我如何修复网络输入形状

Question

There is 1275 images which each image has dimension of (128,19,1). these images are divided into groups of five, so there is 255 (1275/5) samples which each sample has 5 images and final shape of data is (255, 5, 128, 19, 1). this data must be feed to a CONVLSTM2D.network which its code is as below. the process of training is done completely but at start of evaluation process it gives below error. thanks if anyone can helps me to fix it.

Error:

IndexError: list index out of range

File "", line 1, in runfile('D:/thesis/Paper 3/Feature Extraction/two_dimension_Feature_extraction/stft_feature/Training_set/P300/Afrah_convlstm2d.py', wdir='D:/thesis/Paper 3/Feature Extraction/two_dimension_Feature_extraction/stft_feature/Training_set/P300')

File "C:\Users\pouyaandish\AppData\Local\conda\conda\envs\kafieh\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 786, in runfile execfile(filename, namespace)

File "C:\Users\pouyaandish\AppData\Local\conda\conda\envs\kafieh\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile exec(compile(f.read(), filename, 'exec'), namespace)

File "D:/thesis/Paper 3/Feature Extraction/two_dimension_Feature_extraction/stft_feature/Training_set/P300/Afrah_convlstm2d.py", line 111, in test_loss, test_acc = seq.evaluate(test_data)

File "C:\Users\pouyaandish\AppData\Local\conda\conda\envs\kafieh\lib\site-packages\keras\engine\training.py", line 1361, in evaluate callbacks=callbacks)

File "C:\Users\pouyaandish\AppData\Local\conda\conda\envs\kafieh\lib\site-packages\keras\engine\training_arrays.py", line 403, in test_loop if issparse(ins[i]) and not K.is_sparse(feed[i]):

IndexError: list index out of range

#Importing libraries
#-------------------------------------------------
from PIL import Image
from keras.models import Sequential
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers.convolutional_recurrent import ConvLSTM2D
from keras.layers.normalization import BatchNormalization
import numpy as np
import os
from matplotlib import pyplot as plt


#Data Preprocessing
#-----------------------------------------------------------------
Data = np.zeros((255,5,128,19,1),dtype=np.uint8)

image_folder = 'D:\\thesis\\Paper 3\\Feature Extraction\\two_dimension_Feature_extraction\\stft_feature\\Training_set\\P300'
images = [img for img in os.listdir(image_folder) if img.endswith(".png")]

for image in images:
    img = Image.open(image).convert('L')
    array = np.array(img)
    array = np.expand_dims(np.array(img), axis=2)
    for i in range(0, len(Data)):
        for j in range(0, 4):
            Data[i,j] = array

           

labels = np.zeros((2,len(Data)), dtype=np.uint8)
labels = np.transpose(labels)
for i in range(0, len(Data) ):
    if i <= 127:
        labels[i][0] = 1
    elif i > 127 :
        labels[i][1] = 1            
            
#Network Configuration
#--------------------------------------------------------------------------------------------------------------------------
seq = Sequential()
seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
                   input_shape=(5, 128, 19, 1),
                   padding='same', return_sequences=True))
seq.add(BatchNormalization())

seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
                   padding='same', return_sequences=True))
seq.add(BatchNormalization())

seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
                   padding='same', return_sequences=True))
seq.add(BatchNormalization())

seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
                   padding='same', return_sequences=True))
seq.add(BatchNormalization())

seq.add(Flatten())
seq.add(Dense(output_dim = 128, activation = 'relu'))
seq.add(Dense(output_dim = 2, activation = 'relu'))
seq.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['acc'])

#Fit the Data on Model
#--------------------------------------------------------------------------------------
train_data_1 = Data[0:84]
train_data_2 = Data[127:212]
train_data = np.concatenate([train_data_1, train_data_2])
label_train_1 = labels[0:84]
label_train_2 = labels[127:212]
label_train = np.concatenate([label_train_1, label_train_2])

val_data_1 = Data[84:104]
val_data_2 = Data[212:232]
val_data = np.concatenate([val_data_1, val_data_2])
label_val_1 = labels[84:104]
label_val_2 = labels[212:232]
label_val = np.concatenate([label_val_1, label_val_2])


test_data_1 = Data[104:127]
test_data_2 = Data[232:]
test_data = np.concatenate([test_data_1, test_data_2])
label_test_1 = labels[104:127]
label_test_2 = labels[232:]
label_test = np.concatenate([label_test_1, label_test_2])


history = seq.fit(train_data,label_train, validation_data=( val_data, label_val), epochs = 2 , batch_size = 10)

#Visualize the Result
#---------------------------------------------------------------------------------------
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'r', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'r', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.plot()
plt.legend()
plt.show()
#Evaluate Model on test Data
#----------------------------------------------------------------------------------------------
test_loss, test_acc = seq.evaluate(test_data)
print('test_acc:', test_acc)





     

Answer 1

The problem is at the end, when you evaluate your model, you just forgot to give the y argument. This modification should fix the problem:

test_loss, test_acc = seq.evaluate(test_data, y=label_test)