Tensorflow 预测始终为零

Question

我是 Tensorflow 新手。我使用 convNetKerasLarge.py 生成模型并保存为 tflite 模型。

我正在尝试按如下方式测试这个保存的模型

import tensorflow as tf
import numpy as np
import glob
from skimage.transform import resize
from skimage import io

# out of previously used training and test set
start = 4001
# no of images
row_count = 1
end = start + row_count

n_image_rows = 106
n_image_cols = 106

np_val_images = np.zeros(shape=(1, 1))
np_val_labels = np.zeros(shape=(1, 1))


def prepare_validation_set():
    global np_val_images
    global np_val_labels

    positive_samples = glob.glob('datasets/drunk_resize_frontal_faces/pos/*')[start:end]
    # negative_samples = glob.glob('datasets/drunk_resize_frontal_faces/neg/*')[start:end]
    # negative_samples = random.sample(negative_samples, len(positive_samples))

    val_images = []
    val_labels = []

    for i in range(len(positive_samples)):
        val_images.append(resize(io.imread(positive_samples[i]), (n_image_rows, n_image_cols)))
        val_labels.append(1)
    # for i in range(len(negative_samples)):
    #    val_images.append(resize(io.imread(negative_samples[i]), (n_image_rows, n_image_cols)))
    #    val_labels.append(0)
    np_val_images = np.array(val_images)
    np_val_labels = np.array(val_labels)


def run_tflite_model(tflite_file, index):

    prepare_validation_set()

    # Initialize the interpreter
    interpreter = tf.lite.Interpreter(model_path=str(tflite_file))
    interpreter.allocate_tensors()

    input_details = interpreter.get_input_details()[0]
    output_details = interpreter.get_output_details()[0]

    test_image = np_val_images[index]

    test_image = np.expand_dims(test_image, axis=0).astype(input_details["dtype"])
    interpreter.set_tensor(input_details["index"], test_image)
    interpreter.invoke()
    output = interpreter.get_tensor(output_details["index"])[0]
    print(output_details)

    prediction = output.argmax()
    print(prediction)


if __name__ == '__main__':

    test_image_index = 1
    tflite_model_file = "models/converted/model.tflite"
    run_tflite_model(tflite_model_file, 0)

如果我运行这个我得到的预测是 0 即使标签应该是 1 因为我输入的是正图像。（仅供参考：Test loss: 0.08881912380456924 Test accuracy: 0.9729166626930237 有 10 个时期）。我确信我的代码中存在导致此问题的错误，请帮助我找到它。

Answer 1

您链接的脚本在训练前通过减去平均值（这里 0.5）并除以标准差（这里 1）对数据进行归一化：

mean = np.array([0.5,0.5,0.5])
std = np.array([1,1,1])
X_train = X_train.astype('float')
X_test = X_test.astype('float')
for i in range(3):
    X_train[:,:,:,i] = (X_train[:,:,:,i]- mean[i]) / std[i]
    X_test[:,:,:,i] = (X_test[:,:,:,i]- mean[i]) / std[i]

如果您在使用模型进行预测之前不重复相同的操作，您传递给模型的输入将不会具有与您训练时相同的特征。

您可以在准备数据时通过减去图像的平均值 (0.5) 来修复它，即：

    np_val_images = np.array(val_images) - 0.5

Tensorflow 预测始终为零

Tensorflow Prediction always zero

python

tensorflow

tensorflow2.0

tensorflow-lite