Question

我有一个包含每日降水图图像的数据集。我正在尝试使用ConvLSTM2D构建降水临近预报模型。

请帮助我弄清楚如何改变阵列的形状。

以此作为参考：https://github.com/wqxu/ConvLSTM/blob/master/radar_forecast.py

请帮助我解决此错误：

Error when checking input: expected conv_lst_m2d_167_input to have 5 dimensions, but got array with shape (1785, 400, 320, 3)

如果我向X_Train的重塑数组添加另一个尺寸：

X_Train = np.array(X).reshape(-1, 400, 320, 1, 3)[:1785] # converting list to a numpy array

我收到以下错误[1785发生了什么事？]：

Error when checking input: expected conv_lst_m2d_167_input to have 5 dimensions, but got array with shape (400, 320, 1, 3)

如果我向ConvLSTM2D输入层的input_shape添加另一个尺寸：

model.add(ConvLSTM2D(filters=128, kernel_size=(3, 3), input_shape=(None, 400, 320, 1, 3), padding='same', return_sequences=True))

我收到以下错误：

ValueError: Input 0 is incompatible with layer conv_lst_m2d_5:expected ndim=5, found ndim=6

此刻，我正试图找到任何实际可行的情况，以便我可以从那里开始。如果可以的话，请帮忙。

基本上我想知道问题是否出在我数组的维数上？如果可以，该如何更改？

import numpy as np
import os
import cv2
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers.convolutional import Conv3D
from keras.layers.convolutional_recurrent import ConvLSTM2D
from keras.layers.normalization import BatchNormalization

DATADIR = "C:/Users/StephanIliffe/Documents/data"

training_data = []

def create_training_data():
    path = os.path.join(DATADIR, "Input") # path to jpg precipitation maps
    class_num = 1
    for img in os.listdir(path):
        img_array = cv2.imread(os.path.join(path, img))
        new_array = cv2.resize(img_array, (400, 320))
        training_data.append([new_array, class_num])  

create_training_data()

X = []
X_Train = []
X_Test = []

for features, label in training_data:
    X.append(features)

X_Train = np.array(X).reshape(-1, 400, 320, 3)[:1785] # converting list to a numpy array
X_Test = np.array(X).reshape(-1, 400, 320, 3)[765:] # converting list to a numpy array

model = Sequential()

model.add(ConvLSTM2D(filters=128, kernel_size=(3, 3),
                   input_shape=(None, 400, 320, 3),
                   padding='same', return_sequences=True))
model.add(BatchNormalization())

model.add(ConvLSTM2D(filters=128, kernel_size=(3, 3),
                   padding='same', return_sequences=True))
model.add(BatchNormalization())

model.add(ConvLSTM2D(filters=128, kernel_size=(3, 3),
                   padding='same', return_sequences=True))
model.add(BatchNormalization())

model.add(ConvLSTM2D(filters=128, kernel_size=(3, 3),
                 padding='same', return_sequences=True))
model.add(BatchNormalization())

model.add(Conv3D(filters=1, kernel_size=(3, 3, 3),
               activation='sigmoid',
               padding='same', data_format='channels_last'))

model.compile(loss='mean_squared_error', optimizer='adadelta')
model.fit(X_Train, X_Test, batch_size=30, epochs=10, validation_split=0.05)

Answer 1

好吧，这正是错误的意思：expected conv_lst_m2d_167_input to have 5 dimensions, but got array with shape (400, 320, 1, 3)。您的输入数据有四个维度。它必须有五个：(samples, time, rows, columns, channels)。

我为ConvLSTM模型调整大小/输入数组时收到错误

1 个答案: