diff --git a/bob/learn/tensorflow/losses/balanced_cross_entropy.py b/bob/learn/tensorflow/losses/balanced_cross_entropy.py
index 9a7b879f39282b050abe195372274b07319c78b2..9ca688333ea93d1667b6edd608e7c73b65db4a87 100644
--- a/bob/learn/tensorflow/losses/balanced_cross_entropy.py
+++ b/bob/learn/tensorflow/losses/balanced_cross_entropy.py
@@ -28,7 +28,7 @@ def balanced_softmax_cross_entropy_loss_weights(labels, dtype="float32"):
--------
>>> import numpy
>>> import tensorflow as tf
- >>> from bob.learn.tensorflow.loss import balanced_softmax_cross_entropy_loss_weights
+ >>> from bob.learn.tensorflow.losses import balanced_softmax_cross_entropy_loss_weights
>>> labels = numpy.array([[1, 0, 0],
... [1, 0, 0],
... [0, 0, 1],
@@ -111,13 +111,12 @@ def balanced_sigmoid_cross_entropy_loss_weights(labels, dtype="float32"):
--------
>>> import numpy
>>> import tensorflow as tf
- >>> from bob.learn.tensorflow.loss import balanced_sigmoid_cross_entropy_loss_weights
+ >>> from bob.learn.tensorflow.losses import balanced_sigmoid_cross_entropy_loss_weights
>>> labels = numpy.array([1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
... 1, 1, 0, 1, 1, 1, 0, 1, 0, 1], dtype="int32")
>>> sum(labels), len(labels)
(20, 32)
- >>> session = tf.Session() # Eager execution is also possible check https://www.tensorflow.org/guide/eager
- >>> session.run(balanced_sigmoid_cross_entropy_loss_weights(labels, dtype='float32'))
+ >>> balanced_sigmoid_cross_entropy_loss_weights(labels, dtype='float32').numpy()
array([0.8 , 0.8 , 1.3333334, 1.3333334, 1.3333334, 0.8 ,
0.8 , 1.3333334, 0.8 , 0.8 , 0.8 , 0.8 ,
0.8 , 0.8 , 1.3333334, 0.8 , 1.3333334, 0.8 ,
diff --git a/bob/learn/tensorflow/models/__init__.py b/bob/learn/tensorflow/models/__init__.py
index 6e89a96e2210d7cf9e5409f5654703d9e990ee6c..18a84faeb4c2f66e7929ccb4c39488d23da5a5ff 100644
--- a/bob/learn/tensorflow/models/__init__.py
+++ b/bob/learn/tensorflow/models/__init__.py
@@ -1,5 +1,7 @@
from .alexnet import AlexNet_simplified
+from .densenet import DeepPixBiS
from .densenet import DenseNet
+from .densenet import densenet161 # noqa: F401
from .mine import MineModel
@@ -19,5 +21,5 @@ def __appropriate__(*args):
obj.__module__ = __name__
-__appropriate__(AlexNet_simplified, DenseNet, MineModel)
+__appropriate__(AlexNet_simplified, DenseNet, DeepPixBiS, MineModel)
__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/bob/learn/tensorflow/models/densenet.py b/bob/learn/tensorflow/models/densenet.py
index 9eaf12fc28e174ac77912d1449afef6156963274..e8a72ab6245f0f5d63483e9a7edd0764421b6627 100644
--- a/bob/learn/tensorflow/models/densenet.py
+++ b/bob/learn/tensorflow/models/densenet.py
@@ -180,25 +180,35 @@ class TransitionBlock(tf.keras.Model):
class DenseNet(tf.keras.Model):
"""Creating the Densenet Architecture.
- Arguments:
- depth_of_model: number of layers in the model.
- growth_rate: number of filters to add per conv block.
- num_of_blocks: number of dense blocks.
- output_classes: number of output classes.
- num_layers_in_each_block: number of layers in each block.
- If -1, then we calculate this by (depth-3)/4.
- If positive integer, then the it is used as the
- number of layers per block.
- If list or tuple, then this list is used directly.
- data_format: "channels_first" or "channels_last"
- bottleneck: boolean, to decide which part of conv block to call.
- compression: reducing the number of inputs(filters) to the transition block.
- weight_decay: weight decay
- rate: dropout rate.
- pool_initial: If True add a 7x7 conv with stride 2 followed by 3x3 maxpool
- else, do a 3x3 conv with stride 1.
- include_top: If true, GlobalAveragePooling Layer and Dense layer are
- included.
+ Parameters
+ ----------
+ depth_of_model
+ number of layers in the model.
+ growth_rate
+ number of filters to add per conv block.
+ num_of_blocks
+ number of dense blocks.
+ output_classes
+ number of output classes.
+ num_layers_in_each_block
+ number of layers in each block. If -1, then we calculate this by
+ (depth-3)/4. If positive integer, then the it is used as the number of
+ layers per block. If list or tuple, then this list is used directly.
+ data_format
+ "channels_first" or "channels_last"
+ bottleneck
+ boolean, to decide which part of conv block to call.
+ compression
+ reducing the number of inputs(filters) to the transition block.
+ weight_decay
+ weight decay
+ rate
+ dropout rate.
+ pool_initial
+ If True add a 7x7 conv with stride 2 followed by 3x3 maxpool else, do a
+ 3x3 conv with stride 1.
+ include_top
+ If true, GlobalAveragePooling Layer and Dense layer are included.
"""
def __init__(
diff --git a/bob/learn/tensorflow/utils/math.py b/bob/learn/tensorflow/utils/math.py
index 304d3d71fef9d361b4c2080344568745263a6884..9dc1de40f4c1294f930b036dde1148e1f01e75e5 100644
--- a/bob/learn/tensorflow/utils/math.py
+++ b/bob/learn/tensorflow/utils/math.py
@@ -2,13 +2,13 @@ import tensorflow as tf
def gram_matrix(input_tensor):
- """Computes the gram matrix
+ """Computes the gram matrix.
Parameters
----------
- input_tensor : object
- The input tensor. Usually it's the activation of a conv layer. The input shape
- must be ``BHWC``.
+ input_tensor
+ The input tensor. Usually it's the activation of a conv layer. The input
+ shape must be ``BHWC``.
Returns
-------
@@ -17,15 +17,9 @@ def gram_matrix(input_tensor):
Example
-------
- >>>> gram_matrix(tf.zeros((32, 4, 6, 12)))
- <tf.Tensor: id=53, shape=(32, 12, 12), dtype=float32, numpy=
- array([[[0., 0., 0., ..., 0., 0., 0.],
- [0., 0., 0., ..., 0., 0., 0.],
- [0., 0., 0., ..., 0., 0., 0.],
- ...,
- [0., 0., 0., ..., 0., 0., 0.],
- [0., 0., 0., ..., 0., 0., 0.],
- [0., 0., 0., ..., 0., 0., 0.]],
+ >>> from bob.learn.tensorflow.utils import gram_matrix
+ >>> gram_matrix(tf.zeros((32, 4, 6, 12))).numpy().shape
+ (32, 12, 12)
"""
result = tf.linalg.einsum("bijc,bijd->bcd", input_tensor, input_tensor)
input_shape = tf.shape(input=input_tensor)
@@ -51,13 +45,14 @@ def upper_triangle_and_diagonal(A):
Example
-------
+ >>> from bob.learn.tensorflow.utils import upper_triangle_and_diagonal
>>> A = [
... [1, 2, 3],
... [4, 5, 6],
... [7, 8, 9],
... ]
- >>> upper_triangle_and_diagonal(A)
- [1,2,3,5,6,9]
+ >>> upper_triangle_and_diagonal(A).numpy()
+ array([1, 2, 3, 5, 6, 9], dtype=int32)
"""
ones = tf.ones_like(A)
# Upper triangular matrix of 0s and 1s (including diagonal)
diff --git a/doc/nitpick-exceptions.txt b/doc/nitpick-exceptions.txt
index 4d519b031adaa9f7512abf3328a24be22319d739..a94d2b84194f1f613ce75c09dd5d3f7bb94706ff 100644
--- a/doc/nitpick-exceptions.txt
+++ b/doc/nitpick-exceptions.txt
@@ -4,3 +4,4 @@ py:class tensorflow.python.estimator.estimator.Estimator
py:class tensorflow_estimator.python.estimator.estimator.Estimator
py:class tensorflow.python.keras.losses.Loss
py:class tensorflow.python.keras.engine.base_layer.Layer
+py:class tensorflow.python.keras.engine.training.Model
diff --git a/doc/py_api.rst b/doc/py_api.rst
index 6cf7d2859bb0123af75c09d651f27455c1c6c360..fe498eabc88ecf47a9a62e081c01b66aef239081 100644
--- a/doc/py_api.rst
+++ b/doc/py_api.rst
@@ -10,8 +10,12 @@
Models
======
-.. todo:
- Summary the models
+.. autosummary::
+ bob.learn.tensorflow.models.AlexNet_simplified
+ bob.learn.tensorflow.models.DeepPixBiS
+ bob.learn.tensorflow.models.DenseNet
+ bob.learn.tensorflow.models.densenet161
+ bob.learn.tensorflow.models.MineModel
Data
====
@@ -28,13 +32,54 @@ Losses
.. autosummary::
bob.learn.tensorflow.losses.CenterLossLayer
bob.learn.tensorflow.losses.CenterLoss
+ bob.learn.tensorflow.losses.PixelwiseBinaryCrossentropy
+ bob.learn.tensorflow.losses.balanced_sigmoid_cross_entropy_loss_weights
+ bob.learn.tensorflow.losses.balanced_softmax_cross_entropy_loss_weights
+
+
+Image Utilities
+===============
+
+.. autosummary::
+ bob.learn.tensorflow.utils.image.to_channels_last
+ bob.learn.tensorflow.utils.image.to_channels_first
+ bob.learn.tensorflow.utils.image.blocks_tensorflow
+ bob.learn.tensorflow.utils.image.tf_repeat
+ bob.learn.tensorflow.utils.image.all_patches
+
+
+Keras Utilities
+===============
+
+.. autosummary::
+ bob.learn.tensorflow.utils.keras.SequentialLayer
+ bob.learn.tensorflow.utils.keras.keras_channels_index
+ bob.learn.tensorflow.utils.keras.keras_model_weights_as_initializers_for_variables
+ bob.learn.tensorflow.utils.keras.restore_model_variables_from_checkpoint
+ bob.learn.tensorflow.utils.keras.initialize_model_from_checkpoint
+ bob.learn.tensorflow.utils.keras.model_summary
+Math Utilities
+==============
+
+.. autosummary::
+ bob.learn.tensorflow.utils.math.gram_matrix
+ bob.learn.tensorflow.utils.math.upper_triangle_and_diagonal
+ bob.learn.tensorflow.utils.math.upper_triangle
+ bob.learn.tensorflow.utils.math.pdist
+ bob.learn.tensorflow.utils.math.cdist
+ bob.learn.tensorflow.utils.math.random_choice_no_replacement
+
Detailed Information
====================
.. automodule:: bob.learn.tensorflow
.. automodule:: bob.learn.tensorflow.data
-.. automodule:: bob.learn.tensorflow.utils
.. automodule:: bob.learn.tensorflow.losses
+.. automodule:: bob.learn.tensorflow.models
+.. automodule:: bob.learn.tensorflow.utils
+.. automodule:: bob.learn.tensorflow.utils.image
+.. automodule:: bob.learn.tensorflow.utils.keras
+.. automodule:: bob.learn.tensorflow.utils.math