python - CNN 与 Keras - 准确度非常低且损失为负

问题描述

我正在尝试构建一个 CNN，将与皮肤癌相关的图像分为七类。我对 CNN 的概念相对较新，并且一直在调整狗/猫分类用例以适应已知的皮肤癌数据库挑战。问题是损失和准确性极低，并且在各个时期都是恒定的。不过，我不确定问题出在哪里——我的第一个假设是使用的图像数量太少：用于训练的 436 个样本和 109 个验证样本。我使用的图像数量从 10000+ 减少，因为我使用的是我的 macbook pro。

脚本：

    import tensorflow as tf
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D

    import numpy as np
    import pandas as pd

    import matplotlib.pyplot as plt

    import sys
    import os
    import cv2

    DATA_DIR = "/Users/namefolder/PycharmProjects/skin-cancer/HAM10000_images_part_1"

    metadata = pd.read_csv(os.path.join(DATA_DIR, 'HAM10000_metadata.csv'))

    lesion_type_dict = {'nv': 'Melanocytic nevi',
        'mel': 'Melanoma',
        'bkl': 'Benign keratosis-like lesions ',
        'bcc': 'Basal cell carcinoma',
        'akiec': 'Actinic keratoses',
        'vasc': 'Vascular lesions',
        'df': 'Dermatofibroma'}

    metadata['cell_type'] = metadata['dx'].map(lesion_type_dict.get)
    metadata['dx_code'] = pd.Categorical(metadata['dx']).codes

    # save array of image-id and diagnosis-type (categorical)
    metadata = metadata[['image_id', 'dx', 'dx_type', 'dx_code']]

    training_data = []

    IMG_SIZE=40

    # preparing training data

    def creating_training_data(path):
        for img in os.listdir(path):
            try:
                img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
                for index, row in metadata.iterrows():
                    if img == row['image_id']+'.jpg':
                        try:
                            training_data.append([new_array, row['dx_code']])
                        except Exception as ee:
                            pass
            except Exception as e:
                pass

        return training_data

    training_data = creating_training_data(DATA_DIR)

    import random

    random.shuffle(training_data)

    # Splitting data into X features and Y label
    X_train = []
    y_train = []
    for features, label in training_data:
        X_train.append(features)
        y_train.append(label)

    # Reshaping of the data - required by Tensorflow and Keras (*necessary step of deep-learning using these repos)
    X_train = np.array(X_train).reshape(-1, IMG_SIZE, IMG_SIZE, 1)

    # Normalize data - to reduce processing requirements
    X_train = X_train/255.0

    # model configuration
    model = Sequential()
    model.add(Conv2D(64, (3,3), input_shape = X_train.shape[1:]))
    model.add(Activation("relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(64, (3,3)))
    model.add(Activation("relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())
    model.add(Dense(64))

    model.add(Dense(1))
    model.add(Activation("softmax"))

    model.compile(loss="mean_squared_error",
                 optimizer="adam",
                 metrics=["accuracy"])

训练模型：

 Model fitting output:

            Train on 436 samples, validate on 109 samples
            Epoch 1/20
            436/436 [==============================] - 1s 2ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 2/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 3/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 4/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 5/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 6/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 7/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 8/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 9/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 10/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 11/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 12/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 13/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 14/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 15/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 16/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 17/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 18/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 19/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642
            Epoch 20/20
            436/436 [==============================] - 1s 1ms/sample - loss: 11.7890 - acc: 0.0688 - val_loss: 13.6697 - val_acc: 0.0642

型号总结：

Model: "sequential_16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_30 (Conv2D)           (None, 38, 38, 64)        640       
_________________________________________________________________
activation_44 (Activation)   (None, 38, 38, 64)        0         
_________________________________________________________________
max_pooling2d_30 (MaxPooling (None, 19, 19, 64)        0         
_________________________________________________________________
conv2d_31 (Conv2D)           (None, 17, 17, 64)        36928     
_________________________________________________________________
activation_45 (Activation)   (None, 17, 17, 64)        0         
_________________________________________________________________
max_pooling2d_31 (MaxPooling (None, 8, 8, 64)          0         
_________________________________________________________________
flatten_14 (Flatten)         (None, 4096)              0         
_________________________________________________________________
dense_28 (Dense)             (None, 64)                262208    
_________________________________________________________________
dense_29 (Dense)             (None, 1)                 65        
_________________________________________________________________
activation_46 (Activation)   (None, 1)                 0         
=================================================================
Total params: 299,841
Trainable params: 299,841
Non-trainable params: 0

有人可以告诉我是否是这种情况或这样做。您看到我需要更改/修复的其他区域吗？

提前致谢！

标签： pythontensorflowkeras

python - CNN 与 Keras - 准确度非常低且损失为负 - 显然是我的错误

问题描述

解决方案

推荐阅读