Spaces:
Runtime error
Runtime error
File size: 1,949 Bytes
9888dff 10432c9 9888dff 10432c9 9888dff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
from huggingface_hub import push_to_hub_keras
def to_numpy(examples):
examples["pixel_values"] = [np.array(image) for image in examples["image"]]
return examples
def preprocess():
test_dataset = load_dataset("active-learning/test_mnist")
train_dataset = load_dataset("active-learning/labeled_samples")
train_dataset = train_dataset.map(to_numpy, batched=True)
test_dataset = test_dataset.map(to_numpy, batched=True)
x_train = train_dataset["train"]["pixel_values"]
y_train = train_dataset["train"]["label"]
x_test = test_dataset["test"]["pixel_values"]
y_test = test_dataset["test"]["label"]
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
num_classes = 10
input_shape = (28, 28, 1)
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
return x_train, y_train, x_test, y_test
def training():
x_train, y_train, x_test, y_test = preprocess()
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(num_classes, activation="softmax"),
]
)
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=128, epochs=15, validation_split=0.1)
score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])
push_to_hub_keras(model, "active-learning/mnist_classifier")
|