nn
bench ([label, verbose, extra_argv])
|
Run benchmarks for module using nose. |
test ([label, verbose, extra_argv, doctests, ...])
|
Run tests for module using nose. |
Module: nn.histo_resdnn
Class and helper functions for fitting the Histological ResDNN model.
Add (*args, **kwargs)
|
Layer that adds a list of inputs. |
Dense (*args, **kwargs)
|
Just your regular densely-connected NN layer. |
HemiSphere ([x, y, z, theta, phi, xyz, ...])
|
Points on the unit sphere. |
HistoResDNN ([sh_order, basis_type, verbose])
|
This class is intended for the ResDNN Histology Network model. |
Model (*args, **kwargs)
|
Model groups layers into an object with training and inference features. |
Version (version)
|
- Attributes
|
Input ([shape, batch_size, name, dtype, ...])
|
Input() is used to instantiate a Keras tensor. |
doctest_skip_parser (func)
|
Decorator replaces custom skip test markup in doctests. |
get_bval_indices (bvals, bval[, tol])
|
Get indices where the b-value is bval |
get_fnames ([name])
|
Provide full paths to example or test datasets. |
get_sphere ([name])
|
provide triangulated spheres |
optional_package (name[, trip_msg])
|
Return package-like thing and module setup for package name |
set_logger_level (log_level)
|
Change the logger of the HistoResDNN to one on the following: DEBUG, INFO, WARNING, CRITICAL, ERROR |
sf_to_sh (sf, sphere[, sh_order, basis_type, ...])
|
Spherical function to spherical harmonics (SH). |
sh_to_sf (sh, sphere[, sh_order, basis_type, ...])
|
Spherical harmonics (SH) to spherical function (SF). |
sph_harm_ind_list (sh_order[, full_basis])
|
Returns the degree (m ) and order (n ) of all the symmetric spherical harmonics of degree less then or equal to sh_order . |
unique_bvals_magnitude (bvals[, bmag, rbvals])
|
This function gives the unique rounded b-values of the data |
Module: nn.model
bench
-
dipy.nn.bench(label='fast', verbose=1, extra_argv=None)
Run benchmarks for module using nose.
- Parameters
- label{‘fast’, ‘full’, ‘’, attribute identifier}, optional
Identifies the benchmarks to run. This can be a string to pass to
the nosetests executable with the ‘-A’ option, or one of several
special values. Special values are:
‘fast’ - the default - which corresponds to the nosetests -A
option of ‘not slow’.
‘full’ - fast (as above) and slow benchmarks as in the
‘no -A’ option to nosetests - this is the same as ‘’.
None or ‘’ - run all tests.
attribute_identifier - string passed directly to nosetests as ‘-A’.
- verboseint, optional
Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
- extra_argvlist, optional
List with any extra arguments to pass to nosetests.
- Returns
- successbool
Returns True if running the benchmarks works, False if an error
occurred.
Notes
Benchmarks are like tests, but have names starting with “bench” instead
of “test”, and can be found under the “benchmarks” sub-directory of the
module.
Each NumPy module exposes bench in its namespace to run all benchmarks
for it.
Examples
>>> success = np.lib.bench()
Running benchmarks for numpy.lib
...
using 562341 items:
unique:
0.11
unique1d:
0.11
ratio: 1.0
nUnique: 56230 == 56230
...
OK
test
-
dipy.nn.test(label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None, timer=False)
Run tests for module using nose.
- Parameters
- label{‘fast’, ‘full’, ‘’, attribute identifier}, optional
Identifies the tests to run. This can be a string to pass to
the nosetests executable with the ‘-A’ option, or one of several
special values. Special values are:
‘fast’ - the default - which corresponds to the nosetests -A
option of ‘not slow’.
‘full’ - fast (as above) and slow tests as in the
‘no -A’ option to nosetests - this is the same as ‘’.
None or ‘’ - run all tests.
attribute_identifier - string passed directly to nosetests as ‘-A’.
- verboseint, optional
Verbosity value for test outputs, in the range 1-10. Default is 1.
- extra_argvlist, optional
List with any extra arguments to pass to nosetests.
- doctestsbool, optional
If True, run doctests in module. Default is False.
- coveragebool, optional
If True, report coverage of NumPy code. Default is False.
(This requires the
coverage module).
- raise_warningsNone, str or sequence of warnings, optional
This specifies which warnings to configure as ‘raise’ instead
of being shown once during the test execution. Valid strings are:
“develop” : equals (Warning,)
“release” : equals ()
, do not raise on any warnings.
- timerbool or int, optional
Timing of individual tests with nose-timer
(which needs to be
installed). If True, time tests and report on all of them.
If an integer (say N
), report timing results for N
slowest
tests.
- Returns
- resultobject
Returns the result of running the tests as a
nose.result.TextTestResult
object.
Notes
Each NumPy module exposes test in its namespace to run all tests for it.
For example, to run all tests for numpy.lib:
Examples
>>> result = np.lib.test()
Running unit tests for numpy.lib
...
Ran 976 tests in 3.933s
OK
>>> result.errors
[]
>>> result.knownfail
[]
-
class dipy.nn.histo_resdnn.Add(*args, **kwargs)
Bases: keras.layers.merge._Merge
Layer that adds a list of inputs.
It takes as input a list of tensors,
all of the same shape, and returns
a single tensor (also of the same shape).
Examples:
>>> input_shape = (2, 3, 4)
>>> x1 = tf.random.normal(input_shape)
>>> x2 = tf.random.normal(input_shape)
>>> y = tf.keras.layers.Add()([x1, x2])
>>> print(y.shape)
(2, 3, 4)
Used in a functional model:
>>> input1 = tf.keras.layers.Input(shape=(16,))
>>> x1 = tf.keras.layers.Dense(8, activation='relu')(input1)
>>> input2 = tf.keras.layers.Input(shape=(32,))
>>> x2 = tf.keras.layers.Dense(8, activation='relu')(input2)
>>> # equivalent to `added = tf.keras.layers.add([x1, x2])`
>>> added = tf.keras.layers.Add()([x1, x2])
>>> out = tf.keras.layers.Dense(4)(added)
>>> model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)
- Attributes
activity_regularizer
Optional regularizer function for the output of this layer.
compute_dtype
The dtype of the layer’s computations.
dtype
The dtype of the layer weights.
dtype_policy
The dtype policy associated with this layer.
dynamic
Whether the layer is dynamic (eager-only); set in the constructor.
inbound_nodes
Deprecated, do NOT use! Only for compatibility with external Keras.
input
Retrieves the input tensor(s) of a layer.
input_mask
Retrieves the input mask tensor(s) of a layer.
input_shape
Retrieves the input shape(s) of a layer.
input_spec
InputSpec instance(s) describing the input format for this layer.
losses
List of losses added using the add_loss() API.
metrics
List of metrics added using the add_metric() API.
name
Name of the layer (string), set in the constructor.
name_scope
Returns a tf.name_scope instance for this class.
non_trainable_variables
Sequence of non-trainable variables owned by this module and its submodules.
non_trainable_weights
List of all non-trainable weights tracked by this layer.
outbound_nodes
Deprecated, do NOT use! Only for compatibility with external Keras.
output
Retrieves the output tensor(s) of a layer.
output_mask
Retrieves the output mask tensor(s) of a layer.
output_shape
Retrieves the output shape(s) of a layer.
- stateful
submodules
Sequence of all sub-modules.
supports_masking
Whether this layer supports computing a mask using compute_mask.
- trainable
trainable_variables
Sequence of trainable variables owned by this module and its submodules.
trainable_weights
List of all trainable weights tracked by this layer.
- updates
variable_dtype
Alias of Layer.dtype, the dtype of the weights.
variables
Returns the list of all layer variables/weights.
weights
Returns the list of all layer variables/weights.
Methods
__call__ (*args, **kwargs)
|
Wraps call, applying pre- and post-processing steps. |
add_loss (losses, **kwargs)
|
Add loss tensor(s), potentially dependent on layer inputs. |
add_metric (value[, name])
|
Adds metric tensor to the layer. |
add_update (updates[, inputs])
|
Add update op(s), potentially dependent on layer inputs. |
add_variable (*args, **kwargs)
|
Deprecated, do NOT use! Alias for add_weight. |
add_weight ([name, shape, dtype, ...])
|
Adds a new variable to the layer. |
apply (inputs, *args, **kwargs)
|
Deprecated, do NOT use! |
call (inputs)
|
This is where the layer's logic lives. |
compute_mask (inputs[, mask])
|
Computes an output mask tensor. |
compute_output_signature (input_signature)
|
Compute the output tensor signature of the layer based on the inputs. |
count_params ()
|
Count the total number of scalars composing the weights. |
finalize_state ()
|
Finalizes the layers state after updating layer weights. |
from_config (config)
|
Creates a layer from its config. |
get_config ()
|
Returns the config of the layer. |
get_input_at (node_index)
|
Retrieves the input tensor(s) of a layer at a given node. |
get_input_mask_at (node_index)
|
Retrieves the input mask tensor(s) of a layer at a given node. |
get_input_shape_at (node_index)
|
Retrieves the input shape(s) of a layer at a given node. |
get_losses_for (inputs)
|
Deprecated, do NOT use! |
get_output_at (node_index)
|
Retrieves the output tensor(s) of a layer at a given node. |
get_output_mask_at (node_index)
|
Retrieves the output mask tensor(s) of a layer at a given node. |
get_output_shape_at (node_index)
|
Retrieves the output shape(s) of a layer at a given node. |
get_updates_for (inputs)
|
Deprecated, do NOT use! |
get_weights ()
|
Returns the current weights of the layer, as NumPy arrays. |
set_weights (weights)
|
Sets the weights of the layer, from NumPy arrays. |
with_name_scope (method)
|
Decorator to automatically enter the module name scope. |
build |
|
compute_output_shape |
|
-
__init__(**kwargs)
Initializes a Merge layer.
- Args:
**kwargs: standard layer keyword arguments.
-
class dipy.nn.histo_resdnn.Dense(*args, **kwargs)
Bases: keras.engine.base_layer.Layer
Just your regular densely-connected NN layer.
Dense implements the operation:
output = activation(dot(input, kernel) + bias)
where activation is the element-wise activation function
passed as the activation argument, kernel is a weights matrix
created by the layer, and bias is a bias vector created by the layer
(only applicable if use_bias is True). These are all attributes of
Dense.
Note: If the input to the layer has a rank greater than 2, then Dense
computes the dot product between the inputs and the kernel along the
last axis of the inputs and axis 0 of the kernel (using tf.tensordot).
For example, if input has dimensions (batch_size, d0, d1),
then we create a kernel with shape (d1, units), and the kernel operates
along axis 2 of the input, on every sub-tensor of shape (1, 1, d1)
(there are batch_size * d0 such sub-tensors).
The output in this case will have shape (batch_size, d0, units).
Besides, layer attributes cannot be modified after the layer has been called
once (except the trainable attribute).
When a popular kwarg input_shape is passed, then keras will create
an input layer to insert before the current layer. This can be treated
equivalent to explicitly defining an InputLayer.
Example:
>>> # Create a `Sequential` model and add a Dense layer as the first layer.
>>> model = tf.keras.models.Sequential()
>>> model.add(tf.keras.Input(shape=(16,)))
>>> model.add(tf.keras.layers.Dense(32, activation='relu'))
>>> # Now the model will take as input arrays of shape (None, 16)
>>> # and output arrays of shape (None, 32).
>>> # Note that after the first layer, you don't need to specify
>>> # the size of the input anymore:
>>> model.add(tf.keras.layers.Dense(32))
>>> model.output_shape
(None, 32)
- Args:
units: Positive integer, dimensionality of the output space.
activation: Activation function to use.
If you don’t specify anything, no activation is applied
(ie. “linear” activation: a(x) = x).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the kernel weights matrix.
bias_initializer: Initializer for the bias vector.
kernel_regularizer: Regularizer function applied to
the kernel weights matrix.
bias_regularizer: Regularizer function applied to the bias vector.
activity_regularizer: Regularizer function applied to
the output of the layer (its “activation”).
- kernel_constraint: Constraint function applied to
the kernel weights matrix.
bias_constraint: Constraint function applied to the bias vector.
- Input shape:
N-D tensor with shape: (batch_size, …, input_dim).
The most common situation would be
a 2D input with shape (batch_size, input_dim).
- Output shape:
N-D tensor with shape: (batch_size, …, units).
For instance, for a 2D input with shape (batch_size, input_dim),
the output would have shape (batch_size, units).
- Attributes
activity_regularizer
Optional regularizer function for the output of this layer.
compute_dtype
The dtype of the layer’s computations.
dtype
The dtype of the layer weights.
dtype_policy
The dtype policy associated with this layer.
dynamic
Whether the layer is dynamic (eager-only); set in the constructor.
inbound_nodes
Deprecated, do NOT use! Only for compatibility with external Keras.
input
Retrieves the input tensor(s) of a layer.
input_mask
Retrieves the input mask tensor(s) of a layer.
input_shape
Retrieves the input shape(s) of a layer.
input_spec
InputSpec instance(s) describing the input format for this layer.
losses
List of losses added using the add_loss() API.
metrics
List of metrics added using the add_metric() API.
name
Name of the layer (string), set in the constructor.
name_scope
Returns a tf.name_scope instance for this class.
non_trainable_variables
Sequence of non-trainable variables owned by this module and its submodules.
non_trainable_weights
List of all non-trainable weights tracked by this layer.
outbound_nodes
Deprecated, do NOT use! Only for compatibility with external Keras.
output
Retrieves the output tensor(s) of a layer.
output_mask
Retrieves the output mask tensor(s) of a layer.
output_shape
Retrieves the output shape(s) of a layer.
- stateful
submodules
Sequence of all sub-modules.
supports_masking
Whether this layer supports computing a mask using compute_mask.
- trainable
trainable_variables
Sequence of trainable variables owned by this module and its submodules.
trainable_weights
List of all trainable weights tracked by this layer.
- updates
variable_dtype
Alias of Layer.dtype, the dtype of the weights.
variables
Returns the list of all layer variables/weights.
weights
Returns the list of all layer variables/weights.
Methods
__call__ (*args, **kwargs)
|
Wraps call, applying pre- and post-processing steps. |
add_loss (losses, **kwargs)
|
Add loss tensor(s), potentially dependent on layer inputs. |
add_metric (value[, name])
|
Adds metric tensor to the layer. |
add_update (updates[, inputs])
|
Add update op(s), potentially dependent on layer inputs. |
add_variable (*args, **kwargs)
|
Deprecated, do NOT use! Alias for add_weight. |
add_weight ([name, shape, dtype, ...])
|
Adds a new variable to the layer. |
apply (inputs, *args, **kwargs)
|
Deprecated, do NOT use! |
build (input_shape)
|
Creates the variables of the layer (optional, for subclass implementers). |
call (inputs)
|
This is where the layer's logic lives. |
compute_mask (inputs[, mask])
|
Computes an output mask tensor. |
compute_output_shape (input_shape)
|
Computes the output shape of the layer. |
compute_output_signature (input_signature)
|
Compute the output tensor signature of the layer based on the inputs. |
count_params ()
|
Count the total number of scalars composing the weights. |
finalize_state ()
|
Finalizes the layers state after updating layer weights. |
from_config (config)
|
Creates a layer from its config. |
get_config ()
|
Returns the config of the layer. |
get_input_at (node_index)
|
Retrieves the input tensor(s) of a layer at a given node. |
get_input_mask_at (node_index)
|
Retrieves the input mask tensor(s) of a layer at a given node. |
get_input_shape_at (node_index)
|
Retrieves the input shape(s) of a layer at a given node. |
get_losses_for (inputs)
|
Deprecated, do NOT use! |
get_output_at (node_index)
|
Retrieves the output tensor(s) of a layer at a given node. |
get_output_mask_at (node_index)
|
Retrieves the output mask tensor(s) of a layer at a given node. |
get_output_shape_at (node_index)
|
Retrieves the output shape(s) of a layer at a given node. |
get_updates_for (inputs)
|
Deprecated, do NOT use! |
get_weights ()
|
Returns the current weights of the layer, as NumPy arrays. |
set_weights (weights)
|
Sets the weights of the layer, from NumPy arrays. |
with_name_scope (method)
|
Decorator to automatically enter the module name scope. |
-
__init__(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs)
-
build(input_shape)
Creates the variables of the layer (optional, for subclass implementers).
This is a method that implementers of subclasses of Layer or Model
can override if they need a state-creation step in-between
layer instantiation and layer call. It is invoked automatically before
the first execution of call().
This is typically used to create the weights of Layer subclasses
(at the discretion of the subclass implementer).
- Args:
- input_shape: Instance of TensorShape, or list of instances of
TensorShape if the layer expects a list of inputs
(one instance per input).
-
call(inputs)
This is where the layer’s logic lives.
The call() method may not create state (except in its first invocation,
wrapping the creation of variables or other resources in tf.init_scope()).
It is recommended to create state in __init__(), or the build() method
that is called automatically before call() executes the first time.
- Args:
- inputs: Input tensor, or dict/list/tuple of input tensors.
The first positional inputs argument is subject to special rules:
- inputs must be explicitly passed. A layer cannot have zero
arguments, and inputs cannot be provided via the default value
of a keyword argument.
NumPy array or Python scalar values in inputs get cast as tensors.
Keras mask metadata is only collected from inputs.
Layers are built (build(input_shape) method)
using shape info from inputs only.
input_spec compatibility is only checked against inputs.
Mixed precision input casting is only applied to inputs.
If a layer has tensor arguments in *args or **kwargs, their
casting behavior in mixed precision should be handled manually.
The SavedModel input specification is generated using inputs only.
Integration with various ecosystem packages like TFMOT, TFLite,
TF.js, etc is only supported for inputs and not for tensors in
positional and keyword arguments.
- *args: Additional positional arguments. May contain tensors, although
this is not recommended, for the reasons above.
- **kwargs: Additional keyword arguments. May contain tensors, although
this is not recommended, for the reasons above.
The following optional keyword arguments are reserved:
- training: Boolean scalar tensor of Python boolean indicating
whether the call is meant for training or inference.
mask: Boolean input mask. If the layer’s call() method takes a
mask argument, its default value will be set to the mask generated
for inputs by the previous layer (if input did come from a layer
that generated a corresponding mask, i.e. if it came from a Keras
layer with masking support).
- Returns:
A tensor or list/tuple of tensors.
-
compute_output_shape(input_shape)
Computes the output shape of the layer.
This method will cause the layer’s state to be built, if that has not
happened before. This requires that the layer will later be used with
inputs that match the input shape provided here.
- Args:
- input_shape: Shape tuple (tuple of integers)
or list of shape tuples (one per output tensor of the layer).
Shape tuples can include None for free dimensions,
instead of an integer.
- Returns:
An input shape tuple.
-
get_config()
Returns the config of the layer.
A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.
The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by Network (one layer of abstraction above).
Note that get_config() does not guarantee to return a fresh copy of dict
every time it is called. The callers should make a copy of the returned dict
if they want to modify it.
- Returns:
Python dictionary.
-
class dipy.nn.histo_resdnn.HemiSphere(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None, tol=1e-05)
Bases: dipy.core.sphere.Sphere
Points on the unit sphere.
A HemiSphere is similar to a Sphere but it takes antipodal symmetry into
account. Antipodal symmetry means that point v on a HemiSphere is the same
as the point -v. Duplicate points are discarded when constructing a
HemiSphere (including antipodal duplicates). edges and faces are
remapped to the remaining points as closely as possible.
The HemiSphere can be constructed using one of three conventions:
HemiSphere(x, y, z)
HemiSphere(xyz=xyz)
HemiSphere(theta=theta, phi=phi)
- Parameters
- x, y, z1-D array_like
Vertices as x-y-z coordinates.
- theta, phi1-D array_like
Vertices as spherical coordinates. Theta and phi are the inclination
and azimuth angles respectively.
- xyz(N, 3) ndarray
Vertices as x-y-z coordinates.
- faces(N, 3) ndarray
Indices into vertices that form triangular faces. If unspecified,
the faces are computed using a Delaunay triangulation.
- edges(N, 2) ndarray
Edges between vertices. If unspecified, the edges are
derived from the faces.
- tolfloat
Angle in degrees. Vertices that are less than tol degrees apart are
treated as duplicates.
- Attributes
- x
- y
- z
Methods
find_closest (xyz)
|
Find the index of the vertex in the Sphere closest to the input vector, taking into account antipodal symmetry |
from_sphere (sphere[, tol])
|
Create instance from a Sphere |
mirror ()
|
Create a full Sphere from a HemiSphere |
subdivide ([n])
|
Create a more subdivided HemiSphere |
-
__init__(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None, tol=1e-05)
Create a HemiSphere from points
-
faces()
-
find_closest(xyz)
Find the index of the vertex in the Sphere closest to the input vector,
taking into account antipodal symmetry
- Parameters
- xyzarray-like, 3 elements
A unit vector
- Returns
- idxint
The index into the Sphere.vertices array that gives the closest
vertex (in angle).
-
classmethod from_sphere(sphere, tol=1e-05)
Create instance from a Sphere
-
mirror()
Create a full Sphere from a HemiSphere
-
subdivide(n=1)
Create a more subdivided HemiSphere
See Sphere.subdivide for full documentation.
-
class dipy.nn.histo_resdnn.HistoResDNN(sh_order=8, basis_type='tournier07', verbose=False)
Bases: object
This class is intended for the ResDNN Histology Network model.
Methods
fetch_default_weights ()
|
Load the model pre-training weights to use for the fitting. |
load_model_weights (weights_path)
|
Load the custom pre-training weights to use for the fitting. |
predict (data, gtab[, mask, chunk_size])
|
Wrapper function to faciliate prediction of larger dataset. |
-
__init__(sh_order=8, basis_type='tournier07', verbose=False)
The model was re-trained for usage with a different basis function
(‘tournier07’) like the proposed model in [1, 2].
To obtain the pre-trained model, use::
>>> resdnn_model = HistoResDNN()
>>> fetch_model_weights_path = get_fnames(‘histo_resdnn_weights’)
>>> resdnn_model.load_model_weights(fetch_model_weights_path)
This model is designed to take as input raw DWI signal on a sphere
(ODF) represented as SH of order 8 in the tournier basis and predict
fODF of order 8 in the tournier basis. Effectively, this model is
mimicking a CSD fit.
- Parameters
- sh_orderint, optional
Maximum SH order in the SH fit. For sh_order
, there will be
(sh_order + 1) * (sh_order + 2) / 2
SH coefficients for a
symmetric basis. Default: 8
- basis_type{‘tournier07’, ‘descoteaux07’}, optional
tournier07
(default) or descoteaux07
.
- verbosebool (optional)
Whether to show information about the processing.
Default: False
References
-
fetch_default_weights()
Load the model pre-training weights to use for the fitting.
Will not work if the declared SH_ORDER does not match the weights
expected input.
-
load_model_weights(weights_path)
Load the custom pre-training weights to use for the fitting.
Will not work if the declared SH_ORDER does not match the weights
expected input.
- The weights for a sh_order of 8 can be obtained via the function:
get_fnames(‘histo_resdnn_weights’).
- Parameters
- weights_pathstr
Path to the file containing the weights (hdf5, saved by tensorflow)
-
predict(data, gtab, mask=None, chunk_size=1000)
Wrapper function to faciliate prediction of larger dataset.
The function will mask, normalize, split, predict and ‘re-assemble’
the data as a volume.
- Parameters
- datanp.ndarray
DWI signal in a 4D array
- gtabGradientTable class instance
The acquisition scheme matching the data (must contain at least
one b0)
- masknp.ndarray (optional)
Binary mask of the brain to avoid unnecessary computation and
unreliable prediction outside the brain.
Default: Compute prediction only for nonzero voxels (with at least
one nonzero DWI value).
- Returns
- pred_sh_coefnp.ndarray (x, y, z, M)
Predicted fODF (as SH). The volume has matching shape to the input
data, but with (sh_order + 1) * (sh_order + 2) / 2
as a last
dimension.
-
class dipy.nn.histo_resdnn.Model(*args, **kwargs)
Bases: keras.engine.base_layer.Layer
, keras.utils.version_utils.ModelVersionSelector
Model groups layers into an object with training and inference features.
- Args:
- inputs: The input(s) of the model: a keras.Input object or list of
keras.Input objects.
outputs: The output(s) of the model. See Functional API example below.
name: String, the name of the model.
There are two ways to instantiate a Model:
1 - With the “Functional API”, where you start from Input,
you chain layer calls to specify the model’s forward pass,
and finally you create your model from inputs and outputs:
```python
import tensorflow as tf
inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
```
Note: Only dicts, lists, and tuples of input tensors are supported. Nested
inputs are not supported (e.g. lists of list or dicts of dict).
A new Functional API model can also be created by using the
intermediate tensors. This enables you to quickly extract sub-components
of the model.
Example:
```python
inputs = keras.Input(shape=(None, None, 3))
processed = keras.layers.RandomCrop(width=32, height=32)(inputs)
conv = keras.layers.Conv2D(filters=2, kernel_size=3)(processed)
pooling = keras.layers.GlobalAveragePooling2D()(conv)
feature = keras.layers.Dense(10)(pooling)
full_model = keras.Model(inputs, feature)
backbone = keras.Model(processed, conv)
activations = keras.Model(conv, feature)
```
Note that the backbone and activations models are not
created with keras.Input objects, but with the tensors that are originated
from keras.Inputs objects. Under the hood, the layers and weights will
be shared across these models, so that user can train the full_model, and
use backbone or activations to do feature extraction.
The inputs and outputs of the model can be nested structures of tensors as
well, and the created models are standard Functional API models that support
all the existing APIs.
2 - By subclassing the Model class: in that case, you should define your
layers in __init__() and you should implement the model’s forward pass
in call().
```python
import tensorflow as tf
class MyModel(tf.keras.Model):
- def __init__(self):
super().__init__()
self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
- def call(self, inputs):
x = self.dense1(inputs)
return self.dense2(x)
model = MyModel()
```
If you subclass Model, you can optionally have
a training argument (boolean) in call(), which you can use to specify
a different behavior in training and inference:
```python
import tensorflow as tf
class MyModel(tf.keras.Model):
- def __init__(self):
super().__init__()
self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
self.dropout = tf.keras.layers.Dropout(0.5)
- def call(self, inputs, training=False):
x = self.dense1(inputs)
if training:
x = self.dropout(x, training=training)
return self.dense2(x)
model = MyModel()
```
Once the model is created, you can config the model with losses and metrics
with model.compile(), train the model with model.fit(), or use the model
to do prediction with model.predict().
- Attributes
activity_regularizer
Optional regularizer function for the output of this layer.
compute_dtype
The dtype of the layer’s computations.
distribute_strategy
The tf.distribute.Strategy this model was created under.
dtype
The dtype of the layer weights.
dtype_policy
The dtype policy associated with this layer.
dynamic
Whether the layer is dynamic (eager-only); set in the constructor.
inbound_nodes
Deprecated, do NOT use! Only for compatibility with external Keras.
input
Retrieves the input tensor(s) of a layer.
input_mask
Retrieves the input mask tensor(s) of a layer.
input_shape
Retrieves the input shape(s) of a layer.
input_spec
InputSpec instance(s) describing the input format for this layer.
- layers
losses
List of losses added using the add_loss() API.
metrics
Returns the model’s metrics added using compile(), add_metric() APIs.
metrics_names
Returns the model’s display labels for all outputs.
name
Name of the layer (string), set in the constructor.
name_scope
Returns a tf.name_scope instance for this class.
non_trainable_variables
Sequence of non-trainable variables owned by this module and its submodules.
non_trainable_weights
List of all non-trainable weights tracked by this layer.
outbound_nodes
Deprecated, do NOT use! Only for compatibility with external Keras.
output
Retrieves the output tensor(s) of a layer.
output_mask
Retrieves the output mask tensor(s) of a layer.
output_shape
Retrieves the output shape(s) of a layer.
run_eagerly
Settable attribute indicating whether the model should run eagerly.
state_updates
Deprecated, do NOT use!
- stateful
submodules
Sequence of all sub-modules.
supports_masking
Whether this layer supports computing a mask using compute_mask.
- trainable
trainable_variables
Sequence of trainable variables owned by this module and its submodules.
trainable_weights
List of all trainable weights tracked by this layer.
- updates
variable_dtype
Alias of Layer.dtype, the dtype of the weights.
variables
Returns the list of all layer variables/weights.
weights
Returns the list of all layer variables/weights.
Methods
__call__ (*args, **kwargs)
|
Wraps call, applying pre- and post-processing steps. |
add_loss (losses, **kwargs)
|
Add loss tensor(s), potentially dependent on layer inputs. |
add_metric (value[, name])
|
Adds metric tensor to the layer. |
add_update (updates[, inputs])
|
Add update op(s), potentially dependent on layer inputs. |
add_variable (*args, **kwargs)
|
Deprecated, do NOT use! Alias for add_weight. |
add_weight ([name, shape, dtype, ...])
|
Adds a new variable to the layer. |
apply (inputs, *args, **kwargs)
|
Deprecated, do NOT use! |
build (input_shape)
|
Builds the model based on input shapes received. |
call (inputs[, training, mask])
|
Calls the model on new inputs and returns the outputs as tensors. |
compile ([optimizer, loss, metrics, ...])
|
Configures the model for training. |
compute_loss ([x, y, y_pred, sample_weight])
|
Compute the total loss, validate it, and return it. |
compute_mask (inputs[, mask])
|
Computes an output mask tensor. |
compute_metrics (x, y, y_pred, sample_weight)
|
Update metric states and collect all metrics to be returned. |
compute_output_shape (input_shape)
|
Computes the output shape of the layer. |
compute_output_signature (input_signature)
|
Compute the output tensor signature of the layer based on the inputs. |
count_params ()
|
Count the total number of scalars composing the weights. |
evaluate ([x, y, batch_size, verbose, ...])
|
Returns the loss value & metrics values for the model in test mode. |
evaluate_generator (generator[, steps, ...])
|
Evaluates the model on a data generator. |
finalize_state ()
|
Finalizes the layers state after updating layer weights. |
fit ([x, y, batch_size, epochs, verbose, ...])
|
Trains the model for a fixed number of epochs (iterations on a dataset). |
fit_generator (generator[, steps_per_epoch, ...])
|
Fits the model on data yielded batch-by-batch by a Python generator. |
from_config (config[, custom_objects])
|
Creates a layer from its config. |
get_config ()
|
Returns the config of the layer. |
get_input_at (node_index)
|
Retrieves the input tensor(s) of a layer at a given node. |
get_input_mask_at (node_index)
|
Retrieves the input mask tensor(s) of a layer at a given node. |
get_input_shape_at (node_index)
|
Retrieves the input shape(s) of a layer at a given node. |
get_layer ([name, index])
|
Retrieves a layer based on either its name (unique) or index. |
get_losses_for (inputs)
|
Deprecated, do NOT use! |
get_output_at (node_index)
|
Retrieves the output tensor(s) of a layer at a given node. |
get_output_mask_at (node_index)
|
Retrieves the output mask tensor(s) of a layer at a given node. |
get_output_shape_at (node_index)
|
Retrieves the output shape(s) of a layer at a given node. |
get_updates_for (inputs)
|
Deprecated, do NOT use! |
get_weights ()
|
Retrieves the weights of the model. |
load_weights (filepath[, by_name, ...])
|
Loads all layer weights, either from a TensorFlow or an HDF5 weight file. |
make_predict_function ([force])
|
Creates a function that executes one step of inference. |
make_test_function ([force])
|
Creates a function that executes one step of evaluation. |
make_train_function ([force])
|
Creates a function that executes one step of training. |
predict (x[, batch_size, verbose, steps, ...])
|
Generates output predictions for the input samples. |
predict_generator (generator[, steps, ...])
|
Generates predictions for the input samples from a data generator. |
predict_on_batch (x)
|
Returns predictions for a single batch of samples. |
predict_step (data)
|
The logic for one inference step. |
reset_metrics ()
|
Resets the state of all the metrics in the model. |
save (filepath[, overwrite, ...])
|
Saves the model to Tensorflow SavedModel or a single HDF5 file. |
save_spec ([dynamic_batch])
|
Returns the tf.TensorSpec of call inputs as a tuple (args, kwargs). |
save_weights (filepath[, overwrite, ...])
|
Saves all layer weights. |
set_weights (weights)
|
Sets the weights of the layer, from NumPy arrays. |
summary ([line_length, positions, print_fn, ...])
|
Prints a string summary of the network. |
test_on_batch (x[, y, sample_weight, ...])
|
Test the model on a single batch of samples. |
test_step (data)
|
The logic for one evaluation step. |
to_json (**kwargs)
|
Returns a JSON string containing the network configuration. |
to_yaml (**kwargs)
|
Returns a yaml string containing the network configuration. |
train_on_batch (x[, y, sample_weight, ...])
|
Runs a single gradient update on a single batch of data. |
train_step (data)
|
The logic for one training step. |
with_name_scope (method)
|
Decorator to automatically enter the module name scope. |
-
__init__(*args, **kwargs)
-
build(input_shape)
Builds the model based on input shapes received.
This is to be used for subclassed models, which do not know at instantiation
time what their inputs look like.
This method only exists for users who want to call model.build() in a
standalone way (as a substitute for calling the model on real data to
build it). It will never be called by the framework (and thus it will
never throw unexpected errors in an unrelated workflow).
- Args:
- input_shape: Single tuple, TensorShape instance, or list/dict of shapes,
where shapes are tuples, integers, or TensorShape instances.
- Raises:
- ValueError:
In case of invalid user-provided data (not of type tuple,
list, TensorShape, or dict).
If the model requires call arguments that are agnostic
to the input shapes (positional or keyword arg in call signature).
If not all layers were properly built.
If float type inputs are not supported within the layers.
In each of these cases, the user should build their model by calling it
on real tensor data.
-
call(inputs, training=None, mask=None)
Calls the model on new inputs and returns the outputs as tensors.
In this case call() just reapplies
all ops in the graph to the new inputs
(e.g. build a new computational graph from the provided inputs).
Note: This method should not be called directly. It is only meant to be
overridden when subclassing tf.keras.Model.
To call a model on an input, always use the __call__() method,
i.e. model(inputs), which relies on the underlying call() method.
- Args:
inputs: Input tensor, or dict/list/tuple of input tensors.
training: Boolean or boolean scalar tensor, indicating whether to run
the Network in training mode or inference mode.
- mask: A mask or list of masks. A mask can be either a boolean tensor or
- None (no mask). For more details, check the guide
[here](https://www.tensorflow.org/guide/keras/masking_and_padding).
- Returns:
A tensor if there is a single output, or
a list of tensors if there are more than one outputs.
-
compile(optimizer='rmsprop', loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, steps_per_execution=None, jit_compile=None, **kwargs)
Configures the model for training.
Example:
```python
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=[tf.keras.metrics.BinaryAccuracy(),
tf.keras.metrics.FalseNegatives()])
```
- Args:
- optimizer: String (name of optimizer) or optimizer instance. See
tf.keras.optimizers.
- loss: Loss function. Maybe be a string (name of loss function), or
a tf.keras.losses.Loss instance. See tf.keras.losses. A loss
function is any callable with the signature loss = fn(y_true,
y_pred), where y_true are the ground truth values, and
y_pred are the model’s predictions.
y_true should have shape
(batch_size, d0, .. dN) (except in the case of
sparse loss functions such as
sparse categorical crossentropy which expects integer arrays of shape
(batch_size, d0, .. dN-1)).
y_pred should have shape (batch_size, d0, .. dN).
The loss function should return a float tensor.
If a custom Loss instance is
used and reduction is set to None, return value has shape
(batch_size, d0, .. dN-1) i.e. per-sample or per-timestep loss
values; otherwise, it is a scalar. If the model has multiple outputs,
you can use a different loss on each output by passing a dictionary
or a list of losses. The loss value that will be minimized by the
model will then be the sum of all individual losses, unless
loss_weights is specified.
- metrics: List of metrics to be evaluated by the model during training
and testing. Each of this can be a string (name of a built-in
function), function or a tf.keras.metrics.Metric instance. See
tf.keras.metrics. Typically you will use metrics=[‘accuracy’]. A
function is any callable with the signature result = fn(y_true,
y_pred). To specify different metrics for different outputs of a
multi-output model, you could also pass a dictionary, such as
metrics={‘output_a’: ‘accuracy’, ‘output_b’: [‘accuracy’, ‘mse’]}.
You can also pass a list to specify a metric or a list of metrics
for each output, such as metrics=[[‘accuracy’], [‘accuracy’, ‘mse’]]
or metrics=[‘accuracy’, [‘accuracy’, ‘mse’]]. When you pass the
strings ‘accuracy’ or ‘acc’, we convert this to one of
tf.keras.metrics.BinaryAccuracy,
tf.keras.metrics.CategoricalAccuracy,
tf.keras.metrics.SparseCategoricalAccuracy based on the loss
function used and the model output shape. We do a similar
conversion for the strings ‘crossentropy’ and ‘ce’ as well.
- loss_weights: Optional list or dictionary specifying scalar coefficients
(Python floats) to weight the loss contributions of different model
outputs. The loss value that will be minimized by the model will then
be the weighted sum of all individual losses, weighted by the
loss_weights coefficients.
- If a list, it is expected to have a 1:1 mapping to the model’s
outputs. If a dict, it is expected to map output names (strings)
to scalar coefficients.
- weighted_metrics: List of metrics to be evaluated and weighted by
sample_weight or class_weight during training and testing.
- run_eagerly: Bool. Defaults to False. If True, this Model’s
logic will not be wrapped in a tf.function. Recommended to leave
this as None unless your Model cannot be run inside a
tf.function. run_eagerly=True is not supported when using
tf.distribute.experimental.ParameterServerStrategy.
- steps_per_execution: Int. Defaults to 1. The number of batches to run
during each tf.function call. Running multiple batches inside a
single tf.function call can greatly improve performance on TPUs or
small models with a large Python overhead. At most, one full epoch
will be run each execution. If a number larger than the size of the
epoch is passed, the execution will be truncated to the size of the
epoch. Note that if steps_per_execution is set to N,
Callback.on_batch_begin and Callback.on_batch_end methods will
only be called every N batches (i.e. before/after each tf.function
execution).
- jit_compile: If True, compile the model training step with XLA.
[XLA](https://www.tensorflow.org/xla) is an optimizing compiler for
machine learning.
jit_compile is not enabled for by default.
This option cannot be enabled with run_eagerly=True.
Note that jit_compile=True is
may not necessarily work for all models.
For more information on supported operations please refer to the
[XLA documentation](https://www.tensorflow.org/xla).
Also refer to
[known XLA issues](https://www.tensorflow.org/xla/known_issues) for
more details.
**kwargs: Arguments supported for backwards compatibility only.
-
compute_loss(x=None, y=None, y_pred=None, sample_weight=None)
Compute the total loss, validate it, and return it.
Subclasses can optionally override this method to provide custom loss
computation logic.
Example:
```python
class MyModel(tf.keras.Model):
- def __init__(self, *args, **kwargs):
super(MyModel, self).__init__(*args, **kwargs)
self.loss_tracker = tf.keras.metrics.Mean(name=’loss’)
- def compute_loss(self, x, y, y_pred, sample_weight):
loss = tf.reduce_mean(tf.math.squared_difference(y_pred, y))
loss += tf.add_n(self.losses)
self.loss_tracker.update_state(loss)
return loss
- def reset_metrics(self):
self.loss_tracker.reset_states()
@property
def metrics(self):
return [self.loss_tracker]
tensors = tf.random.uniform((10, 10)), tf.random.uniform((10,))
dataset = tf.data.Dataset.from_tensor_slices(tensors).repeat().batch(1)
inputs = tf.keras.layers.Input(shape=(10,), name=’my_input’)
outputs = tf.keras.layers.Dense(10)(inputs)
model = MyModel(inputs, outputs)
model.add_loss(tf.reduce_sum(outputs))
optimizer = tf.keras.optimizers.SGD()
model.compile(optimizer, loss=’mse’, steps_per_execution=10)
model.fit(dataset, epochs=2, steps_per_epoch=10)
print(‘My custom loss: ‘, model.loss_tracker.result().numpy())
```
- Args:
x: Input data.
y: Target data.
y_pred: Predictions returned by the model (output of model(x))
sample_weight: Sample weights for weighting the loss function.
- Returns:
The total loss as a tf.Tensor, or None if no loss results (which is
the case when called by Model.test_step).
-
compute_metrics(x, y, y_pred, sample_weight)
Update metric states and collect all metrics to be returned.
Subclasses can optionally override this method to provide custom metric
updating and collection logic.
Example:
```python
class MyModel(tf.keras.Sequential):
def compute_metrics(self, x, y, y_pred, sample_weight):
# This super call updates self.compiled_metrics and returns results
# for all metrics listed in self.metrics.
metric_results = super(MyModel, self).compute_metrics(
x, y, y_pred, sample_weight)
# Note that self.custom_metric is not listed in self.metrics.
self.custom_metric.update_state(x, y, y_pred, sample_weight)
metric_results[‘custom_metric_name’] = self.custom_metric.result()
return metric_results
```
- Args:
x: Input data.
y: Target data.
y_pred: Predictions returned by the model (output of model.call(x))
sample_weight: Sample weights for weighting the loss function.
- Returns:
A dict containing values that will be passed to
tf.keras.callbacks.CallbackList.on_train_batch_end(). Typically, the
values of the metrics listed in self.metrics are returned. Example:
{‘loss’: 0.2, ‘accuracy’: 0.7}.
-
property distribute_strategy
The tf.distribute.Strategy this model was created under.
-
evaluate(x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, return_dict=False, **kwargs)
Returns the loss value & metrics values for the model in test mode.
Computation is done in batches (see the batch_size arg.)
- Args:
- x: Input data. It could be:
A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
A TensorFlow tensor, or a list of tensors
(in case the model has multiple inputs).
A dict mapping input names to the corresponding array/tensors,
if the model has named inputs.
A tf.data dataset. Should return a tuple
of either (inputs, targets) or
(inputs, targets, sample_weights).
A generator or keras.utils.Sequence returning (inputs, targets)
or (inputs, targets, sample_weights).
A more detailed description of unpacking behavior for iterator types
(Dataset, generator, Sequence) is given in the Unpacking behavior
for iterator-like inputs section of Model.fit.
- y: Target data. Like the input data x, it could be either Numpy
array(s) or TensorFlow tensor(s). It should be consistent with x
(you cannot have Numpy inputs and tensor targets, or inversely). If
x is a dataset, generator or keras.utils.Sequence instance, y
should not be specified (since targets will be obtained from the
iterator/dataset).
- batch_size: Integer or None. Number of samples per batch of
computation. If unspecified, batch_size will default to 32. Do not
specify the batch_size if your data is in the form of a dataset,
generators, or keras.utils.Sequence instances (since they generate
batches).
verbose: 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.
sample_weight: Optional Numpy array of weights for the test samples,
used for weighting the loss function. You can either pass a flat (1D)
Numpy array with the same length as the input samples
- (1:1 mapping between weights and samples), or in the case of
temporal data, you can pass a 2D array with shape (samples,
sequence_length), to apply a different weight to every timestep
of every sample. This argument is not supported when x is a
dataset, instead pass sample weights as the third element of x.
- steps: Integer or None. Total number of steps (batches of samples)
before declaring the evaluation round finished. Ignored with the
default value of None. If x is a tf.data dataset and steps is
None, ‘evaluate’ will run until the dataset is exhausted. This
argument is not supported with array inputs.
- callbacks: List of keras.callbacks.Callback instances. List of
callbacks to apply during evaluation. See
[callbacks](/api_docs/python/tf/keras/callbacks).
- max_queue_size: Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue. If unspecified,
max_queue_size will default to 10.
- workers: Integer. Used for generator or keras.utils.Sequence input
only. Maximum number of processes to spin up when using process-based
threading. If unspecified, workers will default to 1.
- use_multiprocessing: Boolean. Used for generator or
keras.utils.Sequence input only. If True, use process-based
threading. If unspecified, use_multiprocessing will default to
False. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to the
generator as they can’t be passed easily to children processes.
- return_dict: If True, loss and metric results are returned as a dict,
with each key being the name of the metric. If False, they are
returned as a list.
**kwargs: Unused at this time.
See the discussion of Unpacking behavior for iterator-like inputs for
Model.fit.
- Returns:
Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.
- Raises:
RuntimeError: If model.evaluate is wrapped in a tf.function.
-
evaluate_generator(generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
Evaluates the model on a data generator.
- DEPRECATED:
Model.evaluate now supports generators, so there is no longer any need
to use this endpoint.
-
fit(x=None, y=None, batch_size=None, epochs=1, verbose='auto', callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False)
Trains the model for a fixed number of epochs (iterations on a dataset).
- Args:
- x: Input data. It could be:
A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
A TensorFlow tensor, or a list of tensors
(in case the model has multiple inputs).
A dict mapping input names to the corresponding array/tensors,
if the model has named inputs.
A tf.data dataset. Should return a tuple
of either (inputs, targets) or
(inputs, targets, sample_weights).
A generator or keras.utils.Sequence returning (inputs, targets)
or (inputs, targets, sample_weights).
A tf.keras.utils.experimental.DatasetCreator, which wraps a
callable that takes a single argument of type
tf.distribute.InputContext, and returns a tf.data.Dataset.
DatasetCreator should be used when users prefer to specify the
per-replica batching and sharding logic for the Dataset.
See tf.keras.utils.experimental.DatasetCreator doc for more
information.
A more detailed description of unpacking behavior for iterator types
(Dataset, generator, Sequence) is given below. If using
tf.distribute.experimental.ParameterServerStrategy, only
DatasetCreator type is supported for x.
- y: Target data. Like the input data x,
it could be either Numpy array(s) or TensorFlow tensor(s).
It should be consistent with x (you cannot have Numpy inputs and
tensor targets, or inversely). If x is a dataset, generator,
or keras.utils.Sequence instance, y should
not be specified (since targets will be obtained from x).
- batch_size: Integer or None.
Number of samples per gradient update.
If unspecified, batch_size will default to 32.
Do not specify the batch_size if your data is in the
form of datasets, generators, or keras.utils.Sequence instances
(since they generate batches).
- epochs: Integer. Number of epochs to train the model.
An epoch is an iteration over the entire x and y
data provided
(unless the steps_per_epoch flag is set to
something other than None).
Note that in conjunction with initial_epoch,
epochs is to be understood as “final epoch”.
The model is not trained for a number of iterations
given by epochs, but merely until the epoch
of index epochs is reached.
- verbose: ‘auto’, 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.
‘auto’ defaults to 1 for most cases, but 2 when used with
ParameterServerStrategy. Note that the progress bar is not
particularly useful when logged to a file, so verbose=2 is
recommended when not running interactively (eg, in a production
environment).
- callbacks: List of keras.callbacks.Callback instances.
List of callbacks to apply during training.
See tf.keras.callbacks. Note tf.keras.callbacks.ProgbarLogger
and tf.keras.callbacks.History callbacks are created automatically
and need not be passed into model.fit.
tf.keras.callbacks.ProgbarLogger is created or not based on
verbose argument to model.fit.
Callbacks with batch-level calls are currently unsupported with
tf.distribute.experimental.ParameterServerStrategy, and users are
advised to implement epoch-level calls instead with an appropriate
steps_per_epoch value.
- validation_split: Float between 0 and 1.
Fraction of the training data to be used as validation data.
The model will set apart this fraction of the training data,
will not train on it, and will evaluate
the loss and any model metrics
on this data at the end of each epoch.
The validation data is selected from the last samples
in the x and y data provided, before shuffling. This argument is
not supported when x is a dataset, generator or
- keras.utils.Sequence instance.
validation_split is not yet supported with
tf.distribute.experimental.ParameterServerStrategy.
- validation_data: Data on which to evaluate
the loss and any model metrics at the end of each epoch.
The model will not be trained on this data. Thus, note the fact
that the validation loss of data provided using validation_split
or validation_data is not affected by regularization layers like
noise and dropout.
validation_data will override validation_split.
validation_data could be:
A tuple (x_val, y_val) of Numpy arrays or tensors.
A tuple (x_val, y_val, val_sample_weights) of NumPy arrays.
A tf.data.Dataset.
A Python generator or keras.utils.Sequence returning
(inputs, targets) or (inputs, targets, sample_weights).
validation_data is not yet supported with
tf.distribute.experimental.ParameterServerStrategy.
- shuffle: Boolean (whether to shuffle the training data
before each epoch) or str (for ‘batch’). This argument is ignored
when x is a generator or an object of tf.data.Dataset.
‘batch’ is a special option for dealing
with the limitations of HDF5 data; it shuffles in batch-sized
chunks. Has no effect when steps_per_epoch is not None.
- class_weight: Optional dictionary mapping class indices (integers)
to a weight (float) value, used for weighting the loss function
(during training only).
This can be useful to tell the model to
“pay more attention” to samples from
an under-represented class.
- sample_weight: Optional Numpy array of weights for
the training samples, used for weighting the loss function
(during training only). You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape
(samples, sequence_length),
to apply a different weight to every timestep of every sample. This
argument is not supported when x is a dataset, generator, or
- keras.utils.Sequence instance, instead provide the sample_weights
as the third element of x.
- initial_epoch: Integer.
Epoch at which to start training
(useful for resuming a previous training run).
- steps_per_epoch: Integer or None.
Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. When training with input tensors such as
TensorFlow data tensors, the default None is equal to
the number of samples in your dataset divided by
the batch size, or 1 if that cannot be determined. If x is a
tf.data dataset, and ‘steps_per_epoch’
is None, the epoch will run until the input dataset is exhausted.
When passing an infinitely repeating dataset, you must specify the
steps_per_epoch argument. If steps_per_epoch=-1 the training
will run indefinitely with an infinitely repeating dataset.
This argument is not supported with array inputs.
When using tf.distribute.experimental.ParameterServerStrategy:
- validation_steps: Only relevant if validation_data is provided and
is a tf.data dataset. Total number of steps (batches of
samples) to draw before stopping when performing validation
at the end of every epoch. If ‘validation_steps’ is None, validation
will run until the validation_data dataset is exhausted. In the
case of an infinitely repeated dataset, it will run into an
infinite loop. If ‘validation_steps’ is specified and only part of
the dataset will be consumed, the evaluation will start from the
beginning of the dataset at each epoch. This ensures that the same
validation samples are used every time.
- validation_batch_size: Integer or None.
Number of samples per validation batch.
If unspecified, will default to batch_size.
Do not specify the validation_batch_size if your data is in the
form of datasets, generators, or keras.utils.Sequence instances
(since they generate batches).
- validation_freq: Only relevant if validation data is provided. Integer
or collections.abc.Container instance (e.g. list, tuple, etc.).
If an integer, specifies how many training epochs to run before a
new validation run is performed, e.g. validation_freq=2 runs
validation every 2 epochs. If a Container, specifies the epochs on
which to run validation, e.g. validation_freq=[1, 2, 10] runs
validation at the end of the 1st, 2nd, and 10th epochs.
- max_queue_size: Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue.
If unspecified, max_queue_size will default to 10.
- workers: Integer. Used for generator or keras.utils.Sequence input
only. Maximum number of processes to spin up
when using process-based threading. If unspecified, workers
will default to 1.
- use_multiprocessing: Boolean. Used for generator or
keras.utils.Sequence input only. If True, use process-based
threading. If unspecified, use_multiprocessing will default to
False. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can’t be passed easily to children processes.
- Unpacking behavior for iterator-like inputs:
A common pattern is to pass a tf.data.Dataset, generator, or
tf.keras.utils.Sequence to the x argument of fit, which will in fact
yield not only features (x) but optionally targets (y) and sample weights.
Keras requires that the output of such iterator-likes be unambiguous. The
iterator should return a tuple of length 1, 2, or 3, where the optional
second and third elements will be used for y and sample_weight
respectively. Any other type provided will be wrapped in a length one
tuple, effectively treating everything as ‘x’. When yielding dicts, they
should still adhere to the top-level tuple structure.
e.g. ({“x0”: x0, “x1”: x1}, y). Keras will not attempt to separate
features, targets, and weights from the keys of a single dict.
A notable unsupported data type is the namedtuple. The reason is that
it behaves like both an ordered datatype (tuple) and a mapping
datatype (dict). So given a namedtuple of the form:
namedtuple(“example_tuple”, [“y”, “x”])
it is ambiguous whether to reverse the order of the elements when
interpreting the value. Even worse is a tuple of the form:
namedtuple(“other_tuple”, [“x”, “y”, “z”])
where it is unclear if the tuple was intended to be unpacked into x, y,
and sample_weight or passed through as a single element to x. As a
result the data processing code will simply raise a ValueError if it
encounters a namedtuple. (Along with instructions to remedy the issue.)
- Returns:
A History object. Its History.history attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).
- Raises:
RuntimeError: 1. If the model was never compiled or,
2. If model.fit is wrapped in tf.function.
- ValueError: In case of mismatch between the provided input data
and what the model expects or when the input data is empty.
-
fit_generator(generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0)
Fits the model on data yielded batch-by-batch by a Python generator.
- DEPRECATED:
Model.fit now supports generators, so there is no longer any need to use
this endpoint.
-
classmethod from_config(config, custom_objects=None)
Creates a layer from its config.
This method is the reverse of get_config,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights).
- Args:
- config: A Python dictionary, typically the
output of get_config.
- Returns:
A layer instance.
-
get_config()
Returns the config of the layer.
A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.
The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by Network (one layer of abstraction above).
Note that get_config() does not guarantee to return a fresh copy of dict
every time it is called. The callers should make a copy of the returned dict
if they want to modify it.
- Returns:
Python dictionary.
-
get_layer(name=None, index=None)
Retrieves a layer based on either its name (unique) or index.
If name and index are both provided, index will take precedence.
Indices are based on order of horizontal graph traversal (bottom-up).
- Args:
name: String, name of layer.
index: Integer, index of layer.
- Returns:
A layer instance.
-
get_weights()
Retrieves the weights of the model.
- Returns:
A flat list of Numpy arrays.
-
property layers
-
load_weights(filepath, by_name=False, skip_mismatch=False, options=None)
Loads all layer weights, either from a TensorFlow or an HDF5 weight file.
If by_name is False weights are loaded based on the network’s
topology. This means the architecture should be the same as when the weights
were saved. Note that layers that don’t have weights are not taken into
account in the topological ordering, so adding or removing layers is fine as
long as they don’t have weights.
If by_name is True, weights are loaded into layers only if they share the
same name. This is useful for fine-tuning or transfer-learning models where
some of the layers have changed.
Only topological loading (by_name=False) is supported when loading weights
from the TensorFlow format. Note that topological loading differs slightly
between TensorFlow and HDF5 formats for user-defined classes inheriting from
tf.keras.Model: HDF5 loads based on a flattened list of weights, while the
TensorFlow format loads based on the object-local names of attributes to
which layers are assigned in the Model’s constructor.
- Args:
- filepath: String, path to the weights file to load. For weight files in
TensorFlow format, this is the file prefix (the same as was passed
to save_weights). This can also be a path to a SavedModel
saved from model.save.
- by_name: Boolean, whether to load weights by name or by topological
order. Only topological loading is supported for weight files in
TensorFlow format.
- skip_mismatch: Boolean, whether to skip loading of layers where there is
a mismatch in the number of weights, or a mismatch in the shape of
the weight (only valid when by_name=True).
- options: Optional tf.train.CheckpointOptions object that specifies
options for loading weights.
- Returns:
When loading a weight file in TensorFlow format, returns the same status
object as tf.train.Checkpoint.restore. When graph building, restore
ops are run automatically as soon as the network is built (on first call
for user-defined classes inheriting from Model, immediately if it is
already built).
When loading weights in HDF5 format, returns None.
- Raises:
- ImportError: If h5py is not available and the weight file is in HDF5
format.
- ValueError: If skip_mismatch is set to True when by_name is
False.
-
make_predict_function(force=False)
Creates a function that executes one step of inference.
This method can be overridden to support custom inference logic.
This method is called by Model.predict and Model.predict_on_batch.
Typically, this method directly controls tf.function and
tf.distribute.Strategy settings, and delegates the actual evaluation
logic to Model.predict_step.
This function is cached the first time Model.predict or
Model.predict_on_batch is called. The cache is cleared whenever
Model.compile is called. You can skip the cache and generate again the
function with force=True.
- Args:
- force: Whether to regenerate the predict function and skip the cached
function if available.
- Returns:
Function. The function created by this method should accept a
tf.data.Iterator, and return the outputs of the Model.
-
make_test_function(force=False)
Creates a function that executes one step of evaluation.
This method can be overridden to support custom evaluation logic.
This method is called by Model.evaluate and Model.test_on_batch.
Typically, this method directly controls tf.function and
tf.distribute.Strategy settings, and delegates the actual evaluation
logic to Model.test_step.
This function is cached the first time Model.evaluate or
Model.test_on_batch is called. The cache is cleared whenever
Model.compile is called. You can skip the cache and generate again the
function with force=True.
- Args:
- force: Whether to regenerate the test function and skip the cached
function if available.
- Returns:
Function. The function created by this method should accept a
tf.data.Iterator, and return a dict containing values that will
be passed to tf.keras.Callbacks.on_test_batch_end.
-
make_train_function(force=False)
Creates a function that executes one step of training.
This method can be overridden to support custom training logic.
This method is called by Model.fit and Model.train_on_batch.
Typically, this method directly controls tf.function and
tf.distribute.Strategy settings, and delegates the actual training
logic to Model.train_step.
This function is cached the first time Model.fit or
Model.train_on_batch is called. The cache is cleared whenever
Model.compile is called. You can skip the cache and generate again the
function with force=True.
- Args:
- force: Whether to regenerate the train function and skip the cached
function if available.
- Returns:
Function. The function created by this method should accept a
tf.data.Iterator, and return a dict containing values that will
be passed to tf.keras.Callbacks.on_train_batch_end, such as
{‘loss’: 0.2, ‘accuracy’: 0.7}.
-
property metrics
Returns the model’s metrics added using compile(), add_metric() APIs.
Note: Metrics passed to compile() are available only after a keras.Model
has been trained/evaluated on actual data.
Examples:
>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> outputs = tf.keras.layers.Dense(2)(inputs)
>>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
>>> [m.name for m in model.metrics]
[]
>>> x = np.random.random((2, 3))
>>> y = np.random.randint(0, 2, (2, 2))
>>> model.fit(x, y)
>>> [m.name for m in model.metrics]
['loss', 'mae']
>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2, name='out')
>>> output_1 = d(inputs)
>>> output_2 = d(inputs)
>>> model = tf.keras.models.Model(
... inputs=inputs, outputs=[output_1, output_2])
>>> model.add_metric(
... tf.reduce_sum(output_2), name='mean', aggregation='mean')
>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
>>> model.fit(x, (y, y))
>>> [m.name for m in model.metrics]
['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
'out_1_acc', 'mean']
-
property metrics_names
Returns the model’s display labels for all outputs.
Note: metrics_names are available only after a keras.Model has been
trained/evaluated on actual data.
Examples:
>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> outputs = tf.keras.layers.Dense(2)(inputs)
>>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
>>> model.metrics_names
[]
>>> x = np.random.random((2, 3))
>>> y = np.random.randint(0, 2, (2, 2))
>>> model.fit(x, y)
>>> model.metrics_names
['loss', 'mae']
>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2, name='out')
>>> output_1 = d(inputs)
>>> output_2 = d(inputs)
>>> model = tf.keras.models.Model(
... inputs=inputs, outputs=[output_1, output_2])
>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
>>> model.fit(x, (y, y))
>>> model.metrics_names
['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
'out_1_acc']
-
property non_trainable_weights
List of all non-trainable weights tracked by this layer.
Non-trainable weights are not updated during training. They are expected
to be updated manually in call().
- Returns:
A list of non-trainable variables.
-
predict(x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)
Generates output predictions for the input samples.
Computation is done in batches. This method is designed for batch processing
of large numbers of inputs. It is not intended for use inside of loops
that iterate over your data and process small numbers of inputs at a time.
For small numbers of inputs that fit in one batch,
directly use __call__() for faster execution, e.g.,
model(x), or model(x, training=False) if you have layers such as
tf.keras.layers.BatchNormalization that behave differently during
inference. You may pair the individual model call with a tf.function
for additional performance inside your inner loop.
If you need access to numpy array values instead of tensors after your
model call, you can use tensor.numpy() to get the numpy array value of
an eager tensor.
Also, note the fact that test loss is not affected by
regularization layers like noise and dropout.
Note: See [this FAQ entry](
https://keras.io/getting_started/faq/#whats-the-difference-between-model-methods-predict-and-call)
for more details about the difference between Model methods predict()
and __call__().
- Args:
- x: Input samples. It could be:
A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
A TensorFlow tensor, or a list of tensors
(in case the model has multiple inputs).
A tf.data dataset.
A generator or keras.utils.Sequence instance.
A more detailed description of unpacking behavior for iterator types
(Dataset, generator, Sequence) is given in the Unpacking behavior
for iterator-like inputs section of Model.fit.
- batch_size: Integer or None.
Number of samples per batch.
If unspecified, batch_size will default to 32.
Do not specify the batch_size if your data is in the
form of dataset, generators, or keras.utils.Sequence instances
(since they generate batches).
verbose: Verbosity mode, 0 or 1.
steps: Total number of steps (batches of samples)
before declaring the prediction round finished.
Ignored with the default value of None. If x is a tf.data
dataset and steps is None, predict() will
run until the input dataset is exhausted.
- callbacks: List of keras.callbacks.Callback instances.
List of callbacks to apply during prediction.
See [callbacks](/api_docs/python/tf/keras/callbacks).
- max_queue_size: Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue.
If unspecified, max_queue_size will default to 10.
- workers: Integer. Used for generator or keras.utils.Sequence input
only. Maximum number of processes to spin up when using
process-based threading. If unspecified, workers will default
to 1.
- use_multiprocessing: Boolean. Used for generator or
keras.utils.Sequence input only. If True, use process-based
threading. If unspecified, use_multiprocessing will default to
False. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can’t be passed easily to children processes.
See the discussion of Unpacking behavior for iterator-like inputs for
Model.fit. Note that Model.predict uses the same interpretation rules as
Model.fit and Model.evaluate, so inputs must be unambiguous for all
three methods.
- Returns:
Numpy array(s) of predictions.
- Raises:
RuntimeError: If model.predict is wrapped in a tf.function.
ValueError: In case of mismatch between the provided
input data and the model’s expectations,
or in case a stateful model receives a number of samples
that is not a multiple of the batch size.
-
predict_generator(generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
Generates predictions for the input samples from a data generator.
- DEPRECATED:
Model.predict now supports generators, so there is no longer any need
to use this endpoint.
-
predict_on_batch(x)
Returns predictions for a single batch of samples.
- Args:
- x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays (in case the
model has multiple inputs).
- A TensorFlow tensor, or a list of tensors (in case the model has
multiple inputs).
- Returns:
Numpy array(s) of predictions.
- Raises:
RuntimeError: If model.predict_on_batch is wrapped in a tf.function.
-
predict_step(data)
The logic for one inference step.
This method can be overridden to support custom inference logic.
This method is called by Model.make_predict_function.
This method should contain the mathematical logic for one step of inference.
This typically includes the forward pass.
Configuration details for how this logic is run (e.g. tf.function and
tf.distribute.Strategy settings), should be left to
Model.make_predict_function, which can also be overridden.
- Args:
data: A nested structure of `Tensor`s.
- Returns:
The result of one inference step, typically the output of calling the
Model on data.
-
reset_metrics()
Resets the state of all the metrics in the model.
Examples:
>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> outputs = tf.keras.layers.Dense(2)(inputs)
>>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
>>> x = np.random.random((2, 3))
>>> y = np.random.randint(0, 2, (2, 2))
>>> _ = model.fit(x, y, verbose=0)
>>> assert all(float(m.result()) for m in model.metrics)
>>> model.reset_metrics()
>>> assert all(float(m.result()) == 0 for m in model.metrics)
-
reset_states()
-
property run_eagerly
Settable attribute indicating whether the model should run eagerly.
Running eagerly means that your model will be run step by step,
like Python code. Your model might run slower, but it should become easier
for you to debug it by stepping into individual layer calls.
By default, we will attempt to compile your model to a static graph to
deliver the best execution performance.
- Returns:
Boolean, whether the model should run eagerly.
-
save(filepath, overwrite=True, include_optimizer=True, save_format=None, signatures=None, options=None, save_traces=True)
Saves the model to Tensorflow SavedModel or a single HDF5 file.
Please see tf.keras.models.save_model or the
[Serialization and Saving guide](https://keras.io/guides/serialization_and_saving/)
for details.
- Args:
- filepath: String, PathLike, path to SavedModel or H5 file to save the
model.
- overwrite: Whether to silently overwrite any existing file at the
target location, or provide the user with a manual prompt.
include_optimizer: If True, save optimizer’s state together.
save_format: Either ‘tf’ or ‘h5’, indicating whether to save the
model to Tensorflow SavedModel or HDF5. Defaults to ‘tf’ in TF 2.X,
and ‘h5’ in TF 1.X.
- signatures: Signatures to save with the SavedModel. Applicable to the
‘tf’ format only. Please see the signatures argument in
tf.saved_model.save for details.
- options: (only applies to SavedModel format)
tf.saved_model.SaveOptions object that specifies options for
saving to SavedModel.
- save_traces: (only applies to SavedModel format) When enabled, the
SavedModel will store the function traces for each layer. This
can be disabled, so that only the configs of each layer are stored.
Defaults to True. Disabling this will decrease serialization time
and reduce file size, but it requires that all custom layers/models
implement a get_config() method.
Example:
```python
from keras.models import load_model
model.save(‘my_model.h5’) # creates a HDF5 file ‘my_model.h5’
del model # deletes the existing model
# returns a compiled model
# identical to the previous one
model = load_model(‘my_model.h5’)
```
-
save_spec(dynamic_batch=True)
Returns the tf.TensorSpec of call inputs as a tuple (args, kwargs).
This value is automatically defined after calling the model for the first
time. Afterwards, you can use it when exporting the model for serving:
```python
model = tf.keras.Model(…)
@tf.function
def serve(*args, **kwargs):
outputs = model(*args, **kwargs)
# Apply postprocessing steps, or add additional outputs.
…
return outputs
# arg_specs is [tf.TensorSpec(…), …]. kwarg_specs, in this example, is
# an empty dict since functional models do not use keyword arguments.
arg_specs, kwarg_specs = model.save_spec()
- model.save(path, signatures={
‘serving_default’: serve.get_concrete_function(*arg_specs, **kwarg_specs)
- Args:
- dynamic_batch: Whether to set the batch sizes of all the returned
tf.TensorSpec to None. (Note that when defining functional or
Sequential models with tf.keras.Input([…], batch_size=X), the
batch size will always be preserved). Defaults to True.
- Returns:
If the model inputs are defined, returns a tuple (args, kwargs). All
elements in args and kwargs are tf.TensorSpec.
If the model inputs are not defined, returns None.
The model inputs are automatically set when calling the model,
model.fit, model.evaluate or model.predict.
-
save_weights(filepath, overwrite=True, save_format=None, options=None)
Saves all layer weights.
Either saves in HDF5 or in TensorFlow format based on the save_format
argument.
- When saving in HDF5 format, the weight file has:
- layer_names (attribute), a list of strings
(ordered names of model layers).
- For every layer, a group named layer.name
- For every such layer group, a group attribute weight_names,
a list of strings
(ordered names of weights tensor of the layer).
- For every weight in the layer, a dataset
storing the weight value, named after the weight tensor.
When saving in TensorFlow format, all objects referenced by the network are
saved in the same format as tf.train.Checkpoint, including any Layer
instances or Optimizer instances assigned to object attributes. For
networks constructed from inputs and outputs using tf.keras.Model(inputs,
outputs), Layer instances used by the network are tracked/saved
automatically. For user-defined classes which inherit from tf.keras.Model,
Layer instances must be assigned to object attributes, typically in the
constructor. See the documentation of tf.train.Checkpoint and
tf.keras.Model for details.
While the formats are the same, do not mix save_weights and
tf.train.Checkpoint. Checkpoints saved by Model.save_weights should be
loaded using Model.load_weights. Checkpoints saved using
tf.train.Checkpoint.save should be restored using the corresponding
tf.train.Checkpoint.restore. Prefer tf.train.Checkpoint over
save_weights for training checkpoints.
The TensorFlow format matches objects and variables by starting at a root
object, self for save_weights, and greedily matching attribute
names. For Model.save this is the Model, and for Checkpoint.save this
is the Checkpoint even if the Checkpoint has a model attached. This
means saving a tf.keras.Model using save_weights and loading into a
tf.train.Checkpoint with a Model attached (or vice versa) will not match
the Model’s variables. See the
[guide to training checkpoints](https://www.tensorflow.org/guide/checkpoint)
for details on the TensorFlow format.
- Args:
- filepath: String or PathLike, path to the file to save the weights to.
When saving in TensorFlow format, this is the prefix used for
checkpoint files (multiple files are generated). Note that the ‘.h5’
suffix causes weights to be saved in HDF5 format.
- overwrite: Whether to silently overwrite any existing file at the
target location, or provide the user with a manual prompt.
- save_format: Either ‘tf’ or ‘h5’. A filepath ending in ‘.h5’ or
‘.keras’ will default to HDF5 if save_format is None. Otherwise
None defaults to ‘tf’.
- options: Optional tf.train.CheckpointOptions object that specifies
options for saving weights.
- Raises:
- ImportError: If h5py is not available when attempting to save in HDF5
format.
-
property state_updates
Deprecated, do NOT use!
Returns the updates from all layers that are stateful.
This is useful for separating training updates and
state updates, e.g. when we need to update a layer’s internal state
during prediction.
- Returns:
A list of update ops.
-
summary(line_length=None, positions=None, print_fn=None, expand_nested=False, show_trainable=False)
Prints a string summary of the network.
- Args:
- line_length: Total length of printed lines
(e.g. set this to adapt the display to different
terminal window sizes).
- positions: Relative or absolute positions of log elements
in each line. If not provided,
defaults to [.33, .55, .67, 1.].
- print_fn: Print function to use. Defaults to print.
It will be called on each line of the summary.
You can set it to a custom function
in order to capture the string summary.
- expand_nested: Whether to expand the nested models.
If not provided, defaults to False.
- show_trainable: Whether to show if a layer is trainable.
If not provided, defaults to False.
- Raises:
ValueError: if summary() is called before the model is built.
-
test_on_batch(x, y=None, sample_weight=None, reset_metrics=True, return_dict=False)
Test the model on a single batch of samples.
- Args:
- x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays (in case the
model has multiple inputs).
- A TensorFlow tensor, or a list of tensors (in case the model has
multiple inputs).
- A dict mapping input names to the corresponding array/tensors, if
the model has named inputs.
- y: Target data. Like the input data x, it could be either Numpy
array(s) or TensorFlow tensor(s). It should be consistent with x
(you cannot have Numpy inputs and tensor targets, or inversely).
- sample_weight: Optional array of the same length as x, containing
weights to apply to the model’s loss for each sample. In the case of
temporal data, you can pass a 2D array with shape (samples,
sequence_length), to apply a different weight to every timestep of
every sample.
- reset_metrics: If True, the metrics returned will be only for this
batch. If False, the metrics will be statefully accumulated across
batches.
- return_dict: If True, loss and metric results are returned as a dict,
with each key being the name of the metric. If False, they are
returned as a list.
- Returns:
Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.
- Raises:
RuntimeError: If model.test_on_batch is wrapped in a tf.function.
-
test_step(data)
The logic for one evaluation step.
This method can be overridden to support custom evaluation logic.
This method is called by Model.make_test_function.
This function should contain the mathematical logic for one step of
evaluation.
This typically includes the forward pass, loss calculation, and metrics
updates.
Configuration details for how this logic is run (e.g. tf.function and
tf.distribute.Strategy settings), should be left to
Model.make_test_function, which can also be overridden.
- Args:
data: A nested structure of `Tensor`s.
- Returns:
A dict containing values that will be passed to
tf.keras.callbacks.CallbackList.on_train_batch_end. Typically, the
values of the Model’s metrics are returned.
-
to_json(**kwargs)
Returns a JSON string containing the network configuration.
To load a network from a JSON save file, use
keras.models.model_from_json(json_string, custom_objects={}).
- Args:
- **kwargs: Additional keyword arguments
to be passed to json.dumps().
- Returns:
A JSON string.
-
to_yaml(**kwargs)
Returns a yaml string containing the network configuration.
Note: Since TF 2.6, this method is no longer supported and will raise a
RuntimeError.
To load a network from a yaml save file, use
keras.models.model_from_yaml(yaml_string, custom_objects={}).
custom_objects should be a dictionary mapping
the names of custom losses / layers / etc to the corresponding
functions / classes.
- Args:
- **kwargs: Additional keyword arguments
to be passed to yaml.dump().
- Returns:
A YAML string.
- Raises:
RuntimeError: announces that the method poses a security risk
-
train_on_batch(x, y=None, sample_weight=None, class_weight=None, reset_metrics=True, return_dict=False)
Runs a single gradient update on a single batch of data.
- Args:
- x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
- A TensorFlow tensor, or a list of tensors
(in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors,
if the model has named inputs.
- y: Target data. Like the input data x, it could be either Numpy
array(s) or TensorFlow tensor(s). It should be consistent with x
(you cannot have Numpy inputs and tensor targets, or inversely).
- sample_weight: Optional array of the same length as x, containing
weights to apply to the model’s loss for each sample. In the case of
temporal data, you can pass a 2D array with shape (samples,
sequence_length), to apply a different weight to every timestep of
every sample.
- class_weight: Optional dictionary mapping class indices (integers) to a
weight (float) to apply to the model’s loss for the samples from this
class during training. This can be useful to tell the model to “pay
more attention” to samples from an under-represented class.
- reset_metrics: If True, the metrics returned will be only for this
batch. If False, the metrics will be statefully accumulated across
batches.
- return_dict: If True, loss and metric results are returned as a dict,
with each key being the name of the metric. If False, they are
returned as a list.
- Returns:
Scalar training loss
(if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.
- Raises:
RuntimeError: If model.train_on_batch is wrapped in a tf.function.
-
train_step(data)
The logic for one training step.
This method can be overridden to support custom training logic.
For concrete examples of how to override this method see
[Customizing what happends in fit](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit).
This method is called by Model.make_train_function.
This method should contain the mathematical logic for one step of training.
This typically includes the forward pass, loss calculation, backpropagation,
and metric updates.
Configuration details for how this logic is run (e.g. tf.function and
tf.distribute.Strategy settings), should be left to
Model.make_train_function, which can also be overridden.
- Args:
data: A nested structure of `Tensor`s.
- Returns:
A dict containing values that will be passed to
tf.keras.callbacks.CallbackList.on_train_batch_end. Typically, the
values of the Model’s metrics are returned. Example:
{‘loss’: 0.2, ‘accuracy’: 0.7}.
-
property trainable_weights
List of all trainable weights tracked by this layer.
Trainable weights are updated via gradient descent during training.
- Returns:
A list of trainable variables.
-
property weights
Returns the list of all layer variables/weights.
Note: This will not track the weights of nested tf.Modules that are not
themselves Keras layers.
- Returns:
A list of variables.
-
class dipy.nn.histo_resdnn.Version(version: str)
Bases: packaging.version._BaseVersion
- Attributes
- base_version
- dev
- epoch
- is_devrelease
- is_postrelease
- is_prerelease
- local
- major
- micro
- minor
- post
- pre
- public
- release
-
__init__(version: str) → None
-
property base_version: str
-
property dev: Optional[int]
-
property epoch: int
-
property is_devrelease: bool
-
property is_postrelease: bool
-
property is_prerelease: bool
-
property local: Optional[str]
-
property major: int
-
property micro: int
-
property minor: int
-
property post: Optional[int]
-
property pre: Optional[Tuple[str, int]]
-
property public: str
-
property release: Tuple[int, ...]
doctest_skip_parser
-
dipy.nn.histo_resdnn.doctest_skip_parser(func)
Decorator replaces custom skip test markup in doctests.
Say a function has a docstring:
>>> something # skip if not HAVE_AMODULE
>>> something + else
>>> something # skip if HAVE_BMODULE
This decorator will evaluate the expresssion after skip if
. If this
evaluates to True, then the comment is replaced by # doctest: +SKIP
.
If False, then the comment is just removed. The expression is evaluated in
the globals
scope of func.
For example, if the module global HAVE_AMODULE
is False, and module
global HAVE_BMODULE
is False, the returned function will have
docstring:
>>> something
>>> something + else
>>> something
get_bval_indices
-
dipy.nn.histo_resdnn.get_bval_indices(bvals, bval, tol=20)
Get indices where the b-value is bval
- Parameters
- bvals: ndarray
Array containing the b-values
- bval: float or int
b-value to extract indices
- tol: int
The tolerated gap between the b-values to extract
and the actual b-values.
- Returns
- Array of indices where the b-value is bval
get_fnames
-
dipy.nn.histo_resdnn.get_fnames(name='small_64D')
Provide full paths to example or test datasets.
- Parameters
- namestr
the filename/s of which dataset to return, one of:
‘small_64D’ small region of interest nifti,bvecs,bvals 64 directions
‘small_101D’ small region of interest nifti, bvecs, bvals
101 directions
‘aniso_vox’ volume with anisotropic voxel size as Nifti
‘fornix’ 300 tracks in Trackvis format (from Pittsburgh
Brain Competition)
‘gqi_vectors’ the scanner wave vectors needed for a GQI acquisitions
of 101 directions tested on Siemens 3T Trio
‘small_25’ small ROI (10x8x2) DTI data (b value 2000, 25 directions)
‘test_piesno’ slice of N=8, K=14 diffusion data
‘reg_c’ small 2D image used for validating registration
‘reg_o’ small 2D image used for validation registration
‘cb_2’ two vectorized cingulum bundles
- Returns
- fnamestuple
filenames for dataset
Examples
>>> import numpy as np
>>> from dipy.io.image import load_nifti
>>> from dipy.data import get_fnames
>>> fimg, fbvals, fbvecs = get_fnames('small_101D')
>>> bvals=np.loadtxt(fbvals)
>>> bvecs=np.loadtxt(fbvecs).T
>>> data, affine = load_nifti(fimg)
>>> data.shape == (6, 10, 10, 102)
True
>>> bvals.shape == (102,)
True
>>> bvecs.shape == (102, 3)
True
get_sphere
-
dipy.nn.histo_resdnn.get_sphere(name='symmetric362')
provide triangulated spheres
- Parameters
- namestr
which sphere - one of:
* ‘symmetric362’
* ‘symmetric642’
* ‘symmetric724’
* ‘repulsion724’
* ‘repulsion100’
* ‘repulsion200’
- Returns
- spherea dipy.core.sphere.Sphere class instance
Examples
>>> import numpy as np
>>> from dipy.data import get_sphere
>>> sphere = get_sphere('symmetric362')
>>> verts, faces = sphere.vertices, sphere.faces
>>> verts.shape == (362, 3)
True
>>> faces.shape == (720, 3)
True
>>> verts, faces = get_sphere('not a sphere name')
Traceback (most recent call last):
...
DataError: No sphere called "not a sphere name"
optional_package
-
dipy.nn.histo_resdnn.optional_package(name, trip_msg=None)
Return package-like thing and module setup for package name
- Parameters
- namestr
package name
- trip_msgNone or str
message to give when someone tries to use the return package, but we
could not import it, and have returned a TripWire object instead.
Default message if None.
- Returns
- pkg_likemodule or
TripWire
instance If we can import the package, return it. Otherwise return an object
raising an error when accessed
- have_pkgbool
True if import for package was successful, false otherwise
- module_setupfunction
callable usually set as setup_module
in calling namespace, to allow
skipping tests.
Examples
Typical use would be something like this at the top of a module using an
optional package:
>>> from dipy.utils.optpkg import optional_package
>>> pkg, have_pkg, setup_module = optional_package('not_a_package')
Of course in this case the package doesn’t exist, and so, in the module:
and
>>> pkg.some_function()
Traceback (most recent call last):
...
TripWireError: We need package not_a_package for these functions, but
``import not_a_package`` raised an ImportError
If the module does exist - we get the module
>>> pkg, _, _ = optional_package('os')
>>> hasattr(pkg, 'path')
True
Or a submodule if that’s what we asked for
>>> subpkg, _, _ = optional_package('os.path')
>>> hasattr(subpkg, 'dirname')
True
set_logger_level
-
dipy.nn.histo_resdnn.set_logger_level(log_level)
Change the logger of the HistoResDNN to one on the following:
DEBUG, INFO, WARNING, CRITICAL, ERROR
- Parameters
- log_levelstr
Log level for the HistoResDNN only
sf_to_sh
-
dipy.nn.histo_resdnn.sf_to_sh(sf, sphere, sh_order=4, basis_type=None, full_basis=False, legacy=True, smooth=0.0)
Spherical function to spherical harmonics (SH).
- Parameters
- sfndarray
Values of a function on the given sphere
.
- sphereSphere
The points on which the sf is defined.
- sh_orderint, optional
Maximum SH order in the SH fit. For sh_order
, there will be
(sh_order + 1) * (sh_order + 2) / 2
SH coefficients for a symmetric
basis and (sh_order + 1) * (sh_order + 1)
coefficients for a full
SH basis.
- basis_type{None, ‘tournier07’, ‘descoteaux07’}, optional
None
for the default DIPY basis,
tournier07
for the Tournier 2007 [R35636a4a5d66-2]_[R35636a4a5d66-3]_ basis,
descoteaux07
for the Descoteaux 2007 [1] basis,
(None
defaults to descoteaux07
).
- full_basis: bool, optional
True for using a SH basis containing even and odd order SH functions.
False for using a SH basis consisting only of even order SH functions.
- legacy: bool, optional
True to use a legacy basis definition for backward compatibility
with previous tournier07
and descoteaux07
implementations.
- smoothfloat, optional
Lambda-regularization in the SH fit.
- Returns
- shndarray
SH coefficients representing the input function.
References
- 1
Descoteaux, M., Angelino, E., Fitzgibbons, S. and Deriche, R.
Regularized, Fast, and Robust Analytical Q-ball Imaging.
Magn. Reson. Med. 2007;58:497-510.
- 2
Tournier J.D., Calamante F. and Connelly A. Robust determination
of the fibre orientation distribution in diffusion MRI:
Non-negativity constrained super-resolved spherical deconvolution.
NeuroImage. 2007;35(4):1459-1472.
- 3
Tournier J-D, Smith R, Raffelt D, Tabbara R, Dhollander T,
Pietsch M, et al. MRtrix3: A fast, flexible and open software
framework for medical image processing and visualisation.
NeuroImage. 2019 Nov 15;202:116-137.
sh_to_sf
-
dipy.nn.histo_resdnn.sh_to_sf(sh, sphere, sh_order=4, basis_type=None, full_basis=False, legacy=True)
Spherical harmonics (SH) to spherical function (SF).
- Parameters
- shndarray
SH coefficients representing a spherical function.
- sphereSphere
The points on which to sample the spherical function.
- sh_orderint, optional
Maximum SH order in the SH fit. For sh_order
, there will be
(sh_order + 1) * (sh_order + 2) / 2
SH coefficients for a symmetric
basis and (sh_order + 1) * (sh_order + 1)
coefficients for a full
SH basis.
- basis_type{None, ‘tournier07’, ‘descoteaux07’}, optional
None
for the default DIPY basis,
tournier07
for the Tournier 2007 [R30944dc1667c-2]_[R30944dc1667c-3]_ basis,
descoteaux07
for the Descoteaux 2007 [1] basis,
(None
defaults to descoteaux07
).
- full_basis: bool, optional
True to use a SH basis containing even and odd order SH functions.
Else, use a SH basis consisting only of even order SH functions.
- legacy: bool, optional
True to use a legacy basis definition for backward compatibility
with previous tournier07
and descoteaux07
implementations.
- Returns
- sfndarray
Spherical function values on the sphere
.
References
- 1
Descoteaux, M., Angelino, E., Fitzgibbons, S. and Deriche, R.
Regularized, Fast, and Robust Analytical Q-ball Imaging.
Magn. Reson. Med. 2007;58:497-510.
- 2
Tournier J.D., Calamante F. and Connelly A. Robust determination
of the fibre orientation distribution in diffusion MRI:
Non-negativity constrained super-resolved spherical deconvolution.
NeuroImage. 2007;35(4):1459-1472.
- 3
Tournier J-D, Smith R, Raffelt D, Tabbara R, Dhollander T,
Pietsch M, et al. MRtrix3: A fast, flexible and open software
framework for medical image processing and visualisation.
NeuroImage. 2019 Nov 15;202:116-137.
sph_harm_ind_list
-
dipy.nn.histo_resdnn.sph_harm_ind_list(sh_order, full_basis=False)
Returns the degree (m
) and order (n
) of all the symmetric spherical
harmonics of degree less then or equal to sh_order
. The results,
m_list
and n_list
are kx1 arrays, where k depends on sh_order
.
They can be passed to real_sh_descoteaux_from_index()
and
:func:real_sh_tournier_from_index
.
- Parameters
- sh_orderint
even int > 0, max order to return
- full_basis: bool, optional
True for SH basis with even and odd order terms
- Returns
- m_listarray
degrees of even spherical harmonics
- n_listarray
orders of even spherical harmonics
See also
shm.real_sh_descoteaux_from_index
, shm.real_sh_tournier_from_index
unique_bvals_magnitude
-
dipy.nn.histo_resdnn.unique_bvals_magnitude(bvals, bmag=None, rbvals=False)
This function gives the unique rounded b-values of the data
- Parameters
- bvalsndarray
Array containing the b-values
- bmagint
The order of magnitude that the bvalues have to differ to be
considered an unique b-value. B-values are also rounded up to
this order of magnitude. Default: derive this value from the
maximal b-value provided: \(bmag=log_{10}(max(bvals)) - 1\).
- rbvalsbool, optional
If True function also returns all individual rounded b-values.
Default: False
- Returns
- ubvalsndarray
Array containing the rounded unique b-values
-
class dipy.nn.model.MultipleLayerPercepton(input_shape=(28, 28), num_hidden=[128], act_hidden='relu', dropout=0.2, num_out=10, act_out='softmax', loss='sparse_categorical_crossentropy', optimizer='adam')
Bases: object
Methods
evaluate (x_test, y_test[, verbose])
|
Evaluate the model on test dataset. |
fit (x_train, y_train[, epochs])
|
Train the model on train dataset. |
predict (x_test)
|
Predict the output from input samples. |
summary ()
|
Get the summary of the model. |
-
__init__(input_shape=(28, 28), num_hidden=[128], act_hidden='relu', dropout=0.2, num_out=10, act_out='softmax', loss='sparse_categorical_crossentropy', optimizer='adam')
Multiple Layer Perceptron with Dropout.
- Parameters
- input_shapetuple
Shape of data to be trained
- num_hiddenlist
List of number of nodes in hidden layers
- act_hiddenstring
Activation function used in hidden layer
- dropoutfloat
Dropout ratio
- num_out10
Number of nodes in output layer
- act_outstring
Activation function used in output layer
- optimizerstring
Select optimizer. Default adam.
- lossstring
Select loss function for measuring accuracy.
Default sparse_categorical_crossentropy.
-
evaluate(x_test, y_test, verbose=2)
Evaluate the model on test dataset.
The evaluate method will evaluate the model on a test
dataset.
- Parameters
- x_testndarray
the x_test is the test dataset
- y_testndarray shape=(BatchSize,)
the y_test is the labels of the test dataset
- verboseint (Default = 2)
By setting verbose 0, 1 or 2 you just say how do you want to
‘see’ the training progress for each epoch.
- Returns
- evaluateList
return list of loss value and accuracy value on test dataset
-
fit(x_train, y_train, epochs=5)
Train the model on train dataset.
The fit method will train the model for a fixed
number of epochs (iterations) on a dataset.
- Parameters
- x_trainndarray
the x_train is the train dataset
- y_trainndarray shape=(BatchSize,)
the y_train is the labels of the train dataset
- epochsint (Default = 5)
the number of epochs
- Returns
- histobject
A History object. Its History.history attribute is a record of
training loss values and metrics values at successive epochs
-
predict(x_test)
Predict the output from input samples.
The predict method will generates output predictions
for the input samples.
- Parameters
- x_trainndarray
the x_test is the test dataset or input samples
- Returns
- predictndarray shape(TestSize,OutputSize)
Numpy array(s) of predictions.
-
summary()
Get the summary of the model.
The summary is textual and includes information about:
The layers and their order in the model.
The output shape of each layer.
- Returns
- summaryNoneType
the summary of the model
-
class dipy.nn.model.SingleLayerPerceptron(input_shape=(28, 28), num_hidden=128, act_hidden='relu', dropout=0.2, num_out=10, act_out='softmax', optimizer='adam', loss='sparse_categorical_crossentropy')
Bases: object
Methods
evaluate (x_test, y_test[, verbose])
|
Evaluate the model on test dataset. |
fit (x_train, y_train[, epochs])
|
Train the model on train dataset. |
predict (x_test)
|
Predict the output from input samples. |
summary ()
|
Get the summary of the model. |
-
__init__(input_shape=(28, 28), num_hidden=128, act_hidden='relu', dropout=0.2, num_out=10, act_out='softmax', optimizer='adam', loss='sparse_categorical_crossentropy')
Single Layer Perceptron with Dropout.
- Parameters
- input_shapetuple
Shape of data to be trained
- num_hiddenint
Number of nodes in hidden layer
- act_hiddenstring
Activation function used in hidden layer
- dropoutfloat
Dropout ratio
- num_out10
Number of nodes in output layer
- act_outstring
Activation function used in output layer
- optimizerstring
Select optimizer. Default adam.
- lossstring
Select loss function for measuring accuracy.
Default sparse_categorical_crossentropy.
-
evaluate(x_test, y_test, verbose=2)
Evaluate the model on test dataset.
The evaluate method will evaluate the model on a test
dataset.
- Parameters
- x_testndarray
the x_test is the test dataset
- y_testndarray shape=(BatchSize,)
the y_test is the labels of the test dataset
- verboseint (Default = 2)
By setting verbose 0, 1 or 2 you just say how do you want to
‘see’ the training progress for each epoch.
- Returns
- evaluateList
return list of loss value and accuracy value on test dataset
-
fit(x_train, y_train, epochs=5)
Train the model on train dataset.
The fit method will train the model for a fixed
number of epochs (iterations) on a dataset.
- Parameters
- x_trainndarray
the x_train is the train dataset
- y_trainndarray shape=(BatchSize,)
the y_train is the labels of the train dataset
- epochsint (Default = 5)
the number of epochs
- Returns
- histobject
A History object. Its History.history attribute is a record of
training loss values and metrics values at successive epochs
-
predict(x_test)
Predict the output from input samples.
The predict method will generates output predictions
for the input samples.
- Parameters
- x_trainndarray
the x_test is the test dataset or input samples
- Returns
- predictndarray shape(TestSize,OutputSize)
Numpy array(s) of predictions.
-
summary()
Get the summary of the model.
The summary is textual and includes information about:
The layers and their order in the model.
The output shape of each layer.
- Returns
- summaryNoneType
the summary of the model
-
class dipy.nn.model.Version(version: str)
Bases: packaging.version._BaseVersion
- Attributes
- base_version
- dev
- epoch
- is_devrelease
- is_postrelease
- is_prerelease
- local
- major
- micro
- minor
- post
- pre
- public
- release
-
__init__(version: str) → None
-
property base_version: str
-
property dev: Optional[int]
-
property epoch: int
-
property is_devrelease: bool
-
property is_postrelease: bool
-
property is_prerelease: bool
-
property local: Optional[str]
-
property major: int
-
property micro: int
-
property minor: int
-
property post: Optional[int]
-
property pre: Optional[Tuple[str, int]]
-
property public: str
-
property release: Tuple[int, ...]
optional_package
-
dipy.nn.model.optional_package(name, trip_msg=None)
Return package-like thing and module setup for package name
- Parameters
- namestr
package name
- trip_msgNone or str
message to give when someone tries to use the return package, but we
could not import it, and have returned a TripWire object instead.
Default message if None.
- Returns
- pkg_likemodule or
TripWire
instance If we can import the package, return it. Otherwise return an object
raising an error when accessed
- have_pkgbool
True if import for package was successful, false otherwise
- module_setupfunction
callable usually set as setup_module
in calling namespace, to allow
skipping tests.
Examples
Typical use would be something like this at the top of a module using an
optional package:
>>> from dipy.utils.optpkg import optional_package
>>> pkg, have_pkg, setup_module = optional_package('not_a_package')
Of course in this case the package doesn’t exist, and so, in the module:
and
>>> pkg.some_function()
Traceback (most recent call last):
...
TripWireError: We need package not_a_package for these functions, but
``import not_a_package`` raised an ImportError
If the module does exist - we get the module
>>> pkg, _, _ = optional_package('os')
>>> hasattr(pkg, 'path')
True
Or a submodule if that’s what we asked for
>>> subpkg, _, _ = optional_package('os.path')
>>> hasattr(subpkg, 'dirname')
True