Без изменений в потере или точности

Question

Я использую пример кода из блогов Keras (с некоторыми изменениями), но при запуске показатели потери и точности моей модели не улучшаются.

Я не уверен, что неправильно реализовал какую-то функцию.

Я загружаю изображения из сохраненного файла (h5py) и небольшими партиями.

import numpy as np
from scipy.misc import imread, imresize
import cv2
import matplotlib.pyplot as plt

from keras.layers import Conv2D, MaxPooling2D, Input, Flatten, Dense
from keras.models import Model
import keras

#model layers

input_img = Input(shape=(299, 299, 3))

tower_1 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_img)
tower_1 = Conv2D(64, (3, 3), padding='same', activation='relu')(tower_1)

tower_2 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_img)
tower_2 = Conv2D(64, (5, 5), padding='same', activation='relu')(tower_2)

tower_3 = MaxPooling2D((3, 3), strides=(1, 1), padding='same')(input_img)
tower_3 = Conv2D(64, (1, 1), padding='same', activation='relu')(tower_3)

concatenated_layer = keras.layers.concatenate([tower_1, tower_2, tower_3], axis=3)
conv1 = Conv2D(3,(3,3), padding = 'same', activation = 'relu')(concatenated_layer)
flatten = Flatten()(conv1)
dense_1 = Dense(500, activation = 'relu')(flatten)
predictions = Dense(12, activation = 'softmax')(dense_1)


#initialize and compile model


model = Model(inputs= input_img, output = predictions)
SGD =keras.optimizers.SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False)

model.compile(optimizer=SGD,
              loss='categorical_crossentropy',
              metrics=['accuracy'])



#Load images

import loading_hdf5_files
hdf5_path =r'C:\Users\Moondra\Desktop\Keras Applications\training.hdf5' 
batches = loading_hdf5_files.load_batches(12, hdf5_path, classes = 12)

for i in range(10):
    #creating a new generator
    batches = loading_hdf5_files.load_batches(8, hdf5_path, classes = 12)

    for i in range(15):
        x,y = next(batches)
        #plt.imshow(x[0])
        #plt.show()
        x = (x/255).astype('float32')  # trying to save memory
        data =model.train_on_batch(x/255,y)
        print('loss : {:.5},  accuracy :  {:.2%}'.format(*data))

Мой вывод

Это последние 50 шагов или около того, но без изменений с первого шага:

loss : 2.4226,  accuracy :  100.00%
loss : 2.4122,  accuracy :  100.00%
loss : 2.542,  accuracy :  0.00%
loss : 2.4793,  accuracy :  0.00%
loss : 2.4934,  accuracy :  0.00%
loss : 2.5132,  accuracy :  0.00%
loss : 2.4949,  accuracy :  0.00%
loss : 2.472,  accuracy :  0.00%
loss : 2.4616,  accuracy :  0.00%
loss : 2.4865,  accuracy :  0.00%
loss : 2.5585,  accuracy :  0.00%
loss : 2.4406,  accuracy :  0.00%
loss : 2.4882,  accuracy :  0.00%
loss : 2.4311,  accuracy :  0.00%
loss : 2.4895,  accuracy :  0.00%
loss : 2.502,  accuracy :  0.00%
loss : 2.4913,  accuracy :  0.00%
loss : 2.4585,  accuracy :  0.00%
loss : 2.4846,  accuracy :  0.00%
loss : 2.5143,  accuracy :  0.00%
loss : 2.4505,  accuracy :  0.00%
loss : 2.5574,  accuracy :  0.00%
loss : 2.5458,  accuracy :  0.00%
loss : 2.4311,  accuracy :  0.00%
loss : 2.4963,  accuracy :  0.00%
loss : 2.4212,  accuracy :  100.00%
loss : 2.4896,  accuracy :  0.00%
loss : 2.4824,  accuracy :  0.00%
loss : 2.4886,  accuracy :  0.00%
loss : 2.5135,  accuracy :  0.00%
loss : 2.4156,  accuracy :  100.00%
loss : 2.511,  accuracy :  0.00%
loss : 2.484,  accuracy :  0.00%
loss : 2.4965,  accuracy :  0.00%
loss : 2.5457,  accuracy :  0.00%
loss : 2.5343,  accuracy :  0.00%
loss : 2.5185,  accuracy :  0.00%
loss : 2.4902,  accuracy :  0.00%
loss : 2.4137,  accuracy :  100.00%
loss : 2.5271,  accuracy :  0.00%
loss : 2.5111,  accuracy :  0.00%
loss : 2.5014,  accuracy :  0.00%
loss : 2.4908,  accuracy :  0.00%
loss : 2.4904,  accuracy :  0.00%

Answer 1

Обучение в течение более длительных периодов времени, кажется, решает проблему.