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pytutorial/tensorflow/intro/README.md
David Rotermund a92210aac3
Update README.md 
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
2024-01-03 21:32:03 +01:00

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# Tensorflow / Keras -- A fast non-introduction
{:.no_toc}
<nav markdown="1" class="toc-class">
* TOC
{:toc}
</nav>
## Top
This is a fast overview how to get an MNIST example running under TF Keras
If you just want to start with Tensorflow / Keras (especially if it is a scientific project), then you want to reconsider using Keras. In this case please check (& use) PyTorch.
Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
## Data loader / Data generator
|||
|---|---|
| [keras.utils.Sequence](https://www.tensorflow.org/api_docs/python/tf/keras/utils/Sequence) | "Base object for fitting to a sequence of data, such as a dataset."|
| [tf.keras.utils.to_categorical](https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical) | "Converts a class vector (integers) to binary class matrix."|
## Basic
[DataGenerator_no_dataugmentation.py](DataGenerator_no_dataugmentation.py)
```python
from tensorflow import keras
import numpy as np
class DataGenerator(keras.utils.Sequence):
def __init__(
self,
train: bool = True,
size_of_batch: int = 32,
number_of_classes: int = 10,
do_shuffle: bool = True,
) -> None:
super(DataGenerator, self).__init__()
if train is True:
self.pattern_storage: np.ndarray = np.load("./train_pattern_storage.npy")
self.label_storage: np.ndarray = np.load("./train_label_storage.npy")
else:
self.pattern_storage = np.load("./test_pattern_storage.npy")
self.label_storage = np.load("./test_label_storage.npy")
self.pattern_storage = self.pattern_storage.astype(np.float32)
self.pattern_storage /= np.max(self.pattern_storage)
self.dimensions: tuple[int, int] = (
self.pattern_storage.shape[1],
self.pattern_storage.shape[2],
)
# How many pattern are there?
self.number_of_pattern: int = self.label_storage.shape[0]
self.size_of_batch: int = size_of_batch
self.number_of_classes: int = number_of_classes
self.do_shuffle: bool = do_shuffle
if self.pattern_storage.ndim == 3:
self.number_of_channel: int = 1
else:
self.number_of_channel = self.pattern_storage.shape[3]
self.available_indices: np.ndarray = np.arange(self.number_of_pattern)
self.on_epoch_end()
def on_epoch_end(self) -> None:
self.available_indices = np.arange(self.number_of_pattern)
if self.do_shuffle is True:
np.random.shuffle(self.available_indices)
def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray]:
selected_indices: np.ndarray = self.available_indices[
index * self.size_of_batch : (index + 1) * self.size_of_batch
]
image, target = self.__data_generation(selected_indices)
return image, target
def __data_generation(
self, list_of_indice: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
image = np.empty(
(self.size_of_batch, *self.dimensions, self.number_of_channel),
dtype=np.float32,
)
target = np.empty((self.size_of_batch), dtype=int)
for i in range(0, len(list_of_indice)):
if self.pattern_storage.ndim == 3:
image[i, :, :, 0] = self.pattern_storage[
self.available_indices[list_of_indice[i]], :, :
]
else:
image[i, :, :, :] = self.pattern_storage[
self.available_indices[list_of_indice[i]], :, :, :
]
target[i] = self.label_storage[self.available_indices[list_of_indice[i]]]
return image, keras.utils.to_categorical(
target, num_classes=self.number_of_classes
)
def __len__(self):
return int(np.floor(self.number_of_pattern / self.size_of_batch))
if __name__ == "__main__":
pass
```
## With data augmentation
To the pre-processing chain **self.data_augmentation** you can add other preprocessing layers which are then applied to the input before given to the network.
[DataGenerator.py](DataGenerator.py)
```python
from tensorflow import keras
import numpy as np
class DataGenerator(keras.utils.Sequence):
def __init__(
self,
train: bool = True,
size_of_batch: int = 32,
number_of_classes: int = 10,
do_shuffle: bool = True,
) -> None:
super(DataGenerator, self).__init__()
if train is True:
self.pattern_storage: np.ndarray = np.load("./train_pattern_storage.npy")
self.label_storage: np.ndarray = np.load("./train_label_storage.npy")
else:
self.pattern_storage = np.load("./test_pattern_storage.npy")
self.label_storage = np.load("./test_label_storage.npy")
self.pattern_storage = self.pattern_storage.astype(np.float32)
self.pattern_storage /= np.max(self.pattern_storage)
self.dimensions: tuple[int, int] = (
self.pattern_storage.shape[1],
self.pattern_storage.shape[2],
)
reduction: tuple[int, int] = (4, 4)
if train is True:
self.data_augmentation = keras.Sequential(
[
keras.layers.RandomCrop(
height=self.dimensions[0] - reduction[0],
width=self.dimensions[1] - reduction[1],
),
]
)
else:
self.data_augmentation = keras.Sequential(
[
keras.layers.CenterCrop(
height=self.dimensions[0] - reduction[0],
width=self.dimensions[1] - reduction[1],
),
]
)
# How many pattern are there?
self.number_of_pattern: int = self.label_storage.shape[0]
self.size_of_batch: int = size_of_batch
self.number_of_classes: int = number_of_classes
self.do_shuffle: bool = do_shuffle
if self.pattern_storage.ndim == 3:
self.number_of_channel: int = 1
else:
self.number_of_channel = self.pattern_storage.shape[3]
self.available_indices: np.ndarray = np.arange(self.number_of_pattern)
self.on_epoch_end()
def on_epoch_end(self) -> None:
self.available_indices = np.arange(self.number_of_pattern)
if self.do_shuffle is True:
np.random.shuffle(self.available_indices)
def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray]:
selected_indices: np.ndarray = self.available_indices[
index * self.size_of_batch : (index + 1) * self.size_of_batch
]
image, target = self.__data_generation(selected_indices)
return image, target
def __data_generation(
self, list_of_indice: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
image = np.empty(
(self.size_of_batch, *self.dimensions, self.number_of_channel),
dtype=np.float32,
)
target = np.empty((self.size_of_batch), dtype=int)
for i in range(0, len(list_of_indice)):
if self.pattern_storage.ndim == 3:
image[i, :, :, 0] = self.pattern_storage[
self.available_indices[list_of_indice[i]], :, :
]
else:
image[i, :, :, :] = self.pattern_storage[
self.available_indices[list_of_indice[i]], :, :, :
]
target[i] = self.label_storage[self.available_indices[list_of_indice[i]]]
image = self.data_augmentation(image)
return image, keras.utils.to_categorical(
target, num_classes=self.number_of_classes
)
def __len__(self):
return int(np.floor(self.number_of_pattern / self.size_of_batch))
if __name__ == "__main__":
pass
```
## Train an example MNIST network
|||
|---|---|
|[tf.keras.backend.clear_session](https://www.tensorflow.org/api_docs/python/tf/keras/backend/clear_session) | "Resets all state generated by Keras."|
|[tf.keras.Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) | "Sequential groups a linear stack of layers into a tf.keras.Model."|
|[network.add()](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#add) | "Adds a layer instance on top of the layer stack." |
|[tf.keras.layers.Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D) | "2D convolution layer (e.g. spatial convolution over images)."|
|[tf.keras.layers.MaxPool2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPooling2D) | "Max pooling operation for 2D spatial data."|
|[tf.keras.layers.Flatten](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten) | "Flattens the input. Does not affect the batch size."|
|[tf.keras.layers.Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense) | "Just your regular densely-connected NN layer."|
|[network.compile()](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#compile) | "Configures the model for training."|
|[tf.keras.metrics.categorical_crossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/categorical_crossentropy) | "Computes the categorical crossentropy loss."|
|[tf.keras.optimizers.Adam](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam) | "Optimizer that implements the Adam algorithm."|
|[network.fit() ](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#fit) | Trains the model for a fixed number of epochs (iterations on a dataset).|
|[network.summary()](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#summary) | "Prints a string summary of the network."|
|[network.save()](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#save) | "Saves the model to Tensorflow SavedModel or a single HDF5 file."|
Parameters for the layers:
|||
|---|---|
|[padding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D) | "One of "valid", "same" or "causal" (case-insensitive). "valid" means no padding. "same" results in padding with zeros evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. "causal" results in causal (dilated) convolutions, e.g. output[t] does not depend on input[t+1:]. "|
|[use_bias](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D)| "Boolean, whether the layer uses a bias vector."|
|[activation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D)| "Activation function to use. If you don't specify anything, no activation is applied (see [keras.activations](https://www.tensorflow.org/api_docs/python/tf/keras/activations))."|
[data_format](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D)| " A string, one of channels_last (default) or channels_first."|
[keras_learn.py](keras_learn.py)
```python
from tensorflow import keras
from DataGenerator import DataGenerator
epoch_max: int = 50
number_of_classes: int = 10
size_of_batch_train: int = 100
train_data = DataGenerator(
train=True,
size_of_batch=size_of_batch_train,
number_of_classes=number_of_classes,
do_shuffle=True,
)
number_of_channels: int = train_data.number_of_channel
input_dimensions = train_data.dimensions
number_of_pattern_train = train_data.number_of_pattern
number_of_output_channels_conv1: int = 32
number_of_output_channels_conv2: int = 64
number_of_neurons_flatten1: int = 1024
kernel_size_conv1: tuple[int, int] = (5, 5)
kernel_size_pool1: tuple[int, int] = (2, 2)
kernel_size_conv2: tuple[int, int] = (5, 5)
kernel_size_pool2: tuple[int, int] = (2, 2)
stride_conv1: tuple[int, int] = (1, 1)
stride_pool1: tuple[int, int] = (2, 2)
stride_conv2: tuple[int, int] = (1, 1)
stride_pool2: tuple[int, int] = (2, 2)
keras.backend.clear_session()
network = keras.Sequential()
# Conv 1
network.add(
keras.layers.Conv2D(
number_of_output_channels_conv1,
kernel_size=kernel_size_conv1,
activation="relu",
input_shape=(input_dimensions[0], input_dimensions[1], number_of_channels),
padding="valid",
strides=stride_conv1,
data_format="channels_last",
use_bias=True,
)
)
# Pool 1
network.add(
keras.layers.MaxPooling2D(
pool_size=kernel_size_pool1,
padding="valid",
strides=stride_pool1,
data_format="channels_last",
)
)
# Conv 2
network.add(
keras.layers.Conv2D(
number_of_output_channels_conv2,
kernel_size=kernel_size_conv2,
activation="relu",
padding="valid",
strides=stride_conv2,
data_format="channels_last",
use_bias=True,
)
)
# Pool 2
network.add(
keras.layers.MaxPooling2D(
pool_size=kernel_size_pool2,
padding="valid",
strides=stride_pool2,
data_format="channels_last",
)
)
# Flatten
network.add(keras.layers.Flatten(data_format="channels_last"))
# Full layer
network.add(
keras.layers.Dense(number_of_neurons_flatten1, activation="relu", use_bias=True)
)
# Output layer
network.add(keras.layers.Dense(number_of_classes, activation="softmax"))
network.compile(
loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=["accuracy"],
)
for epoch_id in range(0, epoch_max):
print(f"Epoch: {epoch_id} of {epoch_max - 1}")
network.fit(x=train_data)
network.summary()
network.save("Model_" + str(epoch_id) + ".h5")
```
## Test the example network performance
|||
|---|---|
|[tf.keras.models.load_model](https://www.tensorflow.org/api_docs/python/tf/keras/saving/load_model) | "Loads a model saved via model.save()."|
|[network.evaluate()](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#evaluate) | "Returns the loss value & metrics values for the model in test mode."|
[keras_test.py](keras_test.py)
```python
from tensorflow import keras
from DataGenerator import DataGenerator
number_of_classes: int = 10
size_of_batch_test: int = 100
model_id: int = 49
test_data = DataGenerator(
train=False,
size_of_batch=size_of_batch_test,
number_of_classes=number_of_classes,
do_shuffle=False,
)
keras.backend.clear_session()
network = keras.models.load_model("./Model_" + str(model_id) + ".h5")
test_loss, test_acc = network.evaluate(x=test_data)
print(f"Correct: {test_acc * 100.0:.2f}%")
```
## How to extract the activities from the network
```python
from tensorflow import keras
from DataGenerator import DataGenerator
import numpy as np
number_of_classes: int = 10
size_of_batch_test: int = 100
model_id: int = 49
pattern_batch_id: int = 0
pattern_id: int = 42
test_data = DataGenerator(
train=False,
size_of_batch=size_of_batch_test,
number_of_classes=number_of_classes,
do_shuffle=False,
)
keras.backend.clear_session()
network = keras.models.load_model("./Model_" + str(model_id) + ".h5")
image, target = test_data.__getitem__(pattern_batch_id)
the_target = target[pattern_id]
print("Layer 1 (Conv1)")
input_0 = image[pattern_id : pattern_id + 1, :, :, :]
output_0 = network.layers[0](input_0)
print("Input Shape:")
print(input_0.shape)
print("Output Shape:")
print(output_0.numpy().shape)
print("")
print("Layer 2 (Pool1)")
input_1 = output_0
output_1 = network.layers[1](input_1)
print("Input Shape:")
print(input_1.numpy().shape)
print("Output Shape:")
print(output_1.numpy().shape)
print("")
print("Layer 3 (Conv2)")
input_2 = output_1
output_2 = network.layers[2](input_2)
print("Input Shape:")
print(input_2.numpy().shape)
print("Output Shape:")
print(output_2.numpy().shape)
print("")
print("Layer 4 (Pool2)")
input_3 = output_2
output_3 = network.layers[3](input_3)
print("Input Shape:")
print(input_3.numpy().shape)
print("Output Shape:")
print(output_3.numpy().shape)
print("")
print("Layer 5 (Flatten)")
input_4 = output_3
output_4 = network.layers[4](input_4)
print("Input Shape:")
print(input_4.numpy().shape)
print("Output Shape:")
print(output_4.numpy().shape)
print("")
print("Layer 6 (Full)")
input_5 = output_4
output_5 = network.layers[5](input_5)
print("Input Shape:")
print(input_5.numpy().shape)
print("Output Shape:")
print(output_5.numpy().shape)
print("")
print("Layer 7 (Output)")
input_6 = output_5
output_6 = network.layers[6](input_6)
print("Input Shape:")
print(input_6.numpy().shape)
print("Output Shape:")
print(output_6.numpy().shape)
print("")
print("\nEstimation")
print(np.round(output_6.numpy(), 4))
print("Strongest reponse is at " + str(np.argmax(output_6.numpy())))
print("Correct output is " + str(np.argmax(the_target)))
```
## Extracting weight and bias
Here is one way to extract the weights and bias of the whole network. Alternatively you can use [get_weights](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer#get_weights) from [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) in combination with [get_layer](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#get_layer) of [tf.keras.Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential).
```python
from tensorflow import keras
from DataGenerator import DataGenerator
number_of_classes: int = 10
size_of_batch_test: int = 100
model_id: int = 49
pattern_batch_id: int = 0
pattern_id: int = 42
test_data = DataGenerator(
train=False,
size_of_batch=size_of_batch_test,
number_of_classes=number_of_classes,
do_shuffle=False,
)
keras.backend.clear_session()
network = keras.models.load_model("./Model_" + str(model_id) + ".h5")
weights_bias = network.get_weights()
counter_layer: int = 0
for i in range(0, len(weights_bias), 2):
print("Layer " + str(counter_layer) + " weights_bias position: " + str(i) + " =>")
print(weights_bias[i].shape)
counter_layer += 1
print("")
counter_layer = 0
for i in range(1, len(weights_bias), 2):
print("Bias " + str(counter_layer) + " weights_bias position: " + str(i) + " =>")
print(weights_bias[i].shape)
counter_layer += 1
```
## [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.|
|[AveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling1D)| Average pooling for temporal data.|
|[AveragePooling2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D)| Average pooling operation for spatial data.|
|[AveragePooling3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling3D) |Average pooling operation for 3D data (spatial or spatio-temporal).|
|[BatchNormalization](https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization)| Layer that normalizes its inputs.|
|[Conv1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D)| 1D convolution layer (e.g. temporal convolution).|
|[Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D) |2D convolution layer (e.g. spatial convolution over images).|
|[Conv3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv3D)| 3D convolution layer (e.g. spatial convolution over volumes).|
|[Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense)| Just your regular densely-connected NN layer.|
|[Dropout](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout) |Applies Dropout to the input.|
|[Flatten](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Flatten) |Flattens the input. Does not affect the batch size.|
|[MaxPooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPooling1D)| Max pooling operation for 1D temporal data.|
|[MaxPooling2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPooling2D) |Max pooling operation for 2D spatial data.|
|[MaxPooling3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPooling3D) |Max pooling operation for 3D data (spatial or spatio-temporal).|
|[SpatialDropout1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout1D) |Spatial 1D version of Dropout.|
|[SpatialDropout2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout2D) |Spatial 2D version of Dropout.|
|[SpatialDropout3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/SpatialDropout3D) |Spatial 3D version of Dropout.|
|[ZeroPadding1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding1D) |Zero-padding layer for 1D input (e.g. temporal sequence).|
|[ZeroPadding2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding2D) |Zero-padding layer for 2D input (e.g. picture).|
|[ZeroPadding3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/ZeroPadding3D) |Zero-padding layer for 3D data (spatial or spatio-temporal).|
## [Preprocessing layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers#classes_2)
Reduced list with the most relevant preprocessing layers
|||
|---|---|
|[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](https://www.tensorflow.org/api_docs/python/tf/keras/activations)
Reduced list with the most relevant activation functions
|||
|---|---|
|[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](https://www.tensorflow.org/api_docs/python/tf/keras/losses)
Reduced list with the most relevant loss functions
|||
|---|---|
|[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](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)
Reduced list with the most relevant optimizer
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|---|---|
|[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](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)
A very reduced list with the most relevant metrics
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|---|---|
|[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.|
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