使用keras与cifar10数据集与vgg16网络模型没有正确学习？

Question

我似乎有一些问题得到正确的结果与keras，我不知道为什么我总是变坏的结果，所以我决定今天测试一定的工作，作为Cifar10数据集和vgg16网络。

#!/usr/bin/python 


# 

import warnings 
from mpl_toolkits.mplot3d import Axes3D 
from keras.utils import np_utils 
from matplotlib import cm 
from keras import metrics 
import keras 
from keras.layers import GlobalMaxPooling2D 
from keras.layers import GlobalAveragePooling2D 
from keras.preprocessing import image 
from keras.utils import layer_utils 
from keras.utils.data_utils import get_file 
from keras import backend as K 
from keras.applications.imagenet_utils import decode_predictions 
from keras.applications.imagenet_utils import preprocess_input 
from keras.applications.imagenet_utils import _obtain_input_shape 
from keras.engine.topology import get_source_inputs 
from keras.models import Sequential 
from keras.optimizers import SGD 
import scipy 
from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten 
from keras.layers import Conv1D,Conv2D,MaxPooling2D, MaxPooling1D, Reshape 
#from keras.utils.visualize_util import plot 
from keras.utils import np_utils 
from keras.layers.advanced_activations import LeakyReLU, PReLU 
from keras.layers.advanced_activations import ELU 
from keras.models import Model 
from keras.layers import Input, Dense 
from keras.layers import Dropout 
from sklearn.preprocessing import MinMaxScaler 
from keras import backend as K 
from keras.callbacks import ReduceLROnPlateau 
from keras.callbacks import CSVLogger 
from keras.callbacks import EarlyStopping 
from keras.layers.merge import Concatenate 
from keras.models import load_model 
from keras.utils import plot_model 
from skimage.util.shape import view_as_blocks 
from skimage.util.shape import view_as_windows 
from keras.callbacks import ModelCheckpoint 
import tensorflow as tf 
from keras import backend as K 
from keras.layers.local import LocallyConnected1D 
from keras.datasets import mnist,cifar10 



WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5' 
WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5' 

batch_size = 128 
num_classes = 10 
epochs = 12 

# input image dimensions 
img_rows, img_cols = 32, 32 

# the data, shuffled and split between train and test sets 
(x_train, y_train), (x_test, y_test) = cifar10.load_data() 

#print('x_train shape:', x_train.shape) 
#print(x_train.shape[0], 'train samples') 
#print(x_test.shape[0], 'test samples') 

if K.image_data_format() == 'channels_first': 
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) 
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) 
    input_shape = (1, img_rows, img_cols) 
else: 
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3) 
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3) 
    input_shape = (img_rows, img_cols, 1) 

x_train = x_train.astype('float32') 
x_test = x_test.astype('float32') 

x_train /= 255 
x_test /= 255 

print('x_train shape:', x_train.shape) 
print(x_train.shape[0], 'train samples') 
print(x_test.shape[0], 'test samples') 

y_train = keras.utils.to_categorical(y_train, num_classes) 
y_test = keras.utils.to_categorical(y_test, num_classes) 

def fws(): 
    #print "Inside" 
    # Params: 
    # batch , lr, decay , momentum, epochs 
    # 
    #Input shape: (batch_size,40,45,3) 
    #output shape: (1,15,50) 
    # number of unit in conv_feature_map = splitd 
    input = Input(shape=(img_rows,img_cols,3)) 
    zero_padded_section = keras.layers.convolutional.ZeroPadding2D(padding=(20,17), data_format='channels_last')(input) 
    model = keras.applications.vgg16.VGG16(include_top = False, 
        weights = 'imagenet', 
        input_shape = (48,84,3), 
        pooling = 'max', 
        classes = 10) 

    model_output = model(input) 


    #FC 
    dense1 = Dense(units = 512, activation = 'relu', name = "dense_1")(model_output) 
    dense2 = Dense(units = 256, activation = 'relu', name = "dense_2")(dense1) 
    dense3 = Dense(units = 10 , activation = 'softmax', name = "dense_3")(dense2) 


    model = Model(inputs = input , outputs = dense3) 
    #sgd = SGD(lr=0.08,decay=0.025,momentum = 0.99,nesterov = True) 
    model.compile(loss="categorical_crossentropy", optimizer='adam' , metrics = [metrics.categorical_accuracy]) 

    model.fit(x_train[:500], y_train[:500], 
       batch_size=batch_size, 
       epochs=epochs, 
       verbose=1, 
       validation_data=(x_test[:10], y_test[:10])) 
    score = model.evaluate(x_test, y_test, verbose=0) 
    print('Test loss:', score[0]) 
    print('Test accuracy:', score[1]) 


fws() 

我只使用该数据集的500/10，因为这将需要一段时间来训练它......但根据其结果，我现在..没有一直在学习。

7个时代在和cat.acc。是0.1300和val.acc 0.200，并且现在已经是7个时期...

什么是错的？

Answer 1

错误数steps_per_epoch不等于完整大小.. 固定的东西。