预训练的 keras 模型在 android 中返回相同的结果

我在 Keras 中创建了一个图像分类器，后来我以pb格式保存模型以在 android 中使用它。

但是，在python代码中，它可以正确地对图像进行分类。但是在 android 中，无论我输入什么图像，输出总是相同的。

这就是我训练模型的方式

rom keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

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

# Part 2 - Fitting the CNN to the images

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('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

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

classifier.fit_generator(training_set,
                         steps_per_epoch = 8000,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 2000)
classifier.summary()
classifier.save('saved_model.h5')

后来我saved_model.h5使用这个将keras模型（）转换为tensorflow模型

这就是我转换位图浮点数组的方式

    public static float[] getPixels(Bitmap bitmap) {

        final int IMAGE_SIZE = 64;

        int[] intValues = new int[IMAGE_SIZE * IMAGE_SIZE];
        float[] floatValues = new float[IMAGE_SIZE * IMAGE_SIZE * 3];

        if (bitmap.getWidth() != IMAGE_SIZE || bitmap.getHeight() != IMAGE_SIZE) {
            // rescale the bitmap if needed
            bitmap = ThumbnailUtils.extractThumbnail(bitmap, IMAGE_SIZE, IMAGE_SIZE);
        }

        bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());

        for (int i = 0; i < intValues.length; ++i) {
            final int val = intValues[i];
        // bitwise shifting - without our image is shaped [1, 64, 64, 1] but we need [1, 168, 168, 3]
            floatValues[i * 3 + 2] = Color.red(val) / 255.0f;
            floatValues[i * 3 + 1] = Color.green(val) / 255.0f;
            floatValues[i * 3] = Color.blue(val) / 255.0f;
        }
        return floatValues;
    }

后来，我尝试在 android 中使用 tensorflow 对图像进行分类，如下所示。

TensorFlowInferenceInterface tensorFlowInferenceInterface; 
tensorFlowInferenceInterface = new TensorFlowInferenceInterface(getAssets(),"model.pb");
float[] output = new float[2];
tensorFlowInferenceInterface.feed("conv2d_11_input",
                getPixels(bitmap), 1,64,64,3);
tensorFlowInferenceInterface.run(new String[]{"dense_12/Sigmoid"});
tensorFlowInferenceInterface.fetch("dense_12/Sigmoid",output);

无论我给出的值output是 [1,0]

有什么我错过了吗？