执行 convLSTM2D() 时发生错误 "Function call stack: train_function"

Question

执行ConvLSTM2D时出错。

第一次实现时序数据分析。所以排列数据格式的程序可能是错误的。我不知道原因和解决办法，所以请帮助我...！

如果你运行这个程序，请准备5张图像“0.png”，“1.png” ...“4.png”在同一层级。 (Link here)

from scipy.sparse import dok
import tensorflow as tf
import numpy as np

from matplotlib import pyplot as plt
from keras.models import Model
from keras.preprocessing.image import img_to_array, load_img
from keras.layers import Input, Dense, Reshape ,ConvLSTM2D,BatchNormalization,Activation
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split

imgSize = 200
dataNum = 5

labels = list(range(dataNum, 0, -1))
imgs = []

for i in range(5):
    img = img_to_array(load_img(str(i)+'.png', target_size=(imgSize,imgSize)))
    imgs.append(img)


#-----------------------------------------------------------------------------------
# Arrange in a format that can be learned in Time-series
# original data format: List of the same number of labels(:labels) and images(:imgs)

# imgs:[image1,image2,image3,image4,image5...]
# labels:[100,110,150,140,160...]

n_seq = 2
n_sample = dataNum - n_seq
x = np.zeros((n_sample, n_seq, imgSize, imgSize, 3))
for i in range(n_sample):
    x[i] = imgs[i:i + n_seq]
del labels[0:n_seq]

#-----------------------------------------------------------------------------------
# Data-Split(val,test)
img_train, img_val, Label_train, Label_val = train_test_split(x,labels, test_size=0.6, shuffle=False)

#-----------------------------------------------------------------------------------
# Model and Training

inputL = Input(shape=(n_seq, imgSize, imgSize, 3))
x0 = ConvLSTM2D(filters=16, kernel_size=(3, 3), padding="same", return_sequences=True, data_format="channels_last")(inputL)
x0 = BatchNormalization(momentum=0.6)(x0)
x0 = ConvLSTM2D(filters=16, kernel_size=(3, 3), padding="same", return_sequences=True, data_format="channels_last")(x0)
x0 = BatchNormalization(momentum=0.8)(x0)
x0 = ConvLSTM2D(filters=3, kernel_size=(3, 3), padding="same", return_sequences=False, data_format="channels_last")(x0)
out = Activation('tanh')(x0)
outputL = Dense(1, name="a")(out)

model = Model(inputs=inputL, outputs=outputL)
model.compile(Adam(learning_rate=0.01),loss= {'a': 'mae'},metrics= {'a': 'mae'})

history = model.fit([np.array(img_train)], [np.array(Label_train)],
                    epochs=50, batch_size=16,
                    validation_data=([np.array(img_val)],[np.array(Label_val)]))

错误信息↓

Traceback (most recent call last):
  File "c:/Users/UserA/StudyAI/LSTM.py", line 189, in <module>
    history = model.fit([np.array(img_train)], [np.array(Label_train)],
  File "C:\Users\UserA\anaconda3\lib\site-packages\keras\engine\training.py", line 1158, in fit
    tmp_logs = self.train_function(iterator)
  File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 950, in _call
    return self._stateless_fn(*args, **kwds)
  File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 3023, in __call__
    return graph_function._call_flat(
  File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 1960, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 591, in call
    outputs = execute.execute(
  File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError:  required broadcastable shapes at loc(unknown)
         [[node mean_absolute_error_1/sub (defined at C:\Users\UserA\anaconda3\lib\site-packages\keras\losses.py:1301) ]] [Op:__inference_train_function_10812]

Errors may have originated from an input operation.
Input Source operations connected to node mean_absolute_error_1/sub:
 ExpandDims_1 (defined at C:\Users\UserA\anaconda3\lib\site-packages\keras\engine\data_adapter.py:1414)
 model/b/BiasAdd (defined at C:\Users\UserA\anaconda3\lib\site-packages\keras\layers\core.py:1233)

Function call stack:
train_function

Answer 1

您的模型输出是 (200,200,1)，因为您将超过 2 维的张量传递给密集层，所以您将在输出时获得所有其他维度，而不仅仅是 1 (Dense(1))。

您可以通过在最后一层之前添加 Flatten 层来解决此问题：

inputL = Input(shape=(n_seq, imgSize, imgSize, 3))
x0 = ConvLSTM2D(filters=16, kernel_size=(3, 3), padding="same", return_sequences=True, data_format="channels_last")(inputL)
x0 = BatchNormalization(momentum=0.6)(x0)
x0 = ConvLSTM2D(filters=16, kernel_size=(3, 3), padding="same", return_sequences=True, data_format="channels_last")(x0)
x0 = BatchNormalization(momentum=0.8)(x0)
x0 = ConvLSTM2D(filters=3, kernel_size=(3, 3), padding="same", return_sequences=False, data_format="channels_last")(x0)
x0 = Activation('tanh')(x0)        #Alter this
out = keras.layers.Flatten()(x0)   #Add this
outputL = Dense(1, name="a")(out)

P.S：使用 model.summary() 调查模型的输出。

执行 convLSTM2D() 时发生错误 "Function call stack: train_function"

Error "Function call stack: train_function" occurred in implementation of convLSTM2D()

python

conv-neural-network

lstm

keras

tensorflow