如何获得青光眼模型的预测

Question

我正在按照 Coursera 平台上的课程创建模型来预测青光眼。

我已经完成了所有事情，但仍然没有得到我想要的预测或结果。

我的代码是：

from keras import backend as K
import numpy as np


# create the base pre-trained model
base_model = DenseNet121(weights= '/Users/awabe/Desktop/Project/PapilaDB/ClinicalData/DenseNet-BC-121-32-no-top.h5', include_top=False)

x = base_model.output
x = tf.cast(x, dtype=tf.float32)

# add a global spatial average pooling layer
x = GlobalAveragePooling2D()(x)
x = tf.cast(x, dtype=tf.float32)

# and a logistic layer
predictions = Dense(len(labels), activation="sigmoid")(x)

model = Model(inputs=base_model.input, outputs=predictions)
model.compile(optimizer='adam', loss=get_weighted_loss(pos_weights, neg_weights))

历史：

history = model.fit_generator(train_generator, 
                              validation_data=valid_generator,
                              steps_per_epoch=25, 
                              validation_steps=34, 
                              epochs = 25)

plt.plot(history.history['loss'])
plt.ylabel("loss")
plt.xlabel("epoch")
plt.title("Training Loss Curve")
plt.show()

我使用 GradCAM 的可视化学习来生成热图，突出显示图像中的重要区域，以预测病理状况。

df = pd.read_csv("/Users/awabe/Desktop/Project/PapilaDB/ExpertsSegmentations/small train/small-train.csv")
IMAGE_DIR = "/Users/awabe/Desktop/Project/PapilaDB/ExpertsSegmentations/small train"

# only show the labels with top 4 AUC
labels_to_show = np.take(labels, np.argsort(auc_rocs)[::-1], )[:1]

之后我用这个：

def get_gradcam(image_path, gradcam_model):
    print(f"Loading image from: {image_path}")
    img = tf.keras.preprocessing.image.load_img(image_path, target_size=(224, 224))
    print("Image loaded successfully")

    img_tensor = tf.keras.preprocessing.image.img_to_array(img)
    img_tensor = np.expand_dims(img_tensor, axis=0)
    img_tensor /= 255.

    # Compute Grad-CAM using the custom model
    last_conv_layer_output, cam = gradcam_model(img_tensor)

    # Check if either last_conv_layer_output or cam is None
    if last_conv_layer_output is None or cam is None:
        print("Gradients are None. Cannot compute Grad-CAM.")
        return

    # Resize the CAM to the same size as the input image
    cam = tf.image.resize(cam, (img.shape[1], img.shape[0]))

    # Clip the values to keep only positive ones
    cam = tf.maximum(cam, 0)

    # Normalize the CAM
    cam /= tf.reduce_max(cam)

    # Convert the CAM to RGB
    cam = cv2.applyColorMap(np.uint8(255 * cam.numpy()), cv2.COLORMAP_JET)

    # Overlay the CAM on the input image
    heatmap = cv2.cvtColor(cam, cv2.COLOR_BGR2RGB)
    heatmap[np.where(cam.sum(axis=2) == 0)] = 0
    img = cv2.imread(image_path)
    superimposed_img = cv2.addWeighted(img, 0.5, heatmap, 0.5, 0)

    # Display the input image, CAM, and overlaid image
    plt.figure(figsize=(10, 10))
    plt.subplot(131)
    plt.imshow(img)
    plt.title('Input Image')
    plt.axis('off')
    plt.subplot(132)
    plt.imshow(cam)
    plt.title('CAM')
    plt.axis('off')
    plt.subplot(133)
    plt.imshow(superimposed_img)
    plt.title('CAM Overlay')
    plt.axis('off')

    # If you want to visualize gradients, add the following lines
    plt.figure(figsize=(10, 5))
    for i, grad in enumerate(last_conv_layer_output.numpy()[0]):
        plt.subplot(1, last_conv_layer_output.shape[-1], i + 1)
        plt.imshow(grad, cmap='viridis')
        plt.axis('off')
        plt.title(f'Channel {i}')

    plt.show()

# Call the function

gradcam_model = tf.keras.applications.DenseNet121(weights='imagenet', include_top=True)
image_path = "/Users/awabe/Desktop/Project/PapilaDB/ExpertsSegmentations/small train/Opht_cont_RET004OD.jpg"

def get_gradcam(image_path, gradcam_model):
    print(f"Loading image from: {image_path}")
    img = tf.keras.preprocessing.image.load_img(image_path, target_size=(224, 224))
    print("Image loaded successfully")

    img_tensor = tf.keras.preprocessing.image.img_to_array(img)
    img_tensor = np.expand_dims(img_tensor, axis=0)
    img_tensor /= 255.

    # Compute Grad-CAM using the custom model
    gradcam_result = gradcam_model(img_tensor)

    # Check if gradcam_result is a tuple (last_conv_layer_output, cam) or a single value
    if isinstance(gradcam_result, tuple):
        last_conv_layer_output, cam = gradcam_result
    else:
        last_conv_layer_output = gradcam_result
        cam = last_conv_layer_output  # Assuming the result is the activation map itself

    # ... Rest of the function remains the same

# Call the function
get_gradcam(image_path, gradcam_model)

这给了我：

从以下位置加载图像： /用户/awabe/桌面/项目/PapilaDB/ExpertsSegmentations/small train/Opht_cont_RET004OD.jpg 图片加载成功

最后我应该运行这段代码：

import util
from tensorflow.keras.preprocessing import image

IMAGE_DIR = "/Users/awabe/Desktop/Project/PapilaDB/ExpertsSegmentations/small train"

util.compute_gradcam(model, 'Opht_cont_RET004OD.jpg', IMAGE_DIR, df, labels, labels_to_show) 

它应该给我输出图像以及从 0 到 1 的预测或百分比。

但它给了我以下内容：

有什么我错过的东西或者我应该添加的代码或功能吗？

Answer 1

我很确定你不会要求它给你预测。您在这里所做的是计算

GradCAM

可视化，正如您所说，它突出显示了可能触发模型的图像区域。为了真正获得布尔值的预测，您应该运行

model.predict(...)

。