使用自定义数据集进行人脸识别而不是 MNIST

Question

我想使用一个自定义数据集，其中包含不同人的面部图像。我打算使用 CNN 和堆叠式自动编码器对我的图像进行分类。

我应该改变 (x_train, _), (x_test, _) = mnist.load_data() 吗？

或更改 input_img ，我认为问题出在输入数据上，但我不知道应该在哪里修改。

我迷路了，我需要帮助。

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K

input_img = Input(shape=(28, 28, 1))  # adapt this if using`channels_first` image data format

x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
 encoded = MaxPooling2D((2, 2), padding='same')(x)

# at this point the representation is (4, 4, 8) i.e. 128-dimensional

x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

from keras.datasets import mnist
import numpy as np

(x_train, _), (x_test, _) = mnist.load_data()

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))  # adapt this if 
using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if 
using `channels_first` image data format


from keras.callbacks import TensorBoard

autoencoder.fit(x_train, x_train,
               epochs=50,
               batch_size=128,
               shuffle=True,
               validation_data=(x_test, x_test),
               callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])

decoded_imgs = autoencoder.predict(x_test)

n = 10
import matplotlib.pyplot as plt

plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)

# display reconstruction
ax = plt.subplot(2, n, i + n)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()

Answer 1

您需要使用数据加载器更改 (x_train, _), (x_test, _) = mnist.load_data()。您可以使用 keras ImageDataGenerator 类来完成此操作或构建您的 own。如果您的图像尺寸比28 x 28 大得多，您可能还需要更改模型架构，因为直接将它们重塑为28 x 28 不会产生好的结果。

Answer 2

您需要加载数据集并将其拆分为两个子集：x_train 和 x_test。

您的数据以哪种格式存储？