keras訓練曲線,混淆矩陣,CNN層輸出可視化實例

更新時間：2020年06月15日 11:50:17 作者：3D_DLW

這篇文章主要介紹了keras訓練曲線,混淆矩陣,CNN層輸出可視化實例，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧

訓練曲線

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')

# 顯示訓練過程
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)

混淆矩陣

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()

# 顯示混淆矩陣
def plot_confuse(model, x_val, y_val):
 predictions = model.predict_classes(x_val)
 truelabel = y_val.argmax(axis=-1) # 將one-hot轉化為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形式輸入

效果：

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

CNN層輸出可視化

# 卷積網(wǎng)絡可視化
def visual(model, data, num_layer=1):
 # data:圖像array數(shù)據(jù)
 # layer:第n層的輸出
 data = np.expand_dims(data, axis=0) # 開頭加一維
 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 : 顯示第n層的輸出

效果

visual(model, data, 1) # 卷積層
visual(model, data, 2) # 激活層
visual(model, data, 3) # 規(guī)范化層
visual(model, data, 4) # 池化層

補充知識：Python sklearn.cross_validation.train_test_split及混淆矩陣實現(xiàn)

sklearn.cross_validation.train_test_split隨機劃分訓練集和測試集

一般形式：

train_test_split是交叉驗證中常用的函數(shù)，功能是從樣本中隨機的按比例選取train data和testdata，形式為：

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

參數(shù)解釋：

train_data：所要劃分的樣本特征集

train_target：所要劃分的樣本結果

test_size：樣本占比，如果是整數(shù)的話就是樣本的數(shù)量

random_state：是隨機數(shù)的種子。

隨機數(shù)種子：其實就是該組隨機數(shù)的編號，在需要重復試驗的時候，保證得到一組一樣的隨機數(shù)。比如你每次都填1，其他參數(shù)一樣的情況下你得到的隨機數(shù)組是一樣的。但填0或不填，每次都會不一樣。隨機數(shù)的產(chǎn)生取決于種子，隨機數(shù)和種子之間的關系遵從以下兩個規(guī)則：種子不同，產(chǎn)生不同的隨機數(shù)；種子相同，即使實例不同也產(chǎn)生相同的隨機數(shù)。

示例

fromsklearn.cross_validation import train_test_split
train= loan_data.iloc[0: 55596, :]
test= loan_data.iloc[55596:, :]
# 避免過擬合，采用交叉驗證，驗證集占訓練集20%，固定隨機種子（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（混淆矩陣實現(xiàn)實例）

print(__doc__)

import numpy as np
import matplotlib.pyplot as plt

from sklearn import svm, datasets
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix

# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Split the data into a training set and a test set
X_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 results
classifier = 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 matrix
cm = 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()

以上這篇keras訓練曲線,混淆矩陣,CNN層輸出可視化實例就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持腳本之家。

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