问Tensorflow图像分类Python总是说相同的答案

EN

#required imports

#using sequential from tensorflow 

from keras.models import Sequential

from keras.layers import Conv2D

from keras.layers import MaxPooling2D

from keras.layers import Flatten

from keras.layers import Dense


#classification model to be sequential
classifier = Sequential()

classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))

#output layer
classifier.add(Dense(units = 1, activation = 'sigmoid'))

#compilation
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

#training
from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, 
zoom_range = 0.2, horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory("/home/jovyan/dataset/training_set/", target_size = (64, 64), batch_size = 32, class_mode = 'binary')

test_set = test_datagen.flow_from_directory("/home/jovyan/dataset/test_set/", target_size = (64, 64), batch_size = 32, class_mode = 'binary')

classifier.fit_generator(training_set, steps_per_epoch = 85, epochs = 25, validation_data=test_set, validation_steps=2000)

#predictions
import numpy as np
from keras.preprocessing import image
test_image=image.load_img("/home/jovyan/dataset/test_set/test_bad_1.jpg", target_size=(64, 64))
test_image=image.img_to_array(test_image)
test_image=np.expand_dims(test_image, axis=0)
result=classifier.predict(test_image)
training_set.class_indices

if result[0][0]==1:
    prediction='good'
else:
    prediction='bad'

print(prediction)`