val_acc = history.history['val_sparse_categorical_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] plt.subplot(1,2,1) plt.plot(acc, label='Training Accuracy') plt.plot(val_acc, l
>>> loss = make_scorer(my_custom_loss_func, greater_is_better=False) >>> score = make_scorer(my_custom_loss_func, greater_is_better=True) >>> ground_truth = [[1, 1]] >>> predictions = [0, 1] >>> from sklearn.dummy import DummyClassifier >>> clf = DummyClassifier(strategy=...
在Tensoflow 2.0 中可通过 model.compile (optimizer , loss , metrics) 方法绑定损失函数和计算梯度的方法,loss 参数可绑定 tensorflow.keras.losses 中多种已定义的损失函数,常用的 loss 损失函数有下面几种:(当中yi为真实值 yi^为预测值) 2.1 mean_squared_error 均方误差 1defmean_squared_error(y_true, y...
print("Now,we start drawing the loss and acc trends graph...") # summarize history for accuracy fig = plt.figure(1) plt.plot(history.history["accuracy"]) plt.plot(history.history["val_accuracy"]) plt.title("Model accuracy") plt.ylabel("accuracy") plt.xlabel("epoch") plt.legend(["t...
plt.title ('Training and validation accuracy') plt.figure() plt.plot ( epochs, loss ,label='loss') plt.plot ( epochs, val_loss ,label='val_loss') plt.legend(loc='best') plt.title ('Training and validation loss') 1. 2. 3. ...
plt.title("Model accuracy") plt.ylabel("accuracy") plt.xlabel("epoch") plt.legend(["train", "test"], loc="upper left") plt.savefig(acc_trend_graph_path) plt.close(1) # summarize history for loss fig = plt.figure(2) plt.plot(history.history["loss"]) ...
>> Out-of-bag evaluation: accuracy:0.964602 logloss:0.102378 >> Number of trees: 300 >> Total number of nodes: 4170 >> ...# Get feature importance as a array model.make_inspector().variable_importances()["MEAN_DECREASE_IN_ACCURACY"]>> [("flipper_length_mm", 0.149),>> (...
plt.plot(epoch\_range, history.history\['sparse\_categorical_accuracy'\]) # 绘制训练和验证的损失值 plt.plot(epoch_range, history.history\['loss'\]) y\_pred = model.predict\_classes(X_test) confusion_matrix mat plot 结论: 在本教程中,我们训练了简单的卷积神经网络(CNN) 来对 图像进行分类...
ylabel('Accuracy') plt.ylim([min(plt.ylim()),1]) plt.title('Training and Validation Accuracy') plt.subplot(2, 1, 2) plt.plot(loss, label='Training Loss') plt.plot(val_loss, label='Validation Loss') plt.legend(loc='upper right') plt.ylabel('Cross Entropy') plt.ylim([0,5.0])...
plt.plot(epoch_range, history.history['sparse_categorical_accuracy']) # 绘制训练和验证的损失值 plt.plot(epoch_range, history.history['loss']) y_pred = model.predict_classes(X_test) confusion_matrix mat plot 结论: 在本教程中,我们训练了简单的卷积神经网络 (CNN) 来对 图像进行分类。从学习曲线...