Evaluate can be easily intergrated into your Keras and Tensorflow workflow. We’ll demonstrate two ways of incorporating Evaluate into model training, using the Fashion MNIST example dataset. We’ll train a standard classifier to predict two classes from this dataset, and show how to use a metric as a callback during training or afterwards for evaluation.
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
import evaluate
# We pull example code from Keras.io's guide on classifying with MNIST
# Located here: https://keras.io/examples/vision/mnist_convnet/
# Model / data parameters
input_shape = (28, 28, 1)
# Load the data and split it between train and test sets
(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()
# Only select tshirts/tops and trousers, classes 0 and 1
def get_tshirts_tops_and_trouser(x_vals, y_vals):
mask = np.where((y_vals == 0) | (y_vals == 1))
return x_vals[mask], y_vals[mask]
x_train, y_train = get_tshirts_tops_and_trouser(x_train, y_train)
x_test, y_test = get_tshirts_tops_and_trouser(x_test, y_test)
# Scale images to the [0, 1] range
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
model = keras.Sequential(
[
keras.Input(shape=input_shape),
layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Flatten(),
layers.Dropout(0.5),
layers.Dense(1, activation="sigmoid"),
]
)
Suppose we want to keep track of model metrics while a model is training. We can use a Callback in order to calculate this metric during training, after an epoch ends.
We’ll define a callback here that will take a metric name and our training data, and have it calculate a metric after the epoch ends.
class MetricsCallback(keras.callbacks.Callback):
def __init__(self, metric_name, x_data, y_data) -> None:
super(MetricsCallback, self).__init__()
self.x_data = x_data
self.y_data = y_data
self.metric_name = metric_name
self.metric = evaluate.load(metric_name)
def on_epoch_end(self, epoch, logs=dict()):
m = self.model
# Ensure we get labels of "1" or "0"
training_preds = np.round(m.predict(self.x_data))
training_labels = self.y_data
# Compute score and save
score = self.metric.compute(predictions = training_preds, references = training_labels)
logs.update(score)
We can pass this class to the callbacks
keyword-argument to use it during training:
batch_size = 128
epochs = 2
model.compile(loss="binary_crossentropy", optimizer="adam")
model_history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1,
callbacks = [MetricsCallback(x_data = x_train, y_data = y_train, metric_name = "accuracy")])
We can also use the same metric after model training! Here, we show how to check accuracy of the model after training on the test set:
acc = evaluate.load("accuracy")
# Round the predictions to turn them into "0" or "1" labels
test_preds = np.round(model.predict(x_test))
test_labels = y_test
print("Test accuracy is : ", acc.compute(predictions = test_preds, references = test_labels))
# Test accuracy is : 0.9855