问使用随机洗牌的批次作为来自图像数据张量的CNN的输入

EN

# Form shuffled batch of data
def get_batch(inputs, targets, size):          
    '''
    Return a total of `size` random inputs and targets(or labels). 
    '''
    targets_shape = targets.shape
    num_data = targets_shape[0]
    # this is a list of the right number of indices in the indices range
    shuffled_indices = np.random.randint(0,num_data,size)
    #this takes the selected random elements
    inputs_shuffled = inputs[shuffled_indices,:,:,:]
    #depending on the target shape it could be  targets[idx,:,:,...]
    #this takes the corresponding targets
    targets_shuffle = targets[shuffled_indices,:]
    #return the shuffled data and targets
    return inputs_shuffled, targets_shuffle

#This calls the function we created    
inputs_batch, targets_batch = get_batch(inputs_all,targets_all,batch_size)
#This tells to tensorflow which input goes to which placeholder
feed_dict={inputs_placeholder: inputs_batch, 
           targets_placeholder: targets_batch}
#This runs one step of the training
sess.run(train_step,feed_dict = feed_dict)

label_batch = tf.train.batch([label], capacity=20, batch_size=10, num_threads=2)