在代碼中加載SVHN數據集需要修改什么？

Question

我使用Keras MNIST數據集，現在我想使用谷歌數據集街景房號（SVHN）來訓練我的程序。 我不知道我需要修改什么才能在我的代碼中加載SVHN數據集（文件.mat）

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras import backend as K
import tensorflow as tf
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D


img_rows, img_cols = 28, 28

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

image_index = 7777 
print(y_train[image_index])
plt.imshow(x_train[image_index], cmap='Greys')

x_train.shape

x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 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('Number of images in x_train', x_train.shape[0])
print('Number of images in x_test', x_test.shape[0])


model = Sequential()
model.add(Conv2D(28, kernel_size=(3,3), input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation=tf.nn.relu))
model.add(Dropout(0.2))
model.add(Dense(10,activation=tf.nn.softmax))

model.compile(optimizer='adam', 
          loss='sparse_categorical_crossentropy', 
          metrics=['accuracy'])
model.fit(x=x_train,y=y_train, epochs=2)

model.evaluate(x_test, y_test)


image_index = 9999
plt.imshow(x_test[image_index].reshape(28, 28),cmap='Greys')
pred = model.predict(x_test[image_index].reshape(1, img_rows, img_cols, 1))
print(pred.argmax())

Answer 1

您可以使用scipy.io.loadmat讀取.mat文件，該文件返回值為numpy數組的dict。 請在此處查看文檔。 您可能需要根據您的要求重塑數據。

請注意，他們提到

您將需要一個HDF5 python庫來讀取MATLAB 7.3格式的mat文件。