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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Plot train and valid set NLL
Step1:  Plot ratio of update norms to parameter norms across epochs for different layers
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Python Code:
tr = np.array(model.monitor.channels['valid_y_y_1_nll'].time_record) / 3600.
fig = plt.figure(figsize=(12,8))
ax1 = fig.add_subplot(111)
ax1.plot(model.monitor.channels['valid_y_y_1_nll'].val_record)
ax1.plot(model.monitor.channels['train_y_y_1_nll'].val_record)
ax1.set_xlabel('Epochs')
ax1.legend(['Valid', 'Train'])
ax1.set_ylabel('NLL')
ax1.set_ylim(0., 5.)
ax1.grid(True)
ax2 = ax1.twiny()
ax2.set_xticks(np.arange(0,tr.shape[0],20))
ax2.set_xticklabels(['{0:.2f}'.format(t) for t in tr[::20]])
ax2.set_xlabel('Hours')
print(model.yaml_src)
pv = get_weights_report(model=model)
img = pv.get_img()
img = img.resize((4*img.size[0], 4*img.size[1]))
img_data = io.BytesIO()
img.save(img_data, format='png')
display(Image(data=img_data.getvalue(), format='png'))
plt.plot(model.monitor.channels['learning_rate'].val_record)
Explanation: Plot train and valid set NLL
End of explanation
h1_W_up_norms = np.array([float(v) for v in model.monitor.channels['mean_update_h1_W_kernel_norm_mean'].val_record])
h1_W_norms = np.array([float(v) for v in model.monitor.channels['valid_h1_kernel_norms_mean'].val_record])
plt.plot(h1_W_norms / h1_W_up_norms)
plt.ylim(0,1000)
plt.show()
plt.plot(model.monitor.channels['valid_h1_kernel_norms_mean'].val_record)
plt.plot(model.monitor.channels['valid_h1_kernel_norms_max'].val_record)
h2_W_up_norms = np.array([float(v) for v in model.monitor.channels['mean_update_h2_W_kernel_norm_mean'].val_record])
h2_W_norms = np.array([float(v) for v in model.monitor.channels['valid_h2_kernel_norms_mean'].val_record])
plt.plot(h2_W_norms / h2_W_up_norms)
plt.show()
plt.plot(model.monitor.channels['valid_h2_kernel_norms_mean'].val_record)
plt.plot(model.monitor.channels['valid_h2_kernel_norms_max'].val_record)
h3_W_up_norms = np.array([float(v) for v in model.monitor.channels['mean_update_h3_W_kernel_norm_mean'].val_record])
h3_W_norms = np.array([float(v) for v in model.monitor.channels['valid_h3_kernel_norms_mean'].val_record])
plt.plot(h3_W_norms / h3_W_up_norms)
plt.show()
plt.plot(model.monitor.channels['valid_h3_kernel_norms_mean'].val_record)
plt.plot(model.monitor.channels['valid_h3_kernel_norms_max'].val_record)
h4_W_up_norms = np.array([float(v) for v in model.monitor.channels['mean_update_h4_W_kernel_norm_mean'].val_record])
h4_W_norms = np.array([float(v) for v in model.monitor.channels['valid_h4_kernel_norms_mean'].val_record])
plt.plot(h4_W_norms / h4_W_up_norms)
plt.show()
plt.plot(model.monitor.channels['valid_h4_kernel_norms_mean'].val_record)
plt.plot(model.monitor.channels['valid_h4_kernel_norms_max'].val_record)
h5_W_up_norms = np.array([float(v) for v in model.monitor.channels['mean_update_h5_W_kernel_norm_mean'].val_record])
h5_W_norms = np.array([float(v) for v in model.monitor.channels['valid_h5_kernel_norms_mean'].val_record])
plt.plot(h5_W_norms / h5_W_up_norms)
plt.show()
plt.plot(model.monitor.channels['valid_h5_kernel_norms_mean'].val_record)
plt.plot(model.monitor.channels['valid_h5_kernel_norms_max'].val_record)
h6_W_up_norms = np.array([float(v) for v in model.monitor.channels['mean_update_h6_W_col_norm_mean'].val_record])
h6_W_norms = np.array([float(v) for v in model.monitor.channels['valid_h6_col_norms_mean'].val_record])
plt.plot(h6_W_norms / h6_W_up_norms)
plt.show()
plt.plot(model.monitor.channels['valid_h6_col_norms_mean'].val_record)
plt.plot(model.monitor.channels['valid_h6_col_norms_max'].val_record)
y_W_up_norms = np.array([float(v) for v in model.monitor.channels['mean_update_softmax_W_col_norm_mean'].val_record])
y_W_norms = np.array([float(v) for v in model.monitor.channels['valid_y_y_1_col_norms_mean'].val_record])
plt.plot(y_W_norms / y_W_up_norms)
plt.show()
plt.plot(model.monitor.channels['valid_y_y_1_col_norms_mean'].val_record)
plt.plot(model.monitor.channels['valid_y_y_1_col_norms_max'].val_record)
Explanation: Plot ratio of update norms to parameter norms across epochs for different layers
End of explanation
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