of values to a probability distribution.|
|[softplus(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softplus) |Softplus activation function, softplus(x) = log(exp(x) + 1).|
|[softsign(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softsign) |Softsign activation function, softsign(x) = x / (abs(x) + 1).|
|[tanh(...)](https://www.tensorflow.org/api_docs/python/tf/keras/activations/tanh) |Hyperbolic tangent activation function.|
Loss-functions
## [Loss-functions](https://www.tensorflow.org/api_docs/python/tf/keras/losses)
Reduced list with the most relevant loss functions
BinaryCrossentropy Computes the cross-entropy loss between true labels and predicted labels.
CategoricalCrossentropy Computes the crossentropy loss between the labels and predictions.
KLDivergence Computes Kullback-Leibler divergence loss between y_true and y_pred.
MeanAbsoluteError Computes the mean of absolute difference between labels and predictions.
MeanSquaredError Computes the mean of squares of errors between labels and predictions.
Poisson Computes the Poisson loss between y_true and y_pred.
SparseCategoricalCrossentropy Computes the crossentropy loss between the labels and predictions.
|||
|---|---|
|[BinaryCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy) |Computes the cross-entropy loss between true labels and predicted labels.|
|[CategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy) |Computes the crossentropy loss between the labels and predictions.|
|[KLDivergence](https://www.tensorflow.org/api_docs/python/tf/keras/losses/KLDivergence) |Computes Kullback-Leibler divergence loss between y_true and y_pred.|
|[MeanAbsoluteError](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MeanAbsoluteError) |Computes the mean of absolute difference between labels and predictions.|
|[MeanSquaredError](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MeanSquaredError) |Computes the mean of squares of errors between labels and predictions.|
|[Poisson](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Poisson) |Computes the Poisson loss between y_true and y_pred.|
|[SparseCategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy) |Computes the crossentropy loss between the labels and predictions.|
Optimizer
## [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)
Reduced list with the most relevant optimizer
Adagrad Optimizer that implements the Adagrad algorithm.
Adam Optimizer that implements the Adam algorithm.
RMSprop Optimizer that implements the RMSprop algorithm.
SGD Gradient descent (with momentum) optimizer.
|||
|---|---|
|[Adagrad](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/Adagrad) |Optimizer that implements the Adagrad algorithm.|
|[Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) |Optimizer that implements the Adam algorithm.|
|[RMSprop](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/RMSprop) |Optimizer that implements the RMSprop algorithm.|
|[SGD](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/SGD) |Gradient descent (with momentum) optimizer.|
Metrics
## [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)
A very reduced list with the most relevant metrics
Accuracy Calculates how often predictions equal labels.
BinaryAccuracy Calculates how often predictions match binary labels.
BinaryCrossentropy Computes the crossentropy metric between the labels and predictions.
CategoricalAccuracy Calculates how often predictions match one-hot labels.
CategoricalCrossentropy Computes the crossentropy metric between the labels and predictions.
KLDivergence Computes Kullback-Leibler divergence metric between y_true and y_pred.
Mean Computes the (weighted) mean of the given values.
MeanAbsoluteError Computes the mean absolute error between the labels and predictions.
MeanSquaredError Computes the mean squared error between y_true and y_pred.
Poisson Computes the Poisson metric between y_true and y_pred.
Precision Computes the precision of the predictions with respect to the labels.
RootMeanSquaredError Computes root mean squared error metric between y_true and y_pred.
SparseCategoricalAccuracy Calculates how often predictions match integer labels.
SparseCategoricalCrossentropy Computes the crossentropy metric between the labels and predictions.
SparseTopKCategoricalAccuracy Computes how often integer targets are in the top K predictions.
Sum Computes the (weighted) sum of the given values.
TopKCategoricalAccuracy Computes how often targets are in the top K predictions.
|||
|---|---|
|[Accuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Accuracy) | Calculates how often predictions equal labels.|
|[BinaryAccuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/BinaryAccuracy) | Calculates how often predictions match binary labels.|
|[BinaryCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/BinaryCrossentropy) | Computes the crossentropy metric between the labels and predictions.|
|[CategoricalAccuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalAccuracy) | Calculates how often predictions match one-hot labels.|
|[CategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/CategoricalCrossentropy) | Computes the crossentropy metric between the labels and predictions.|
|[KLDivergence](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/KLDivergence) | Computes Kullback-Leibler divergence metric between y_true and y_pred.|
|[Mean](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Mean) | Computes the (weighted) mean of the given values.|
|[MeanAbsoluteError](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanAbsoluteError) | Computes the mean absolute error between the labels and predictions.|
|[MeanSquaredError](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanSquaredError) | Computes the mean squared error between y_true and y_pred.|
|[Poisson](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Poisson) | Computes the Poisson metric between y_true and y_pred.|
|[Precision](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Precision) | Computes the precision of the predictions with respect to the labels.|
|[RootMeanSquaredError](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/RootMeanSquaredError) | Computes root mean squared error metric between y_true and y_pred.|
|[SparseCategoricalAccuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseCategoricalAccuracy) | Calculates how often predictions match integer labels.|
|[SparseCategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseCategoricalCrossentropy) | Computes the crossentropy metric between the labels and predictions.|
|[SparseTopKCategoricalAccuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SparseTopKCategoricalAccuracy) | Computes how often integer targets are in the top K predictions.|
|[Sum](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Sum) | Computes the (weighted) sum of the given values.|
|[TopKCategoricalAccuracy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/TopKCategoricalAccuracy)| Computes how often targets are in the top K predictions.|
```python
```