read_cifar_batch.py

+import pickle
+import numpy as np
+import glob
+def unpickle(file):
+    with open(file, 'rb') as fo:
+        dict = pickle.load(fo, encoding='bytes')
+    return dict
+
+def read_cifar_batch(path_to_batch_file):
+    dict=unpickle(path_to_batch_file)
+    data=np.array(dict[b'data'],dtype=np.float32)/255
+    labels=np.array(dict[b'labels'],dtype=np.int64)
+    return data,labels
+
+def read_cifar(path_to_batches_files):
+    files=glob.glob(path_to_batches_files)
+    data,labels=read_cifar_batch(files[0])
+    for i in range(1,len(files)):
+        data_temp,labels_temp=read_cifar_batch(files[i])
+        data=np.concatenate((data,data_temp),axis=0)
+        labels=np.concatenate((labels,labels_temp),axis=0)
+    return data,labels
+
+
+if __name__ == "__main__":
+    path="image-classification/data/cifar-10-batches-py/data_batch_1"
+    read_cifar_batch(path)
+    #plot the 9 first image of the batch    
+    import matplotlib.pyplot as plt
+    data,labels=read_cifar_batch(path)
+    fig, axes = plt.subplots(3, 3)
+    fig.subplots_adjust(hspace=0.6, wspace=0.3)
+    for i, ax in enumerate(axes.flat):
+        ax.imshow(data[i].reshape(3, 32, 32).transpose([1, 2, 0]))
+        ax.set_xticks([])
+        ax.set_yticks([])
+        ax.set_xlabel(labels[i])
+    plt.show()
+