diff --git a/tensorflow/intro/README.md b/tensorflow/intro/README.md index 58b92d4..5a5844b 100644 --- a/tensorflow/intro/README.md +++ b/tensorflow/intro/README.md @@ -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 -```