keras训练曲线训练曲线,混淆矩阵混淆矩阵,CNN层输出可视化实例层输出可视化实例
训练曲线训练曲线
def show_train_history(train_history, train_metrics, validation_metrics):
plt.plot(train_history.history[train_metrics])
plt.plot(train_history.history[validation_metrics])
plt.title('Train History')
plt.ylabel(train_metrics)
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='upper left')
# 显示训练过程
def plot(history):
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
show_train_history(history, 'acc', 'val_acc')
plt.subplot(1, 2, 2)
show_train_history(history, 'loss', 'val_loss')
plt.show()
效果：
plot(history)
混淆矩阵混淆矩阵
def plot_confusion_matrix(cm, classes,
title='Confusion matrix',
cmap=plt.cm.jet):
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, '{:.2f}'.format(cm[i, j]), horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
# 显示混淆矩阵
def plot_confuse(model, x_val, y_val):
predictions = model.predict_classes(x_val)
truelabel = y_val.argmax(axis=-1) # 将one-hot转化为label
conf_mat = confusion_matrix(y_true=truelabel, y_pred=predictions)
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