One Hot Encoding with Sparse categorical entropy throwing error

Question

So I am doing the MNIST Fashion example for Keras. And in the program I wrote for it, I didn't need to use "to_categorical" to one hot encode my data, and it still worked. When i tried to one hot encode it, it did not work. I am confused why this happened, because usually one should one hot encode their outputs right? If someone could help clarify this that would be great!

from tensorflow.keras.datasets import fashion_mnist
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Activation
from tensorflow.keras import backend as K
from kerastuner import RandomSearch
import time
from tensorflow.keras.utils import to_categorical

from kerastuner.engine.hyperparameters import HyperParameters


LOG_DIR = f"{int(time.time())}"

(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
#

#
if K.image_data_format() == 'channels_first':
    input_shape = (1, x_train.shape[0], 28, 28)
    input_shap2 = (1, x_test.shape[0], 28, 28)
else:
    input_shape = (x_train.shape[0], 28, 28, 1)
    input_shap2 = (x_test.shape[0], 28, 28, 1)

y_train = to_categorical(y_train)<<<<<<<<<<<<<<<<<<<<<<
y_test = to_categorical(y_test) <<<<<<<<<<<<<<<<<<<<<<<<<<<

# plt.imshow(x_train[0], cmap='gray')
# plt.show()

x_train = x_train.reshape(input_shape)
x_test = x_test.reshape(input_shap2)


def build_model(hp):
    model = keras.models.Sequential()

    model.add(Conv2D(hp.Int("input_units", min_value=32, max_value=256, step=32), (3, 3), input_shape=(28, 28, 1)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    for i in range(hp.Int("n_layers", 1, 4)):
        model.add(Conv2D(hp.Int(f"conv_{i}_units", min_value=32, max_value=256, step=32), (3, 3)))
        model.add(Activation('relu'))
        # model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors

    model.add(Dense(10))
    model.add(Activation("softmax"))

    model.compile(optimizer="adam",
                  loss="sparse_categorical_crossentropy",
                  metrics=["accuracy"])
    return model

# model=build_model()
# model.fit(x_train, y_train, batch_size=64, epochs=8, validation_data = (x_test, y_test))

tuner = RandomSearch(
    build_model,
    objective="val_accuracy",
    max_trials=1,
    executions_per_trial=1,
    directory=LOG_DIR
)

tuner.search(
    x=x_train,
    y=y_train,
    epochs=1,
    batch_size=64,
    validation_data=(x_test,y_test)
)

Answer 1

You were using sparse-categorical-crossentropy instead of categorical-crossentropy. Sparse uses integer encoding whereas the other one uses one hot encoding. You should either use sparse and not one hot your labels, or use the other and one hot encode the labels