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