CNN model 预测任何输入的相同 output

Question

enter code here
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import MaxPooling2D
classifier = Sequential()
classifier.add(Convolution2D(32,(3,3),input_shape = (64,64,3), activation = 'relu'))

classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Convolution2D(32,(3,3), activation = 'relu'))

classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Flatten())
classifier.add(Dense(units=32,activation = 'relu'))
classifier.add(Dense(units=64,activation = 'relu'))

classifier.add(Dense(units=128,activation = 'relu'))

classifier.add(Dense(units=256,activation = 'relu'))

classifier.add(Dense(units=256,activation = 'relu'))

classifier.add(Dense(units=6,activation = 'softmax'))

classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])

from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255, # To rescaling the image in range of [0,1]
                               shear_range = 0.2, # To randomly shear the images 
                               zoom_range = 0.2, # To randomly zoom the images
                               horizontal_flip = True) #  for randomly flipping half of the images 
horizontally 

test_datagen = ImageDataGenerator(rescale = 1./255)
print("\nTraining the data...\n")
training_set = train_datagen.flow_from_directory('train',
                                            target_size=(64,64),
                                            batch_size=12, #Total no. of batches
                                            class_mode='categorical')

test_set = test_datagen.flow_from_directory('test',
                                        target_size=(64,64),
                                        batch_size=12,
                                        class_mode='categorical')

classifier.fit_generator(training_set,
                     steps_per_epoch=len(training_set), # Total training images
                     epochs = 20, # Total no. of epochs
                     validation_data = test_set,
                     validation_steps = len(test_set)) # Total testing images

classifier.save("model.h5")


#Prediction
classes = ['Fresh Apple','Fresh Banana','Fresh Orange','Rotten Apple','Rotten Banana','Rotten 
Orange']
from keras.preprocessing import image
from keras.models import load_model
import numpy as np
new_model = load_model('model.h5')
filename = 'a1.jpeg'
new_model.summary()
test_image = image.load_img('images\\a1.jpg',target_size=(64,64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = new_model(test_image)
result1 = result[0]
for i in range(6):
  if result1[i] == 1.:
    break;
prediction = classes[i]
print(prediction)

My model is giving the same output for any input. The errors and warnings have been removed but the output still remains the same. Earlier the model was giving same value 'A'(example) before removing Warnings and after removing Warnings, the model is giving same value 'B'. I don't know where is the problem in my code whether it is in model or whether it is in #Prediction.

Answer 1

A couple of things. In your generators you set a batch size of 12. then in model.fit you have steps_per_epoch=len(training_set). This means you will go through your training set 12 times per epoch. I usually leave steps per epoch and validation steps as None. model.fit will determine the value internally but if you want to then set

steps_per_epoch = int(len(train_set/batch_size) + 1
validation_steps= int(len(test_set/batch_size) +1

Now in predictions. You scaled your train and test images by 1/255. You need to do the same for images you wish to predict. So right after the code to expand dimension add code

test_image=test_image/255