Cifar10 Data Prep

gistfile1.py

# 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()   

#Only look at cats [=3] and dogs [=5]
train_picks = np.ravel(np.logical_or(y_train==3,y_train==5))  
test_picks = np.ravel(np.logical_or(y_test==3,y_test==5))     

y_train = np.array(y_train[train_picks]==5,dtype=int)
y_test = np.array(y_test[test_picks]==5,dtype=int)

x_train = x_train[train_picks]
x_test = x_test[test_picks]

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

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')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(np.ravel(y_train), num_classes)
y_test = keras.utils.to_categorical(np.ravel(y_test), num_classes)

# Look at the first 9 images from the dataset

images = range(0,9)

for i in images:
    plt.subplot(330 + 1 + i)
    plt.imshow(x_train[i], cmap=pyplot.get_cmap('gray'))
# show the plot
plt.show()