我正在尝试构建一个自动编码器，但我遇到了以下错误，我不知道为什么。
ValueError：检查输入时出错：预期input_1具有形状(2562563(，但得到的数组具有形状(256 2564(
如果我打印图像形状，我得到(2562563(，但我仍然得到关于形状的错误。
任何帮助都将是美妙的。
Ubuntu 18.04|Python 3.7.6|Tensorflow 2.1
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, Dropout, Conv2DTranspose, UpSampling2D, add
from tensorflow.keras.models import Model
from tensorflow.keras import regularizers
import os
import re
from scipy import ndimage, misc
from skimage.transform import resize, rescale
from matplotlib import pyplot
import numpy as np
#Functions
def train_batches(just_load_dataset=False):
batches = 256 # Number of images to have at the same time in a batch
batch = 0 # Number if images in the current batch (grows over time and then resets for each batch)
batch_nb = 0 # Batch current index
max_batches = -1 # If you want to train only on a limited number of images to finish the training even faster.
ep = 4 # Number of epochs
images = []
x_train_n = []
x_train_down = []
x_train_n2 = [] # Resulting high res dataset
x_train_down2 = [] # Resulting low res dataset
for root, dirnames, filenames in os.walk(input_dir):
for filename in filenames:
if re.search(".(jpg|jpeg|JPEG|png|bmp|tiff)$", filename):
if batch_nb == max_batches: # If we limit the number of batches, just return earlier
return x_train_n2, x_train_down2
filepath = os.path.join(root, filename)
image = pyplot.imread(filepath)
if len(image.shape) > 2:
image_resized = resize(image, (256, 256))
x_train_n.append(image_resized)
x_train_down.append(rescale(rescale(image_resized, 0.5), 2.0))
batch += 1
if batch == batches:
batch_nb += 1
x_train_n2 = np.array(x_train_n)
x_train_down2 = np.array(x_train_down)
if just_load_dataset:
return x_train_n2, x_train_down2
print('Training batch', batch_nb, '(', batches, ')')
autoencoder.fit(x_train_down2, x_train_n2, epochs=ep, batch_size=10, shuffle=True, validation_split=0.15)
x_train_n = []
x_train_down = []
batch = 0
return x_train_n2, x_train_down2
#Script
input_dir="/mnt/vanguard/datasets/ffhq-dataset/thumbnails256x256"
n = 256
chan = 3
input_img = Input(shape=(n, n, chan))
# Encoder
l1 = Conv2D(64, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(input_img)
l2 = Conv2D(64, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l1)
l3 = MaxPooling2D(padding='same')(l2)
l3 = Dropout(0.3)(l3)
l4 = Conv2D(128, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l3)
l5 = Conv2D(128, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l4)
l6 = MaxPooling2D(padding='same')(l5)
l7 = Conv2D(256, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l6)
# Decoder
l8 = UpSampling2D()(l7)
l9 = Conv2D(128, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l8)
l10 = Conv2D(128, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l9)
l11 = add([l5, l10])
l12 = UpSampling2D()(l11)
l13 = Conv2D(64, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l12)
l14 = Conv2D(64, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l13)
l15 = add([l14, l2])
#chan = 3, for RGB
decoded = Conv2D(chan, (3, 3), padding='same', activation='relu', activity_regularizer=regularizers.l1(10e-10))(l15)
# Create neural network
autoencoder = Model(input_img, decoded)
autoencoder_hfenn = Model(input_img, decoded)
autoencoder.summary()
autoencoder.compile(optimizer='adadelta', loss='mean_squared_error')
x_train_n = []
x_train_down = []
x_train_n, x_train_down = train_batches()
重新缩放x_train_down.append(rescale(rescale(image_resized, 0.5), 2.0))导致问题。OpenCV可用于降低图像质量：
small = cv2.resize(image_resized, (0,0), fx=0.5, fy=0.5)
large = cv2.resize(small, (0,0), fx=2.0, fy=2.0)
此外，请注意，这是GPU密集型计算。要么应该缩小图像大小，要么尝试具有更多内存的GPU(K80(。