Wert-Fehler: Eingabe-arrays müssen die gleiche Anzahl von samples, die als Ziel-arrays. Gefunden 1600-Eingang Proben-und 6400-target-Proben

Ich versuche zu tun, eine 8-Klasse Klassifizierung. Hier ist der code:

import keras
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
from keras import applications
from keras.optimizers import SGD
from keras import backend as K
K.set_image_dim_ordering('tf')
img_width, img_height = 48,48
top_model_weights_path = 'modelom.h5'
train_data_dir = 'chCdata1/train'
validation_data_dir = 'chCdata1/validation'
nb_train_samples = 6400
nb_validation_samples = 1600
epochs = 50
batch_size = 10
def save_bottlebeck_features():
   datagen = ImageDataGenerator(rescale=1. / 255)
   model = applications.VGG16(include_top=False, weights='imagenet', input_shape=(48,48,3))
   generator = datagen.flow_from_directory(
               train_data_dir,
               target_size=(img_width, img_height),
               batch_size=batch_size,
               class_mode='categorical',
               shuffle=False)
   bottleneck_features_train = model.predict_generator(
               generator, nb_train_samples // batch_size)
   np.save(open('bottleneck_features_train', 'wb'),bottleneck_features_train)

   generator = datagen.flow_from_directory(
               validation_data_dir,
               target_size=(img_width, img_height),
               batch_size=batch_size,
               class_mode='categorical',
               shuffle=False)
   bottleneck_features_validation = model.predict_generator(
               generator, nb_validation_samples // batch_size)
   np.save(open('bottleneck_features_validation', 'wb'),bottleneck_features_validation)

def train_top_model():
   train_data = np.load(open('bottleneck_features_train', 'rb'))
   train_labels = np.array([0] * (nb_train_samples // 8) + [1] * (nb_train_samples // 8) + [2] * (nb_train_samples // 8) + [3] * (nb_train_samples // 8) + [4] * (nb_train_samples // 8) + [5] * (nb_train_samples // 8) + [6] * (nb_train_samples // 8) + [7] * (nb_train_samples // 8))
   validation_data = np.load(open('bottleneck_features_validation', 'rb'))
   validation_labels = np.array([0] * (nb_train_samples // 8) + [1] * (nb_train_samples // 8) + [2] * (nb_train_samples // 8) + [3] * (nb_train_samples // 8) + [4] * (nb_train_samples // 8) + [5] * (nb_train_samples // 8) + [6] * (nb_train_samples // 8) + [7] * (nb_train_samples // 8))
   train_labels = keras.utils.to_categorical(train_labels, num_classes = 8)
   validation_labels = keras.utils.to_categorical(validation_labels, num_classes = 8)
   model = Sequential()
   model.add(Flatten(input_shape=train_data.shape[1:]))
   model.add(Dense(512, activation='relu'))
   model.add(Dropout(0.5))
   model.add(Dense(8, activation='softmax'))
   sgd = SGD(lr=1e-2, decay=0.00371, momentum=0.9, nesterov=False)
   model.compile(optimizer=sgd,
         loss='categorical_crossentropy', metrics=['accuracy'])
   model.fit(train_data, train_labels,
          epochs=epochs,
          batch_size=batch_size,
   validation_data=(validation_data, validation_labels))
   model.save_weights(top_model_weights_path)

save_bottlebeck_features()
train_top_model()

Habe ich Hinzugefügt die vollständige Liste der Fehler hier:

Traceback (most recent call last):

  File "<ipython-input-14-1d34826b5dd5>", line 1, in <module>
    runfile('C:/Users/rajaramans2/codes/untitled15.py', wdir='C:/Users/rajaramans2/codes')

  File "C:\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 866, in runfile
    execfile(filename, namespace)

  File "C:\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "C:/Users/rajaramans2/codes/untitled15.py", line 71, in <module>
    train_top_model()

  File "C:/Users/rajaramans2/codes/untitled15.py", line 67, in train_top_model
    validation_data=(validation_data, validation_labels))

  File "C:\Anaconda3\lib\site-packages\keras\models.py", line 856, in fit
    initial_epoch=initial_epoch)

  File "C:\Anaconda3\lib\site-packages\keras\engine\training.py", line 1449, in fit
    batch_size=batch_size)

  File "C:\Anaconda3\lib\site-packages\keras\engine\training.py", line 1317, in _standardize_user_data
    _check_array_lengths(x, y, sample_weights)

  File "C:\Anaconda3\lib\site-packages\keras\engine\training.py", line 235, in _check_array_lengths
    'and ' + str(list(set_y)[0]) + ' target samples.')

ValueError: Input arrays should have the same number of samples as target arrays. Found 1600 input samples and 6400 target samples.

Der "ValueError: Eingabe-arrays müssen die gleiche Anzahl von samples, die als Ziel-arrays. Gefunden 1600 input-Proben und 6400 target-Muster" erscheint. Bitte helfen Sie mit, die Lösung und die notwendigen änderungen am code. Vielen Dank im Voraus.