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我正在尝试使用以下ConvLSTM2D架构来估计低分辨率图像序列的高分辨率图像序列:

import numpy as np, scipy.ndimage, matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, ConvLSTM2D, MaxPooling2D, UpSampling2D
from sklearn.metrics import accuracy_score, confusion_matrix, cohen_kappa_score
from sklearn.preprocessing import MinMaxScaler, StandardScaler
np.random.seed(123)

raw = np.arange(96).reshape(8,3,4)
data1 = scipy.ndimage.zoom(raw, zoom=(1,100,100), order=1, mode='nearest') #low res
print (data1.shape)
#(8, 300, 400)

data2 = scipy.ndimage.zoom(raw, zoom=(1,100,100), order=3, mode='nearest') #high res
print (data2.shape)
#(8, 300, 400)

X_train = data1.reshape(data1.shape[0], 1, data1.shape[1], data1.shape[2], 1)
Y_train = data2.reshape(data2.shape[0], …