keras训练曲线,混淆矩阵,CNN层输出可视化实例

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()

def plot_confusion_matrix(cm, classe<div style="color:transparent">本文来源gaodai.ma#com搞#代!码(网</div>s,
    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)
 plt.figure()
 plot_confusion_matrix(conf_mat, range(np.max(truelabel)+1))

x_val.shape # (25838, 48, 48, 1)
y_val.shape # (25838, 7)
plot_confuse(model, x_val, y_val)

# 卷积网络可视化
def visual(model, data, num_layer=1):
 # data:图像array数据
 # layer:第n层的输出
 data = np.expand_dims(data, axis=0) # 开头加一维
 layer = keras.backend.function([model.layers[0].input], [model.layers[num_layer].output])
 f1 = layer([data])[0]
 num = f1.shape[-1]
 plt.figure(figsize=(8, 8))
 for i in range(num):
 plt.subplot(np.ceil(np.sqrt(num)), np.ceil(np.sqrt(num)), i+1)
 plt.imshow(f1[0, :, :, i] * 255, cmap='gray')
 plt.axis('off')
 plt.show()

visual(model, data, 1) # 卷积层
visual(model, data, 2) # 激活层
visual(model, data, 3) # 规范化层
visual(model, data, 4) # 池化层