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我的预测输出有问题。我留下了我的分类器的模型代码（6个类）我正在使用Tensorflow 2.2代码是：

import tensorflow as tf
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow import keras
import cv2
import PIL
import os
import numpy as np

#Dir
train_dir ='/home/Tensorflow/Dataset_mix/train/'
validation_dir = '/home/Tensorflow/Dataset_mix/test/'

ACCURACY_THRESHOLD = 0.99
class myCallback(tf.keras.callbacks.Callback): 
    def on_epoch_end(self, epoch, logs={}): 
        if(logs.get('accuracy') > ACCURACY_THRESHOLD):   
            print("\nReached %2.2f%% accuracy, so stopping training!!" %(ACCURACY_THRESHOLD*100))   
            self.model.stop_training = True

train_0_dir = os.path.join(train_dir, '0')  
train_1_dir = os.path.join(train_dir, '1')
train_2_dir = os.path.join(train_dir, '2')  
train_3_dir = os.path.join(train_dir, '3')  
train_4_dir = os.path.join(train_dir, '4')  
train_5_dir = os.path.join(train_dir, '5')  
validation_0_dir = os.path.join(validation_dir, '0')  
validation_1_dir = os.path.join(validation_dir, '1')  
validation_2_dir = os.path.join(validation_dir, '2')  
validation_3_dir = os.path.join(validation_dir, '3')  
validation_4_dir = os.path.join(validation_dir, '4')  
validation_5_dir = os.path.join(validation_dir, '5')  


num_0_tr = len(os.listdir(train_0_dir))
num_1_tr = len(os.listdir(train_1_dir))
num_2_tr = len(os.listdir(train_2_dir))
num_3_tr = len(os.listdir(train_3_dir))
num_4_tr = len(os.listdir(train_4_dir))
num_5_tr = len(os.listdir(train_5_dir))

num_0_val = len(os.listdir(validation_0_dir))
num_1_val = len(os.listdir(validation_1_dir))
num_2_val = len(os.listdir(validation_2_dir))
num_3_val = len(os.listdir(validation_3_dir))
num_4_val = len(os.listdir(validation_4_dir))
num_5_val = len(os.listdir(validation_5_dir))

total_train = num_0_tr + num_1_tr + num_2_tr + num_3_tr + num_4_tr + num_5_tr
total_val = num_0_val + num_1_val + num_2_val + num_3_val + num_4_val + num_5_val

batch_size = 32
epochs = 20
IMG_HEIGHT = 128
IMG_WIDTH = 128
callbacks = myCallback()
CLASS_NAMES = ['0','1','2','3','4','5']

train_image_generator = ImageDataGenerator(rescale=1./255, zoom_range=0.25, horizontal_flip=True, height_shift_range=.15, rotation_range=75, brightness_range=(0.25,0.9)) # Generator for our training data
validation_image_generator = ImageDataGenerator(rescale=1./255) # Generator for our validation data

train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
                                                           directory=train_dir,
                                                           color_mode='grayscale',
                                                           shuffle=True,
                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                           classes = list(CLASS_NAMES))

val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size,
                                                              directory=validation_dir,
                                                              color_mode='grayscale',
                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),                                                              
                                                              classes = list(CLASS_NAMES))


model = Sequential([
    Conv2D(16, 3, activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,1)), #(W-F)/S +1 (128-3)/1 + 1 = 126  (32, 126, 126)
    Conv2D(16, 3, activation='relu'), #(32, 124, 124)
    MaxPooling2D(), #(32, 62. 62)
    Conv2D(32, 3, activation='relu'), #   (64, 60, 60)
    Conv2D(32, 3, activation='relu'), #   (64, 58, 58)
    MaxPooling2D(),   #   (64, 26, 26)
    Conv2D(64, 3, activation='relu'), #   (128, 24, 24)
    Conv2D(64, 3, activation='relu'), #   (128, 22, 22)
    MaxPooling2D(),   #   (128, 11, 11)
    Conv2D(128, 3, activation='relu'), #   (256, 9, 9)
    Conv2D(128, 3, activation='relu'), #   (256, 7, 7)
    MaxPooling2D(),   #   (256, 3, 3)
    Flatten(),
    Dense(1152, activation='relu'),
    Dropout(0.3),
    Dense(1152, activation='relu'),
    Dropout(0.3),
    Dense(6, activation='softmax')

])

opt = keras.optimizers.Adam(learning_rate=0.0001)
model.compile(optimizer=opt,
              loss='categorical_crossentropy',
              metrics=['accuracy'])


model.summary()

history = model.fit(
    train_data_gen,
    steps_per_epoch=total_train // batch_size,
    epochs=epochs,
    validation_data=val_data_gen,
    validation_steps=total_val // batch_size,
    callbacks= callbacks,
    verbose=1
)

model.save('saved_model/ModelMix')
model.save('Modelmix.h5')

而对于预测：

image_to_predict = cv2.imread('./image054.png')

image_to_predict = cv2.cvtColor(image_to_predict, cv2.COLOR_BGR2GRAY)
image_to_predict = image_to_predict.reshape(1, 128, 128, 1)
print(image_to_predict.shape)

prediction = model.predict(image_to_precit)

print(prediction)

好吧，当我执行 model.predict(image_to_predict) 并打印结果时，返回给我的输出如下：

[[0. 0. 0. 1. 0. 0.]]

输出是正确的，检测到的类是预期的，但这不是我想要的。我需要有一个输出，可以让我恢复做出选择的信心百分比，例如：

[[0.02 0.04 0.10 0.72 0.08 0.04]]

但我找不到任何能让我确定评估的功能。有人知道我该怎么做吗？

4

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