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tensorflow - 一种带有稀疏分类熵抛出错误的热编码

问题描述

所以我正在为 Keras 做 MNIST 时尚示例。在我为它编写的程序中，我不需要使用“to_categorical”对我的数据进行一次热编码，它仍然有效。当我尝试对其进行热编码时，它不起作用。我很困惑为什么会发生这种情况，因为通常应该对他们的输出进行热编码，对吗？如果有人可以帮助澄清这一点，那就太好了！

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)
)

标签： tensorflowkerasconv-neural-networkmnistone-hot-encoding

解决方案

您使用的是稀疏分类交叉熵而不是分类交叉熵。稀疏使用整数编码，而另一种使用一种热编码。您应该使用稀疏而不是一个热标签，或者使用另一个和一个热编码标签

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