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()该段代码中如何实现每幅直方图中的每一列颜色不一致
时间: 2023-06-07 17:11:22 浏览: 85
可以在每个子图中使用不同的颜色来绘制每个标签的条形。将颜色列表作为参数传递给plt.bar()函数即可,例如:
colors = ['r', 'g', 'b', 'c', 'm', 'y']
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[:len(unique_values)])
plt.title('Client {}'.format(i))
plt.show()
相关问题
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()
```
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()
```
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