ax[0].plt.plot(first_layer_activation[0,:,0], cmap="viridis")
时间: 2023-03-31 10:04:38 浏览: 63
这是一个关于 Python 数据可视化的问题,我可以回答。这段代码是用来绘制第一层激活函数的可视化图像,其中 first_layer_activation 是一个三维张量,第一个维度是样本数,第二个维度是特征图的尺寸,第三个维度是特征图的通道数。plt.plot() 函数用来绘制第一个通道的特征图的可视化图像,cmap="viridis" 表示使用 viridis 颜色映射方案。
相关问题
def draw_stats(self, vals, vals1, vals2, vals3, vals4, vals5, vals6): self.ax1 = plt.subplot(self.gs[0, 0]) self.ax1.plot(vals) self.ax1.set_xlim(self.xlim) locs = self.ax1.get_xticks() locs[0] = self.xlim[0] locs[-1] = self.xlim[1] self.ax1.set_xticks(locs) self.ax1.use_sticky_edges = False self.ax1.set_title(f'Connected Clients Ratio') self.ax2 = plt.subplot(self.gs[1, 0]) self.ax2.plot(vals1) self.ax2.set_xlim(self.xlim) self.ax2.set_xticks(locs) self.ax2.yaxis.set_major_formatter(FuncFormatter(format_bps)) self.ax2.use_sticky_edges = False self.ax2.set_title('Total Bandwidth Usage') self.ax3 = plt.subplot(self.gs[2, 0]) self.ax3.plot(vals2) self.ax3.set_xlim(self.xlim) self.ax3.set_xticks(locs) self.ax3.use_sticky_edges = False self.ax3.set_title('Bandwidth Usage Ratio in Slices (Averaged)') self.ax4 = plt.subplot(self.gs[3, 0]) self.ax4.plot(vals3) self.ax4.set_xlim(self.xlim) self.ax4.set_xticks(locs) self.ax4.use_sticky_edges = False self.ax4.set_title('Client Count Ratio per Slice') self.ax5 = plt.subplot(self.gs[0, 1]) self.ax5.plot(vals4) self.ax5.set_xlim(self.xlim) self.ax5.set_xticks(locs) self.ax5.use_sticky_edges = False self.ax5.set_title('Coverage Ratio') self.ax6 = plt.subplot(self.gs[1, 1]) self.ax6.plot(vals5) self.ax6.set_xlim(self.xlim) self.ax6.set_xticks(locs) self.ax6.yaxis.set_major_formatter(FormatStrFormatter('%.3f')) self.ax6.use_sticky_edges = False self.ax6.set_title('Block ratio') self.ax7 = plt.subplot(self.gs[2, 1]) self.ax7.plot(vals6) self.ax7.set_xlim(self.xlim) self.ax7.set_xticks(locs) self.ax7.yaxis.set_major_formatter(FormatStrFormatter('%.3f')) self.ax7.use_sticky_edges = False self.ax7.set_title('Handover ratio')修改为一张张输出图片
好的,你可以将这段代码修改为:
```python
def draw_stats(self, vals, vals1, vals2, vals3, vals4, vals5, vals6, save_path):
fig, axs = plt.subplots(4, 2, figsize=(16, 16))
axs[0, 0].plot(vals)
axs[0, 0].set_xlim(self.xlim)
locs = axs[0, 0].get_xticks()
locs[0] = self.xlim[0]
locs[-1] = self.xlim[1]
axs[0, 0].set_xticks(locs)
axs[0, 0].use_sticky_edges = False
axs[0, 0].set_title(f'Connected Clients Ratio')
axs[1, 0].plot(vals1)
axs[1, 0].set_xlim(self.xlim)
axs[1, 0].set_xticks(locs)
axs[1, 0].yaxis.set_major_formatter(FuncFormatter(format_bps))
axs[1, 0].use_sticky_edges = False
axs[1, 0].set_title('Total Bandwidth Usage')
axs[2, 0].plot(vals2)
axs[2, 0].set_xlim(self.xlim)
axs[2, 0].set_xticks(locs)
axs[2, 0].use_sticky_edges = False
axs[2, 0].set_title('Bandwidth Usage Ratio in Slices (Averaged)')
axs[3, 0].plot(vals3)
axs[3, 0].set_xlim(self.xlim)
axs[3, 0].set_xticks(locs)
axs[3, 0].use_sticky_edges = False
axs[3, 0].set_title('Client Count Ratio per Slice')
axs[0, 1].plot(vals4)
axs[0, 1].set_xlim(self.xlim)
axs[0, 1].set_xticks(locs)
axs[0, 1].use_sticky_edges = False
axs[0, 1].set_title('Coverage Ratio')
axs[1, 1].plot(vals5)
axs[1, 1].set_xlim(self.xlim)
axs[1, 1].set_xticks(locs)
axs[1, 1].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
axs[1, 1].use_sticky_edges = False
axs[1, 1].set_title('Block ratio')
axs[2, 1].plot(vals6)
axs[2, 1].set_xlim(self.xlim)
axs[2, 1].set_xticks(locs)
axs[2, 1].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
axs[2, 1].use_sticky_edges = False
axs[2, 1].set_title('Handover ratio')
plt.tight_layout()
plt.savefig(save_path)
plt.show()
```
这样就可以一张张输出图片了,你只需要传入一个保存路径参数 `save_path` 即可。
绘制训练集和测试集的真实值和预测值图像 train_predict_plot = np.empty_like(data_scaled) train_predict_plot[:, :] = np.nan train_predict_plot[time_steps:len(train_predict) + time_steps, :] = train_predict test_predict_plot = np.empty_like(data_scaled) test_predict_plot[:, :] = np.nan test_predict_plot[len(train_predict) + time_steps * 2 + 1:len(data_scaled) - 1, :] = test_predict plt.figure(figsize=(10, 6)) plt.plot(scaler.inverse_transform(data_scaled)) plt.plot(train_predict_plot) plt.plot(test_predict_plot) plt.legend(['True', 'Train Predict', 'Test Predict']) plt.xlabel('Time/h') plt.ylabel('kwh') plt.show(改写以上代码,使得训练集部分和测试集部分分别绘制在两张图上
下面是改写后的代码,将训练集和测试集的真实值和预测值分别绘制在两张图上:
```
train_predict_plot = np.empty_like(data_scaled)
train_predict_plot[:, :] = np.nan
train_predict_plot[time_steps:len(train_predict) + time_steps, :] = train_predict
test_predict_plot = np.empty_like(data_scaled)
test_predict_plot[:, :] = np.nan
test_predict_plot[len(train_predict) + time_steps * 2 + 1:len(data_scaled) - 1, :] = test_predict
# 绘制训练集部分真实值和预测值图像
plt.figure(figsize=(10, 6))
plt.plot(scaler.inverse_transform(data_scaled[:len(train_predict)]))
plt.plot(train_predict_plot[:len(train_predict)])
plt.legend(['True', 'Train Predict'])
plt.xlabel('Time/h')
plt.ylabel('kwh')
plt.title('Training Set')
plt.show()
# 绘制测试集部分真实值和预测值图像
plt.figure(figsize=(10, 6))
plt.plot(scaler.inverse_transform(data_scaled[len(train_predict) + time_steps * 2 + 1:]))
plt.plot(test_predict_plot[len(train_predict) + time_steps * 2 + 1:])
plt.legend(['True', 'Test Predict'])
plt.xlabel('Time/h')
plt.ylabel('kwh')
plt.title('Test Set')
plt.show()
```