test_nn.py运行结果错误

[a790399215]

想请教一下，我运行create_traindata生成了5000张图片，batch_size是256训练了20轮，但是在运行test_nn.py文件输出的结果却是完全错误的，想请教一下大家有遇到这种情况吗

[a790399215]

例如：藏UPXSGW车牌被识别成：藏PPPPPP

[a790399215]

后面六个字符的识别结果基本是一样的

[a790399215]

test_nn.py代码如下： `import numpy as np import pickle from keras.layers import Dense, Input, BatchNormalization, Conv2D, Flatten, MaxPooling2D, Activation, Reshape, Layer from keras.models import Model, load_model, model_from_json from keras.callbacks import TensorBoard, ModelCheckpoint, LearningRateScheduler from keras.optimizers import RMSprop from keras import backend as K import tensorflow as tf import keras.backend.tensorflow_backend as KTF

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.4) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"]

class NormLayer(Layer):

def __init__(self, **kwargs):
    super(NormLayer, self).__init__(**kwargs)

def build(self, input_shape):
    self.kernal = self.add_weight(name='NormLayer', shape=(
        1, 13), initializer='ones', trainable=True)
    super(NormLayer, self).build(input_shape)

def call(self, inputs):
    # out = self.kernal * inputs
    out = K.dot(self.kernal, inputs)
    out = K.permute_dimensions(out, (1, 0, 2))
    return out[:, 0, :]
''' because this NormLayer do not change the input_shape,
    so the compute_output_shape need not to implement (maybe)
'''

def compute_output_shape(self, input_shape):
    return (input_shape[0], 65)

def get_config(self):
    # config = {}
    base_config = super(NormLayer, self).get_config()
    return dict(list(base_config.items()))
    # return dict(list(base_config.items()) + list(config.items()))

e2e_model = load_model('e2e_model.h5', custom_objects={'NormLayer': NormLayer}) label_path = 'label.txt' tem_label = np.loadtxt(label_path) row, col = tem_label.shape label1 = np.zeros([row, 31]) label2 = np.zeros([row, 34]) label3 = np.zeros([row, 34]) label4 = np.zeros([row, 34]) label5 = np.zeros([row, 34]) label6 = np.zeros([row, 34]) label7 = np.zeros([row, 34]) for i in range(row): label1[i, int(tem_label[i, 0])] = 1 label2[i, int(tem_label[i, 1]) - 31] = 1 label3[i, int(tem_label[i, 2]) - 31] = 1 label4[i, int(tem_label[i, 3]) - 31] = 1 label5[i, int(tem_label[i, 4]) - 31] = 1 label6[i, int(tem_label[i, 5]) - 31] = 1 label7[i, int(tem_label[i, 6]) - 31] = 1

img_path = './train_data.pkl' img_data = np.load(img_path,allow_pickle=True) img_data = img_data.transpose(0, 2, 1, 3)

e2e_predict = e2e_model.predict(img_data[0:5, :, :, :])

def print_trueLabel(num): print(np.array([np.argmax(label1[num, :]), np.argmax(label2[num, :]) + 31, np.argmax(label3[num, :]) + 31, np.argmax(label4[num, :]) + 31, np.argmax(label5[num, :]) + 31, np.argmax(label6[num, :]) + 31, np.argmax(label7[num, :]) + 31])) print(chars[np.argmax(label1[num, :])] + chars[np.argmax(label2[num, :]) + 31] + chars[np.argmax(label3[num, :]) + 31] + chars[np.argmax(label4[num, :]) + 31] + chars[np.argmax(label5[num, :]) + 31] + chars[np.argmax(label6[num, :]) + 31] + chars[np.argmax(label7[num, :]) + 31])

def print_predictLabel(x): num = x[0].shape[0] sort = np.zeros([num, len(x)]) for i in range(len(x)): temp = x[i] sort[:,i] = np.argmax(temp, 1) for i in range(num): print(np.array([int(sort[i, 0]), int(sort[i, 1] + 31), int(sort[i, 2] + 31), int(sort[i, 3] + 31), int(sort[i, 4] + 31), int(sort[i, 5] + 31), int(sort[i, 6] + 31)])) print(chars[int(sort[i, 0])] + chars[int(sort[i, 1] + 31)] + chars[int(sort[i, 2] + 31)] + chars[int(sort[i, 3] + 31)] + chars[int(sort[i, 4] + 31)] + chars[int(sort[i, 5] + 31)] + chars[int(sort[i, 6] + 31)])

for i in range(5): print_trueLabel(i) print_predictLabel(e2e_predict)`

[caisan]

test_nn.py是哪里的？