Question

我正在使用自动编码器进行图像去噪（使用keras tensorflow后端）。

当我训练我的模型时，损失率非常高且稳定（大约2.x左右）。

我无法理解我做错了什么。

这是我的代码：

from keras.layers import Input, Dense, Convolution2D, MaxPooling2D,UpSampling2D
from keras.models import Model
from keras.datasets import mnist
from keras.callbacks import TensorBoard
import matplotlib.pyplot as plt
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))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))

# adding noise to images
noise_factor = 0.5
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) 
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) 

x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)


input_img = Input(shape=(28, 28, 1))

x = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(input_img)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(x)
encoded = MaxPooling2D((2, 2), border_mode='same')(x)

# at this point the representation is (32, 7, 7)

x = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Convolution2D(1, 3, 3, activation='sigmoid', border_mode='same')(x)

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


autoencoder.fit(x_train_noisy, x_train,
                nb_epoch=100,
                batch_size=128,
                shuffle=True,
                validation_data=(x_test_noisy, x_test))

有什么建议吗？

训练我的模型时损失率很高

0 个答案: