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

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) plt.figure() plot_confusion_matrix(conf_mat, range(np.max(truelabel)+1))

其中y_val以one-hot形式输入

效果：

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

CNN层输出可视化

# 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()

num_layer : 显示第n层的输出

效果

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

补充知识：Python sklearn.cross_validation.train_test_split及混淆矩阵实现

sklearn.cross_validation.train_test_split随机划分训练集和测试集

一般形式：

train_test_split是交叉验证中常用的函数，功能是从样本中随机的按比例选取train data和testdata，形式为：

X_train,X_test, y_train, y_test =
cross_validation.train_test_split(train_data,train_target,test_size=0.4, random_state=0)

参数解释：

train_data：所要划分的样本特征集

train_target：所要划分的样本结果

test_size：样本占比，如果是整数的话就是样本的数量

random_state：是随机数的种子。

随机数种子：其实就是该组随机数的编号，在需要重复试验的时候，保证得到一组一样的随机数。比如你每次都填1，其他参数一样的情况下你得到的随机数组是一样的。但填0或不填，每次都会不一样。随机数的产生取决于种子，随机数和种子之间的关系遵从以下两个规则：种子不同，产生不同的随机数；种子相同，即使实例不同也产生相同的随机数。

示例

fromsklearn.cross_validation import train_test_splittrain= loan_data.iloc[0: 55596, :]test= loan_data.iloc[55596:, :]# 避免过拟合，采用交叉验证，验证集占训练集20%，固定随机种子（random_state)train_X,test_X, train_y, test_y = train_test_split(train, target, test_size = 0.2, random_state = 0)train_y= train_y['label']test_y= test_y['label']

plot_confusion_matrix.py（混淆矩阵实现实例）

print(__doc__)import numpy as npimport matplotlib.pyplot as pltfrom sklearn import svm, datasetsfrom sklearn.cross_validation import train_test_splitfrom sklearn.metrics import confusion_matrix# import some data to play withiris = datasets.load_iris()X = iris.datay = iris.target# Split the data into a training set and a test setX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)# Run classifier, using a model that is too regularized (C too low) to see# the impact on the resultsclassifier = svm.SVC(kernel='linear', C=0.01)y_pred = classifier.fit(X_train, y_train).predict(X_test)def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues): plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(iris.target_names)) plt.xticks(tick_marks, iris.target_names, rotation=45) plt.yticks(tick_marks, iris.target_names) plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label')# Compute confusion matrixcm = confusion_matrix(y_test, y_pred)np.set_printoptions(precision=2)print('Confusion matrix, without normalization')print(cm)plt.figure()plot_confusion_matrix(cm)# Normalize the confusion matrix by row (i.e by the number of samples# in each class)cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]print('Normalized confusion matrix')print(cm_normalized)plt.figure()plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')plt.show()

以上这篇keras训练曲线,混淆矩阵,CNN层输出可视化实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。