Inception Resnet v2 使用遷移學習測試准確率不佳

Question

我想使用遷移學習和 Inception Resnet v2 對 BreakHis 數據集 ( https://www.kaggle.com/ambarish/breakhis ) 中的乳腺癌組織病理學圖像進行二進制分類。 目標是通過向模型添加兩個神經元來凍結所有層並訓練全連接層。 特別是，最初我想考慮與放大系數 40X（良性：625，惡性：1370）相關的圖像。 以下是我所做的總結：

• 我閱讀了圖像並將它們調整為 150x150
• 我將數據集划分為訓練集、驗證集和測試集
• 我加載預訓練的網絡 Inception Resnet v2
• 我凍結了我添加兩個神經元進行二元分類的所有層（1 =“良性”，0 =“惡性”）
• 我使用 Adam 方法作為激活函數來編譯模型
• 我進行培訓
• 我做出預測
• 我計算精度

這是代碼：

data = dataset[dataset["Magnificant"]=="40X"]
def preprocessing(dataset, img_size):
    # images
    X = []
    # labels 
    y = []
    
    i = 0
    for image in list(dataset["Path"]):
        # Ridimensiono e leggo le immagini
        X.append(cv2.resize(cv2.imread(image, cv2.IMREAD_COLOR), 
                            (img_size, img_size), interpolation=cv2.INTER_CUBIC))
        basename = os.path.basename(image)
        
        # Get labels
        if dataset.loc[i][2] == "benign":
            y.append(1)
        else:
            y.append(0)
        i = i+1
    return X, y

X, y = preprocessing(data, 150)
X = np.array(X)
y = np.array(y)
# Splitting
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify = y_40, shuffle=True, random_state=1)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1) 

conv_base = InceptionResNetV2(weights='imagenet', include_top=False, input_shape=[150, 150, 3])   

# Freezing
for layer in conv_base.layers:
    layer.trainable = False

model = models.Sequential()
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dense(1, activation='sigmoid'))

opt = tf.keras.optimizers.Adam(learning_rate=0.0002)

loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)

model.compile(loss=loss, optimizer=opt, metrics = ["accuracy", tf.metrics.AUC()])

batch_size = 32

train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255) 
train_generator = train_datagen.flow(X_train, y_train, batch_size=batch_size) 
val_generator = val_datagen.flow(X_val, y_val, batch_size=batch_size)

ntrain =len(X_train)
nval = len(X_val)
len(y_train)
epochs = 70
history = model.fit_generator(train_generator,
                              steps_per_epoch=ntrain // batch_size,
                              epochs=epochs,
                              validation_data=val_generator,
                              validation_steps=nval // batch_size)

這是最后一個時期訓練的輸出：

Epoch 70/70
32/32 [==============================] - 3s 84ms/step - loss: 0.0499 - accuracy: 0.9903 - auc_5: 0.9996 - val_loss: 0.5661 - val_accuracy: 0.8250 - val_auc_5: 0.8521

我做出預測：

test_datagen = ImageDataGenerator(rescale=1./255) 
x = X_test
y_pred = model.predict(test_datagen.flow(x))

y_p = []
for i in range(len(y_pred)):
    if y_pred[i] > 0.5:
        y_p.append(1)
    else:
        y_p.append(0)

我計算精度：

from sklearn.metrics import accuracy_score
accuracy =  accuracy_score(y_test, y_p)
print(accuracy)

這是我得到的准確度值：0.5459098497495827

為什么我的准確率這么低，我做了幾次測試，但總是得到相似的結果？

更新

我進行了以下更改，但始終得到相同的結果（僅放置代碼的修改部分）：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify = y, shuffle=True, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, stratify = y_train, shuffle=True, random_state=1)

model = models.Sequential()
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))

callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)

ntrain =len(X_train)
nval = len(X_val)
len(y_train)
epochs = 70
history = model.fit_generator(train_generator,
                              steps_per_epoch=ntrain // batch_size,
                              epochs=epochs,
                              validation_data=val_generator,
                              validation_steps=nval // batch_size, callbacks=[callback]) 

更新 2

我還將 from_logits 從 True 更改為 False，但當然這還不是問題。 我總是得到 57% 的准確率。

這是超過 30 個時期的 model.fit 輸出：

Epoch 1/30
32/32 [==============================] - 23s 202ms/step - loss: 0.7994 - accuracy: 0.6010 - auc: 0.5272 - val_loss: 0.5338 - val_accuracy: 0.7688 - val_auc: 0.7943
Epoch 2/30
32/32 [==============================] - 3s 87ms/step - loss: 0.5778 - accuracy: 0.7206 - auc: 0.7521 - val_loss: 0.4763 - val_accuracy: 0.7781 - val_auc: 0.8155
Epoch 3/30
32/32 [==============================] - 3s 85ms/step - loss: 0.5311 - accuracy: 0.7581 - auc: 0.7710 - val_loss: 0.4740 - val_accuracy: 0.7719 - val_auc: 0.8212
Epoch 4/30
32/32 [==============================] - 3s 85ms/step - loss: 0.4684 - accuracy: 0.7718 - auc: 0.8219 - val_loss: 0.4270 - val_accuracy: 0.8031 - val_auc: 0.8611
Epoch 5/30
32/32 [==============================] - 3s 83ms/step - loss: 0.4280 - accuracy: 0.7943 - auc: 0.8617 - val_loss: 0.4496 - val_accuracy: 0.7969 - val_auc: 0.8468
Epoch 6/30
32/32 [==============================] - 3s 88ms/step - loss: 0.4237 - accuracy: 0.8250 - auc: 0.8673 - val_loss: 0.3993 - val_accuracy: 0.7937 - val_auc: 0.8840
Epoch 7/30
32/32 [==============================] - 3s 85ms/step - loss: 0.4130 - accuracy: 0.8513 - auc: 0.8767 - val_loss: 0.4207 - val_accuracy: 0.7781 - val_auc: 0.8692
Epoch 8/30
32/32 [==============================] - 3s 85ms/step - loss: 0.3446 - accuracy: 0.8485 - auc: 0.9077 - val_loss: 0.4229 - val_accuracy: 0.7937 - val_auc: 0.8730
Epoch 9/30
32/32 [==============================] - 3s 85ms/step - loss: 0.3690 - accuracy: 0.8514 - auc: 0.9003 - val_loss: 0.4300 - val_accuracy: 0.8062 - val_auc: 0.8696
Epoch 10/30
32/32 [==============================] - 3s 100ms/step - loss: 0.3204 - accuracy: 0.8533 - auc: 0.9270 - val_loss: 0.4235 - val_accuracy: 0.7969 - val_auc: 0.8731
Epoch 11/30
32/32 [==============================] - 3s 86ms/step - loss: 0.3555 - accuracy: 0.8508 - auc: 0.9124 - val_loss: 0.4124 - val_accuracy: 0.8000 - val_auc: 0.8797
Epoch 12/30
32/32 [==============================] - 3s 85ms/step - loss: 0.3243 - accuracy: 0.8481 - auc: 0.9308 - val_loss: 0.3979 - val_accuracy: 0.7969 - val_auc: 0.8908
Epoch 13/30
32/32 [==============================] - 3s 85ms/step - loss: 0.3017 - accuracy: 0.8744 - auc: 0.9348 - val_loss: 0.4239 - val_accuracy: 0.8094 - val_auc: 0.8758
Epoch 14/30
32/32 [==============================] - 3s 89ms/step - loss: 0.3317 - accuracy: 0.8521 - auc: 0.9221 - val_loss: 0.4238 - val_accuracy: 0.8094 - val_auc: 0.8704
Epoch 15/30
32/32 [==============================] - 3s 85ms/step - loss: 0.2840 - accuracy: 0.8908 - auc: 0.9490 - val_loss: 0.4131 - val_accuracy: 0.8281 - val_auc: 0.8858
Epoch 16/30
32/32 [==============================] - 3s 85ms/step - loss: 0.2583 - accuracy: 0.8905 - auc: 0.9511 - val_loss: 0.3841 - val_accuracy: 0.8375 - val_auc: 0.9007
Epoch 17/30
32/32 [==============================] - 3s 87ms/step - loss: 0.2810 - accuracy: 0.8648 - auc: 0.9470 - val_loss: 0.3928 - val_accuracy: 0.8438 - val_auc: 0.8972
Epoch 18/30
32/32 [==============================] - 3s 89ms/step - loss: 0.2622 - accuracy: 0.8923 - auc: 0.9550 - val_loss: 0.3732 - val_accuracy: 0.8438 - val_auc: 0.9089
Epoch 19/30
32/32 [==============================] - 3s 84ms/step - loss: 0.2486 - accuracy: 0.8990 - auc: 0.9579 - val_loss: 0.4077 - val_accuracy: 0.8250 - val_auc: 0.8924
Epoch 20/30
32/32 [==============================] - 3s 85ms/step - loss: 0.2412 - accuracy: 0.9074 - auc: 0.9635 - val_loss: 0.4249 - val_accuracy: 0.8219 - val_auc: 0.8787
Epoch 21/30
32/32 [==============================] - 3s 84ms/step - loss: 0.2386 - accuracy: 0.9095 - auc: 0.9657 - val_loss: 0.4177 - val_accuracy: 0.8094 - val_auc: 0.8904
Epoch 22/30
32/32 [==============================] - 3s 99ms/step - loss: 0.2313 - accuracy: 0.8996 - auc: 0.9668 - val_loss: 0.4089 - val_accuracy: 0.8406 - val_auc: 0.8890
Epoch 23/30
32/32 [==============================] - 3s 86ms/step - loss: 0.2424 - accuracy: 0.9067 - auc: 0.9654 - val_loss: 0.4033 - val_accuracy: 0.8500 - val_auc: 0.8953
Epoch 24/30
32/32 [==============================] - 3s 85ms/step - loss: 0.2315 - accuracy: 0.9045 - auc: 0.9626 - val_loss: 0.3903 - val_accuracy: 0.8250 - val_auc: 0.9030
Epoch 25/30
32/32 [==============================] - 3s 85ms/step - loss: 0.2001 - accuracy: 0.9321 - auc: 0.9788 - val_loss: 0.4276 - val_accuracy: 0.8000 - val_auc: 0.8855
Epoch 26/30
32/32 [==============================] - 3s 87ms/step - loss: 0.2118 - accuracy: 0.9212 - auc: 0.9695 - val_loss: 0.4335 - val_accuracy: 0.8125 - val_auc: 0.8897
Epoch 27/30
32/32 [==============================] - 3s 85ms/step - loss: 0.2463 - accuracy: 0.8941 - auc: 0.9665 - val_loss: 0.4112 - val_accuracy: 0.8438 - val_auc: 0.8882
Epoch 28/30
32/32 [==============================] - 3s 85ms/step - loss: 0.2130 - accuracy: 0.9033 - auc: 0.9771 - val_loss: 0.3834 - val_accuracy: 0.8406 - val_auc: 0.9021
Epoch 29/30
32/32 [==============================] - 3s 86ms/step - loss: 0.2021 - accuracy: 0.9229 - auc: 0.9754 - val_loss: 0.3855 - val_accuracy: 0.8469 - val_auc: 0.9008
Epoch 30/30
32/32 [==============================] - 3s 88ms/step - loss: 0.1859 - accuracy: 0.9314 - auc: 0.9824 - val_loss: 0.4018 - val_accuracy: 0.8375 - val_auc: 0.8928

Answer 1

您必須在損失函數from_logits=True更改為from_logits=False 。 再次致謝-@ Frightera 。

Answer 2

您的模型似乎在某處過度擬合。 如果你能檢查一下就最好了。

• 做 10 折的 K-Fold 測試。 它會顯示真實的結果
• 在您的指標中，添加 F1 分數。 F1 值會讓您真正了解 TP 的 FP 和 FN 指標
• 添加一些增強（除了重新縮放），使模型對數據集的變化具有魯棒性。
• 調整訓練參數（如果你覺得）。

如果這些更改失敗，則模型可能無法學習圖像的偽影。 你應該繼續使用不同的模型！