我需要帮助来完成使用CNN对乳腺癌图像进行分类的代码。
我需要从随机图像进行预测的模型，但是对于所有总共306个图像，它始终给出一个预测类（有创）。 另外，我需要计算准确性和混淆矩阵。 感谢您的帮助。 代码的某些部分如下：
s=100
X_train = []
y_train = []
for folder in os.listdir(trainpath +'TrainingSet') :
files = gb.glob(pathname= str( trainpath +'TrainingSet//' + folder + '/*.png'))
for file in files:
image = cv2.imread(file)
image_array = cv2.resize(image , (s,s))
X_train.append(list(image_array))
y_train.append(code[folder])
X_pred = []
s=100
files = gb.glob(pathname= str(testpath + 'ValidationSet/*.png'))
for file in files:
image = cv2.imread(file)
image_array = cv2.resize(image , (s,s))
X_pred.append(list(image_array))
X_train = np.array(X_train)
y_train = np.array(y_train)
X_pred_array = np.array(X_pred)
print(f'X_train shape is {X_train.shape}')
print(f'y_train shape is {y_train.shape}')
print(f'X_pred shape is {X_pred_array.shape}')
KerasModel = keras.models.Sequential([
keras.layers.Conv2D(200,kernel_size=(3,3),activation='relu',input_shape=(s,s,3)),
keras.layers.Conv2D(150,kernel_size=(3,3),activation='relu'),
keras.layers.MaxPool2D(4,4),
keras.layers.Conv2D(120,kernel_size=(3,3),activation='relu'),
keras.layers.Conv2D(80,kernel_size=(3,3),activation='relu'),
keras.layers.Conv2D(50,kernel_size=(3,3),activation='relu'),
keras.layers.MaxPool2D(4,4),
keras.layers.Flatten() ,
keras.layers.Dense(120,activation='relu') ,
keras.layers.Dense(100,activation='relu') ,
keras.layers.Dense(50,activation='relu') ,
keras.layers.Dropout(rate=0.5) ,
keras.layers.Dense(3,activation='softmax') ,
])
KerasModel.compile(optimizer ='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
print('Model Details are : ')
print(KerasModel.summary())
epochs = 15
ThisModel = KerasModel.fit(X_train, y_train, epochs=epochs,batch_size=16,verbose=1, callbacks=[learn_control, checkpoint])
ModelLoss, ModelAccuracy = KerasModel.evaluate(X_train, y_train)
print('Total Loss is {}'.format(ModelLoss))
print('Total Accuracy is {}'.format(ModelAccuracy ))
y_result = KerasModel.predict(X_pred_array)
print('Prediction Shape is {}'.format(y_result.shape))
import warnings
warnings.filterwarnings('always')
warnings.filterwarnings('ignore')
import fnmatch
import os
from sklearn.metrics import confusion_matrix
image_path = 'Data\ValidationSet'
for filename in os.listdir(image_path):
filename = os.path.basename(filename)
Actual_list = []
Predicted_list = []
if any(map(filename.startswith, 'InSitu_')):
Actual='Insitu'
if any(map(filename.startswith, 'SOB_B_')):
Actual='Benign'
if any(map(filename.startswith, 'SOB_M_')):
Actual='Invasive'
plt.figure(figsize=(20,40))
for n , i in enumerate(list(np.random.randint(0,len(X_pred),306))) :
plt.subplot(34,9,n+1)
plt.imshow(X_pred[i])
plt.axis('off')
Predicted= getcode(np.argmax(y_result[i]))
plt.title('Predicted: ' + Predicted +'\n' + 'Actual:' + Actual)
Actual_list.append(Actual)
Predicted_list.append(Predicted)
培训过程的输出：
Epoch 1/15
1231/1231 [===] - 284s 230ms/step - loss: 0.6721 - accuracy: 0.7295
Epoch 2/15
1231/1231 [===] - 279s 227ms/step - loss: 0.6698 - accuracy: 0.7295
Epoch 3/15
1231/1231 [===] - 259s 211ms/step - loss: 0.6784 - accuracy: 0.7295
Epoch 4/15
1231/1231 [===] - 263s 214ms/step - loss: 0.6751 - accuracy: 0.7295
Epoch 5/15
1231/1231 [===] - 255s 207ms/step - loss: 0.6715 - accuracy: 0.7295
Epoch 6/15
1231/1231 [===] - 255s 207ms/step - loss: 0.6695 - accuracy: 0.7295
Epoch 7/15
1231/1231 [===] - 253s 205ms/step - loss: 0.6737 - accuracy: 0.7295
Epoch 8/15
1231/1231 [===] - 256s 208ms/step - loss: 0.6730 - accuracy: 0.7295
Epoch 9/15
1231/1231 [===] - 263s 214ms/step - loss: 0.6733 - accuracy: 0.7295
Epoch 10/15
1231/1231 [===] - 277s 225ms/step - loss: 0.6741 - accuracy: 0.7295
Epoch 11/15
1231/1231 [===] - 279s 227ms/step - loss: 0.6698 - accuracy: 0.7295
Epoch 12/15
1231/1231 [===] - 254s 206ms/step - loss: 0.6743 - accuracy: 0.7295
Epoch 13/15
1231/1231 [===] - 255s 207ms/step - loss: 0.6758 - accuracy: 0.7295
Epoch 14/15
1231/1231 [===] - 254s 206ms/step - loss: 0.6722 - accuracy: 0.7295
Epoch 15/15
1231/1231 [===] - 257s 209ms/step - loss: 0.6706 - accuracy: 0.7295
总损失和准确性：
1231/1231 [==============================]-114s 93ms / step 总损失为0.667820717773236 总准确度为0.7294881939888
答案 0 :(得分：0)
首先，您需要找到正确和不正确的预测。
def get_tested_predictions(Actual_list, Predicted_list):
correct_preds = []
incorrect_preds = []
correct_count = 0
incorrect_count = 0
for ii in range(len(Actual_list)):
label_idx = np.argmax(Actual_list[ii])
prediction_idx = np.argmax(Predicted_list[ii])
prediction = Predicted_list[ii][prediction_idx]
if prediction_idx == label_idx:
correct_preds.append(prediction)
incorrect_preds.append(np.nan)
correct_count = correct_count + 1
if prediction_idx != label_idx:
incorrect_preds.append(prediction)
correct_preds.append(np.nan)
incorrect_count = incorrect_count + 1
return correct_preds, correct_count, incorrect_preds, incorrect_count
correct_preds, correct_count, incorrect_preds, incorrect_count = get_tested_predictions(Actual_list,Predicted_list)
print("Correct predictions: %d/%d" % (correct_count,len(Predicted_list)))
print("Incorrect predictions: %d/%d" % (incorrect_count,len(Predicted_list)))
现在，我们需要从sklearn导入混淆矩阵并将其绘制：
import itertools
from sklearn.metrics import confusion_matrix
y_trues = [np.argmax(ii) for ii in Actual_list]
y_preds = [np.argmax(ii) for ii in Predicted_list]
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
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)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
cnf_matrix = confusion_matrix(y_trues, y_preds)
class_names=list(set(np.concatenate((y_trues,y_preds),axis=0)))
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix, without normalization')
plt.show()
NameError现在已修复。