Module `phiml.backend.tensorflow.nets`

TensorFlow implementation of the unified machine learning API. Equivalent functions also exist for the other frameworks.

For API documentation, see phiml.nn.

Functions

def adagrad(net: keras.src.engine.training.Model, learning_rate: float = 0.001, lr_decay=0.0, weight_decay=0.0, initial_accumulator_value=0.0, eps=1e-10)
def adam(net: keras.src.engine.training.Model, learning_rate: float = 0.001, betas=(0.9, 0.999), epsilon=1e-07)
def conv_classifier(in_features: int, in_spatial: Union[tuple, list], num_classes: int, blocks=(64, 128, 256, 256, 512, 512), block_sizes=(2, 2, 3, 3, 3), dense_layers=(4096, 4096, 100), batch_norm=True, activation='ReLU', softmax=True, periodic=False)
def conv_net(in_channels: int, out_channels: int, layers: Sequence[int], batch_norm: bool = False, activation: Union[str, Callable] = 'ReLU', periodic=False, in_spatial: Union[int, tuple] = 2) ‑> keras.src.engine.training.Model
def double_conv(x, d: int, out_channels: int, mid_channels: int, batch_norm: bool, activation: Callable, periodic: bool, kernel_size=3)
def get_mask(inputs, reverse_mask, data_format='NHWC'): Compute mask for slicing input feature map for Invertible Nets
def get_parameters(model: keras.src.engine.training.Model, wrap=True) ‑> dict
def invertible_net(num_blocks: int, construct_net: Union[str, Callable], **construct_kwargs)
def load_state(obj: Union[keras.src.engine.training.Model, keras.src.optimizers.optimizer.Optimizer], path: str)
def mlp(in_channels: int, out_channels: int, layers: Sequence[int], batch_norm=False, activation='ReLU', softmax=False) ‑> keras.src.engine.training.Model
def pad_periodic(x: tensorflow.python.framework.ops.Tensor)
def res_net(in_channels: int, out_channels: int, layers: Sequence[int], batch_norm: bool = False, activation: Union[str, Callable] = 'ReLU', periodic=False, in_spatial: Union[int, tuple] = 2)
def resnet_block(in_channels: int, out_channels: int, periodic: bool, batch_norm: bool = False, activation: Union[str, Callable] = 'ReLU', in_spatial: Union[int, tuple] = 2, kernel_size=3)
def rmsprop(net: keras.src.engine.training.Model, learning_rate: float = 0.001, alpha=0.99, eps=1e-08, weight_decay=0.0, momentum=0.0, centered=False)
def save_state(obj: Union[keras.src.engine.training.Model, keras.src.optimizers.optimizer.Optimizer], path: str)
def sgd(net: keras.src.engine.training.Model, learning_rate: float = 0.001, momentum=0.0, dampening=0.0, weight_decay=0.0, nesterov=False)
def u_net(in_channels: int, out_channels: int, levels: int = 4, filters: Union[int, Sequence[+T_co]] = 16, batch_norm: bool = True, activation: Union[str, Callable] = 'ReLU', in_spatial: Union[int, tuple] = 2, periodic=False, use_res_blocks: bool = False, down_kernel_size=3, up_kernel_size=3) ‑> keras.src.engine.training.Model
def update_weights(net: keras.src.engine.training.Model, optimizer: keras.src.optimizers.optimizer.Optimizer, loss_function: Callable, *loss_args, **loss_kwargs)

Classes

class CouplingLayer (construct_net: Callable, construction_kwargs: dict, reverse_mask)

A model grouping layers into an object with training/inference features.

Args

inputs: The input(s) of the model: a keras.Input object or a combination of keras.Input objects in a dict, list or tuple.
outputs: The output(s) of the model: a tensor that originated from keras.Input objects or a combination of such tensors in a dict, list or tuple. 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:

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:

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.Input 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().

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:

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().

Expand source code

class CouplingLayer(keras.Model):

    def __init__(self, construct_net: Callable, construction_kwargs: dict, reverse_mask):
        super().__init__()
        self.reverse_mask = reverse_mask
        self.s1 = construct_net(**construction_kwargs)
        self.t1 = construct_net(**construction_kwargs)
        self.s2 = construct_net(**construction_kwargs)
        self.t2 = construct_net(**construction_kwargs)

    def call(self, x, invert=False):
        mask = tf.cast(get_mask(x, self.reverse_mask, 'NCHW'), x.dtype)
        if invert:
            v1 = x * mask
            v2 = x * (1 - mask)
            u2 = (1 - mask) * (v2 - self.t1(v1)) * tf.math.exp(tf.tanh(-self.s1(v1)))
            u1 = mask * (v1 - self.t2(u2)) * tf.math.exp(tf.tanh(-self.s2(u2)))
            return u1 + u2
        else:
            u1 = x * mask
            u2 = x * (1 - mask)
            v1 = mask * (u1 * tf.math.exp(tf.tanh(self.s2(u2))) + self.t2(u2))
            v2 = (1 - mask) * (u2 * tf.math.exp(tf.tanh(self.s1(v1))) + self.t1(v1))
            return v1 + v2

Ancestors

keras.src.engine.training.Model
keras.src.engine.base_layer.Layer
tensorflow.python.module.module.Module
tensorflow.python.trackable.autotrackable.AutoTrackable
tensorflow.python.trackable.base.Trackable
keras.src.utils.version_utils.LayerVersionSelector
keras.src.utils.version_utils.ModelVersionSelector

Methods

def call(self, x, invert=False)

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.

Returns

A tensor if there is a single output, or a list of tensors if there are more than one outputs.

class InvertibleNet (num_blocks: int, construct_net, construction_kwargs: dict)

A model grouping layers into an object with training/inference features.

Args

inputs: The input(s) of the model: a keras.Input object or a combination of keras.Input objects in a dict, list or tuple.
outputs: The output(s) of the model: a tensor that originated from keras.Input objects or a combination of such tensors in a dict, list or tuple. 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:

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:

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)

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().

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:

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().

Expand source code

class InvertibleNet(keras.Model):

    def __init__(self, num_blocks: int, construct_net, construction_kwargs: dict):
        super(InvertibleNet, self).__init__()
        self.num_blocks = num_blocks
        self.layer_dict = {}
        for i in range(num_blocks):
            self.layer_dict[f'coupling_block{i + 1}'] = CouplingLayer(construct_net, construction_kwargs, (i % 2 == 0))

    def call(self, x, backward=False):
        if backward:
            for i in range(self.num_blocks, 0, -1):
                x = self.layer_dict[f'coupling_block{i}'](x, backward)
        else:
            for i in range(1, self.num_blocks + 1):
                x = self.layer_dict[f'coupling_block{i}'](x)
        return x

Ancestors

keras.src.engine.training.Model
keras.src.engine.base_layer.Layer
tensorflow.python.module.module.Module
tensorflow.python.trackable.autotrackable.AutoTrackable
tensorflow.python.trackable.base.Trackable
keras.src.utils.version_utils.LayerVersionSelector
keras.src.utils.version_utils.ModelVersionSelector

Methods

def call(self, x, backward=False)

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).

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.

Returns

A tensor if there is a single output, or a list of tensors if there are more than one outputs.