f = plt.figure(figsize=(12, 7)) f.suptitle('Label Counts for a Sample of Clients') for i in range(6): client_dataset = dataset.shuffle(buffer_size=len(train_images)) client_dataset = dataset.batch(batch_size=10) #plot_data = collections.defaultdict(list) #for example in client_dataset: # Append counts individually per label to make plots # more colorful instead of one color per plot. example = next(iter(client_dataset)) label = example['label'].numpy() unique_values, value_counts = np.unique(label, return_counts=True) plt.bar(unique_values, value_counts) plt.title('Client {}'.format(i)) plt.show() #plot_data[label].append(label) #plt.subplot(2, 3, i+1) #plt.title('Client {}'.format(i)) #for j in range(10): #plt.hist( #plot_data[j], #density=False, #bins=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])该段代码中如何让输出的直方图呈现2*3的排列

这段代码用于生成一个大小为12x7的图像并设置标题为“一组客户的标签计数”。然后，它会循环6次，每次使用shuffle()方法将数据随机混排，然后使用batch()方法将数据分成10个一组。最终生成的client_dataset是一个由随机混排并分成10个一组的数据集。

相关问题

f = plt.figure(figsize=(12, 7)) f.suptitle('Label Counts for a Sample of Clients') for i in range(6): client_dataset = dataset.shuffle(buffer_size=len(train_images)) client_dataset = dataset.batch(batch_size=10) example = next(iter(client_dataset)) label = example['label'].numpy() unique_values, value_counts = np.unique(label, return_counts=True) plt.bar(unique_values, value_counts) plt.title('Client {}'.format(i)) plt.show()该段代码中如何修改实现使得输出的直方图呈现2*3的排列

把for循环中的i变量改为两个变量row和col，表示行和列的数量，然后用嵌套的for循环输出直方图，并在外层循环前面添加subplot方法，修改后的代码如下：

f = plt.figure(figsize=(12, 7))
f.suptitle('Label Counts for a Sample of Clients')

row = 2
col = 3

for i in range(row * col):
    client_dataset = dataset.shuffle(buffer_size=len(train_images))
    client_dataset = dataset.batch(batch_size=10)
    example = next(iter(client_dataset))
    label = example['label'].numpy()
    unique_values, value_counts = np.unique(label, return_counts=True)
    
    plt.subplot(row, col, i+1)
    plt.bar(unique_values, value_counts)
    plt.title('Client {}'.format(i))
    
plt.show()

f = plt.figure(figsize=(12, 7)) f.suptitle('Label Counts for a Sample of Clients') row = 2 col = 3 for i in range(row * col): client_dataset = dataset.shuffle(buffer_size=len(train_images)) client_dataset = dataset.batch(batch_size=10) example = next(iter(client_dataset)) label = example['label'].numpy() unique_values, value_counts = np.unique(label, return_counts=True) plt.subplot(row, col, i+1) plt.bar(unique_values, value_counts) plt.title('Client {}'.format(i)) plt.show()该段代码中如何修改实现直方图中每列数据颜色不一样

您可以按照如下方法修改代码，实现直方图中每列数据颜色不一样：

1. 导入 colormap 模块：`from matplotlib import cm`

2. 定义颜色序列：`colors = cm.rainbow(np.linspace(0, 1, row * col))`

3. 在 for 循环中，为每个子图指定一种颜色：`plt.bar(unique_values, value_counts, color=colors[i])`

修改后的代码如下：

from matplotlib import pyplot as plt
from matplotlib import cm

f = plt.figure(figsize=(12, 7))
f.suptitle('Label Counts for a Sample of Clients')

row = 2
col = 3

colors = cm.rainbow(np.linspace(0, 1, row * col))

for i in range(row * col):
    client_dataset = dataset.shuffle(buffer_size=len(train_images))
    client_dataset = dataset.batch(batch_size=10)
    example = next(iter(client_dataset))
    label = example['label'].numpy()
    unique_values, value_counts = np.unique(label, return_counts=True)
    
    plt.subplot(row, col, i + 1)
    plt.bar(unique_values, value_counts, color=colors[i])
    plt.title('Client {}'.format(i))

plt.show()