» Code examples / Quick Keras Recipes / Writing Keras Models With TensorFlow NumPy

Writing Keras Models With TensorFlow NumPy

Author: lukewood
Date created: 2021/08/28
Description: Overview of how to use the TensorFlow NumPy API to write Keras models. View in Colab GitHub source

Introduction

NumPy is a hugely successful Python linear algebra library.

TensorFlow recently launched tf_numpy, a TensorFlow implementation of a large subset of the NumPy API. Thanks to tf_numpy, you can write Keras layers or models in the NumPy style!

The TensorFlow NumPy API has full integration with the TensorFlow ecosystem. Features such as automatic differentiation, TensorBoard, Keras model callbacks, TPU distribution and model exporting are all supported.

Let's run through a few examples.

Setup

TensorFlow NumPy requires TensorFlow 2.5 or later.

import tensorflow as tf
import tensorflow.experimental.numpy as tnp
import keras
import keras.layers as layers
import numpy as np

Optionally, you can call tnp.experimental_enable_numpy_behavior() to enable type promotion in TensorFlow. This allows TNP to more closely follow the NumPy standard.

tnp.experimental_enable_numpy_behavior()

To test our models we will use the Boston housing prices regression dataset.

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.boston_housing.load_data(
path="boston_housing.npz", test_split=0.2, seed=113
)

def evaluate_model(model: keras.Model):
[loss, percent_error] = model.evaluate(x_test, y_test, verbose=0)
print("Mean absolute percent error before training: ", percent_error)
model.fit(x_train, y_train, epochs=200, verbose=0)
[loss, percent_error] = model.evaluate(x_test, y_test, verbose=0)
print("Mean absolute percent error after training:", percent_error)

Subclassing keras.Model with TNP

The most flexible way to make use of the Keras API is to subclass the [keras.Model](/api/models/model#model-class) class. Subclassing the Model class gives you the ability to fully customize what occurs in the training loop. This makes subclassing Model a popular option for researchers.

In this example, we will implement a Model subclass that performs regression over the boston housing dataset using the TNP API. Note that differentiation and gradient descent is handled automatically when using the TNP API alongside keras.

First let's define a simple TNPForwardFeedRegressionNetwork class.

class TNPForwardFeedRegressionNetwork(keras.Model):
def __init__(self, blocks=None, **kwargs):
super(TNPForwardFeedRegressionNetwork, self).__init__(**kwargs)
if not isinstance(blocks, list):
raise ValueError(f"blocks must be a list, got blocks={blocks}")
self.blocks = blocks
self.block_weights = None
self.biases = None

def build(self, input_shape):
current_shape = input_shape
self.block_weights = []
self.biases = []
for i, block in enumerate(self.blocks):
self.block_weights.append(
shape=(current_shape, block), trainable=True, name=f"block-{i}"
)
)
self.biases.append(
)
current_shape = block

shape=(current_shape, 1), name="linear_projector", trainable=True
)

def call(self, inputs):
activations = inputs
for w, b in zip(self.block_weights, self.biases):
activations = tnp.matmul(activations, w) + b
# ReLu activation function
activations = tnp.maximum(activations, 0.0)

return tnp.matmul(activations, self.linear_layer)

Just like with any other Keras model we can utilize any supported optimizer, loss, metrics or callbacks that we want.

Let's see how the model performs!

model = TNPForwardFeedRegressionNetwork(blocks=[3, 3])
model.compile(
loss="mean_squared_error",
metrics=[keras.metrics.MeanAbsolutePercentageError()],
)
evaluate_model(model)
Mean absolute percent error before training:  422.45343017578125
Mean absolute percent error after training: 97.24715423583984

Great! Our model seems to be effectively learning to solve the problem at hand.

We can also write our own custom loss function using TNP.

def tnp_mse(y_true, y_pred):
return tnp.mean(tnp.square(y_true - y_pred), axis=0)

keras.backend.clear_session()
model = TNPForwardFeedRegressionNetwork(blocks=[3, 3])
model.compile(
loss=tnp_mse,
metrics=[keras.metrics.MeanAbsolutePercentageError()],
)
evaluate_model(model)
Mean absolute percent error before training:  79.84039306640625
Mean absolute percent error after training: 28.658035278320312

Implementing a Keras Layer Based Model with TNP

If desired, TNP can also be used in layer oriented Keras code structure. Let's implement the same model, but using a layered approach!

def tnp_relu(x):
return tnp.maximum(x, 0)

class TNPDense(keras.layers.Layer):
def __init__(self, units, activation=None):
super().__init__()
self.units = units
self.activation = activation

def build(self, input_shape):
name="weights",
shape=(input_shape, self.units),
initializer="random_normal",
trainable=True,
)
name="bias",
shape=(self.units,),
initializer="random_normal",
trainable=True,
)

def call(self, inputs):
outputs = tnp.matmul(inputs, self.w) + self.bias
if self.activation:
return self.activation(outputs)
return outputs

def create_layered_tnp_model():
return keras.Sequential(
[
TNPDense(3, activation=tnp_relu),
TNPDense(3, activation=tnp_relu),
TNPDense(1),
]
)

model = create_layered_tnp_model()
model.compile(
loss="mean_squared_error",
metrics=[keras.metrics.MeanAbsolutePercentageError()],
)
model.build((None, 13,))
model.summary()

evaluate_model(model)
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
tnp_dense (TNPDense)         (None, 3)                 42
_________________________________________________________________
tnp_dense_1 (TNPDense)       (None, 3)                 12
_________________________________________________________________
tnp_dense_2 (TNPDense)       (None, 1)                 4
=================================================================
Total params: 58
Trainable params: 58
Non-trainable params: 0
_________________________________________________________________
Mean absolute percent error before training:  101.17143249511719
Mean absolute percent error after training: 23.479856491088867

You can also seamlessly switch between TNP layers and native Keras layers!

def create_mixed_model():
return keras.Sequential(
[
TNPDense(3, activation=tnp_relu),
# The model will have no issue using a normal Dense layer
layers.Dense(3, activation="relu"),
# ... or switching back to tnp layers!
TNPDense(1),
]
)

model = create_mixed_model()
model.compile(
loss="mean_squared_error",
metrics=[keras.metrics.MeanAbsolutePercentageError()],
)
model.build((None, 13,))
model.summary()

evaluate_model(model)
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
tnp_dense_3 (TNPDense)       (None, 3)                 42
_________________________________________________________________
dense (Dense)                (None, 3)                 12
_________________________________________________________________
tnp_dense_4 (TNPDense)       (None, 1)                 4
=================================================================
Total params: 58
Trainable params: 58
Non-trainable params: 0
_________________________________________________________________
Mean absolute percent error before training:  104.59967041015625
Mean absolute percent error after training: 27.712949752807617

The Keras API offers a wide variety of layers. The ability to use them alongside NumPy code can be a huge time saver in projects.

Distribution Strategy

TensorFlow NumPy and Keras integrate with TensorFlow Distribution Strategies. This makes it simple to perform distributed training across multiple GPUs, or even an entire TPU Pod.

gpus = tf.config.list_logical_devices("GPU")
if gpus:
strategy = tf.distribute.MirroredStrategy(gpus)
else:
# We can fallback to a no-op CPU strategy.
strategy = tf.distribute.get_strategy()
print("Running with strategy:", str(strategy.__class__.__name__))

with strategy.scope():
model = create_layered_tnp_model()
model.compile(
loss="mean_squared_error",
metrics=[keras.metrics.MeanAbsolutePercentageError()],
)
model.build((None, 13,))
model.summary()
evaluate_model(model)
Running with strategy: _DefaultDistributionStrategy
Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
tnp_dense_5 (TNPDense)       (None, 3)                 42
_________________________________________________________________
tnp_dense_6 (TNPDense)       (None, 3)                 12
_________________________________________________________________
tnp_dense_7 (TNPDense)       (None, 1)                 4
=================================================================
Total params: 58
Trainable params: 58
Non-trainable params: 0
_________________________________________________________________
Mean absolute percent error before training:  100.5331039428711
Mean absolute percent error after training: 20.71842384338379

TensorBoard Integration

One of the many benefits of using the Keras API is the ability to monitor training through TensorBoard. Using the TensorFlow NumPy API alongside Keras allows you to easily leverage TensorBoard.

keras.backend.clear_session()

To load the TensorBoard from a Jupyter notebook, you can run the following magic:

models = [
(TNPForwardFeedRegressionNetwork(blocks=[3, 3]), "TNPForwardFeedRegressionNetwork"),
(create_layered_tnp_model(), "layered_tnp_model"),
(create_mixed_model(), "mixed_model"),
]
for model, model_name in models:
model.compile(
loss="mean_squared_error",
metrics=[keras.metrics.MeanAbsolutePercentageError()],
)
model.fit(
x_train,
y_train,
epochs=200,
verbose=0,
callbacks=[keras.callbacks.TensorBoard(log_dir=f"logs/{model_name}")],
)

To load the TensorBoard from a Jupyter notebook you can use the %tensorboard magic:

%tensorboard --logdir logs

The TensorBoard monitor metrics and examine the training curve. The TensorBoard also allows you to explore the computation graph used in your models. The ability to introspect into your models can be valuable during debugging.

Conclusion

Porting existing NumPy code to Keras models using the tensorflow_numpy API is easy! By integrating with Keras you gain the ability to use existing Keras callbacks, metrics and optimizers, easily distribute your training and use Tensorboard.

Migrating a more complex model, such as a ResNet, to the TensorFlow NumPy API would be a great follow up learning exercise.

Several open source NumPy ResNet implementations are available online.