如何查看数据集上的输出测试图像

Question

我正在使用cifar 10分类代码解决我的问题。我仅通过对自己的数据集使用numpy数组文件来更改数据集。在运行代码之后，我也得到了准确性和损失，但是我只想看到可视化的输出图像。谁能帮助我如何查看输出图像？

import keras
from keras import backend as K
from keras.models import Sequential
from matplotlib import pyplot as plt
from matplotlib.pyplot import *
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D
#from keras.datasets import cifar10
from keras import regularizers
from keras.callbacks import LearningRateScheduler
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import normalize
from sklearn.metrics import confusion_matrix
from scipy.misc import toimage
from matplotlib import pyplot
from keras.models import model_from_json


def lr_schedule(epoch):
    lrate = 0.001
    if epoch > 75:
        lrate = 0.0005
    if epoch > 100:
        lrate = 0.0003
    return lrate


num_classes = 2
epochs=3
img_x, img_y = 224, 224

x = np.load('train.npy')
y = np.load('train_labels.npy')

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)

#(x_train, y_train), (x_test, y_test) = cifar10.load_data()


x_train = x_train.reshape(x_train.shape[0], img_x, img_y, 1)
x_test = x_test.reshape(x_test.shape[0], img_x, img_y, 1)
input_shape = (img_x, img_y, 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') 

#z-score
mean = np.mean(x_train,axis=(0,1,2,3))
std = np.std(x_train,axis=(0,1,2,3))
x_train = (x_train-mean)/(std+1e-7)
x_test = (x_test-mean)/(std+1e-7)


y_train = np_utils.to_categorical(y_train,num_classes)
y_test = np_utils.to_categorical(y_test,num_classes)


weight_decay = 1e-4
model = Sequential()

#Encoder 1
model.add(Conv2D(64, kernel_size=(3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=input_shape))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(64, kernel_size=(3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

#Encoder 2
model.add(Conv2D(64, kernel_size=(3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(64, kernel_size=(3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
#model.add(Dropout(0.2))

#Decoder 1 

model.add(UpSampling2D(size=(2,2)))
model.add(Conv2D(64, kernel_size=(3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(64, kernel_size=(3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(BatchNormalization())
model.add(Activation('relu'))


#Decoder 2
model.add(UpSampling2D(size=(2,2)))
model.add(Conv2D(64, kernel_size=(3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(64, kernel_size=(3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(BatchNormalization())
model.add(Activation('relu'))

#model.add(Dropout(0.3))

#model.add(Flatten())
model.add(Dense(num_classes, activation='softmax'))

model.summary()
batch_size = 5
opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)

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

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test),
          callbacks=[history])


#save to disk
model_json = model.to_json()
with open('model.json', 'w') as json_file:
    json_file.write(model_json)
model.save_weights('model.h5') 

#testing
scores = model.evaluate(x_test, y_test, verbose=1)
print('\nTest result: %.3f loss: %.3f' % (scores[1]*100,scores[0]))
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
plt.plot(range(1, epochs+1), history.acc)
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.show()