Question

我正在遵循一个非常基本的tf.keras教程来构建二进制图像分类器。然后，我将完成的模型转换为.tflite文件，我正尝试将其合并到android应用中。

代码创建顺序tf.keras模型。

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense
from tensorflow.keras import backend as K
from tensorflow.keras.callbacks import TensorBoard, ReduceLROnPlateau
from sklearn.utils import class_weight
import numpy as np


# dimensions of our images.====================================================================================================
img_width, img_height = 200, 200

train_data_dir = 'augmentedImg/200/training_data'#=============================================================================
validation_data_dir = 'augmentedImg/200/validation_data'#=============================================================================
nb_train_samples = 9009
nb_validation_samples = 2252
epochs = 100
batch_size = 32

layer_size = 64

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

model = Sequential()
model.add(Conv2D(layer_size, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(layer_size, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(layer_size, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(layer_size, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

#model.add(Dropout(0.5))

model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    rotation_range=90,
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
    )

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    color_mode='grayscale',
    batch_size=batch_size,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    color_mode='grayscale',
    batch_size=batch_size,
    class_mode='binary')

class_weights = class_weight.compute_class_weight(
               'balanced',
                np.unique(train_generator.classes), 
                train_generator.classes)

model.fit_generator(
    train_generator,
    class_weight=class_weights,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples# // batch_size,
    #callbacks=[tensorboard, reduce_lr]
    )

model.save_weights('model.h5')
print("End of program")

代码转换为tflite模型：

import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_keras_model_file("models/1Data_aug_200x200.h5")
tflite_model = converter.convert()
open("models/convertedModels/1Data_aug_200x200.tflite", "wb").write(tflite_model)

然后我在模型上调用“运行”，传入ByteBuffer（要分类的图像）和输出数组。

输出数组：

private float[][] labelProbArray = new float[1][numLabels]; //numLabels=2

但是，当我致电tflite.run(imgData, labelProbArray);时，我遇到了错误。

java.lang.IllegalArgumentException: Cannot copy between a TensorFlowLite tensor with shape [1, 1] and a Java object with shape [1, 2].

张量也应该具有[1,2]形状吗？还是有其他选择可以让我得到预测的类别和返回的概率？

Tensorflow：tflite模型和输出数组的形状不兼容

0 个答案: