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HenryAI
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KerasCodeExamples.txt

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Epoch 9/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0710 - sparse_categorical_accuracy: 0.9827 - val_loss: 0.1613 - val_sparse_categorical_accuracy: 0.9694
Epoch 10/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0633 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1463 - val_sparse_categorical_accuracy: 0.9758
Epoch 11/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0604 - sparse_categorical_accuracy: 0.9856 - val_loss: 0.1390 - val_sparse_categorical_accuracy: 0.9769
Epoch 12/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0561 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1761 - val_sparse_categorical_accuracy: 0.9740
Epoch 13/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0589 - sparse_categorical_accuracy: 0.9873 - val_loss: 0.1598 - val_sparse_categorical_accuracy: 0.9769
Epoch 14/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9879 - val_loss: 0.1565 - val_sparse_categorical_accuracy: 0.9802
Epoch 15/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0563 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.1970 - val_sparse_categorical_accuracy: 0.9758
Epoch 16/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0525 - sparse_categorical_accuracy: 0.9888 - val_loss: 0.1937 - val_sparse_categorical_accuracy: 0.9757
Epoch 17/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0522 - sparse_categorical_accuracy: 0.9898 - val_loss: 0.1777 - val_sparse_categorical_accuracy: 0.9797
Epoch 18/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0568 - sparse_categorical_accuracy: 0.9894 - val_loss: 0.1831 - val_sparse_categorical_accuracy: 0.9791
Epoch 19/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0526 - sparse_categorical_accuracy: 0.9900 - val_loss: 0.1812 - val_sparse_categorical_accuracy: 0.9782
Epoch 20/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0503 - sparse_categorical_accuracy: 0.9902 - val_loss: 0.2098 - val_sparse_categorical_accuracy: 0.9776
313/313 [==============================] - 0s 731us/step - loss: 0.2002 - sparse_categorical_accuracy: 0.9776
[0.20024622976779938, 0.9775999784469604]
Overview of how to use the TensorFlow NumPy API to write Keras models.
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[1]
self.block_weights = []
self.biases = []
for i, block in enumerate(self.blocks):
self.block_weights.append(
self.add_weight(
shape=(current_shape, block), trainable=True, name=f\"block-{i}\"
)
)
self.biases.append(
self.add_weight(shape=(block,), trainable=True, name=f\"bias-{i}\")
)
current_shape = block
self.linear_layer = self.add_weight(
shape=(current_shape, 1), name=\"linear_projector\", trainable=True
)
def call(self, inputs):
activations = inputs