title: What do i have to modify to load the SVHN Dataset in my code?
body: <p>I work with the Keras MNIST dataset and I now I want to use the google dataset Street view house numbers (SVHN) to train my program. I don't know what I have to modify to load the SVHN dataset ( file .mat) in my code</p>
  
  <pre><code>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())
  </code></pre>