TypeError: 'History' object 不可调用

Question

我正在尝试实施 saliency_map。 我正在使用 DenseNet121 并且我适合 model. 粗略的片段：

for train_index, val_index in skf.split(X_train, y_train):
    X_train_fold, X_val_fold = X_train[train_index], X_train[val_index]
    y_train_fold, y_val_fold = y_train[train_index], y_train[val_index]
    i = i+1;
    print("Fold:",i)
    DenseNet121 = model.fit(datagen.flow(X_train_fold, y_train_fold, batch_size=32), epochs=10, verbose=1,validation_data=(X_val_fold,y_val_fold) ,callbacks=[ es_callback])

saliency_map 的代码片段：

# Function to generate saliency maps
def generate_saliency_map(model, X, y):
    # Convert numpy arrays to TensorFlow tensors
    X = tf.convert_to_tensor(X)
    y = tf.convert_to_tensor(y)
    X = tf.expand_dims(X, axis=0)
    with tf.GradientTape() as tape:
        tape.watch(X)
        output_tensor = model(X)
        output_class = tf.math.argmax(output_tensor, axis=-1)
        one_hot = tf.one_hot(output_class, depth=4)
        loss = tf.reduce_sum(output_tensor * one_hot, axis=-1)
    grads = tape.gradient(loss, X)
    saliency_map = tf.reduce_max(tf.abs(grads), axis=-1)
    return saliency_map
# Generate saliency maps for a few test images
for i in range(5):
    # print(X_test[i].shape)
    saliency_map = generate_saliency_map(DenseNet121, X_test[i], y_test[i])
    plt.imshow(saliency_map, cmap='gray')
    plt.show()

错误： TypeError: 'History' object is not callable

我附上一张图片以便更好地理解错误。

Answer 1

此行用历史记录 object 覆盖您之前的 Keras model。

DenseNet121 = model.fit(datagen.flow(X_train_fold, y_train_fold, batch_size=32), epochs=10, verbose=1,validation_data=(X_val_fold,y_val_fold) ,callbacks=[ es_callback])

如果你想存储训练的历史，你可以声明一个新变量

history = model.fit(datagen.flow(X_train_fold, y_train_fold, batch_size=32), epochs=10, verbose=1,validation_data=(X_val_fold,y_val_fold) ,callbacks=[es_callback])

Answer 2

您需要在generate_saliency_map中使用model而不是DenseNet121作为参数，因为DenseNet121保存历史 object 而不是 model。像这样，

# Generate saliency maps for a few test images
for i in range(5):
    # print(X_test[i].shape)
    saliency_map = generate_saliency_map(model, X_test[i], y_test[i])
    plt.imshow(saliency_map, cmap='gray')
    plt.show()