Question

这是我第一次发布问题，如果它的书面或结构不正确，请原谅我。

数据集包含TIF格式的图像。这意味着我正在运行3D CNN。

这些图像是模拟的X射线图像，数据集有2类；正常和异常。对于正常，它们的标签为'0'，对于异常，它们的标签为'1'。

我的文件夹树如下：

火车
- 普通
- 异常
验证
- 普通
- 异常

我所做的是初始化2个数组。培训和 y_train 。

我运行了一个FOR循环，将 Normal 图像导入并将其附加到 train 中，并将'0'附加到 y_train中（用于添加的每个图像）。因此，如果我在训练中有10张“普通”图像，则在 y_train 中也将有10个'0'。

对于异常图片重复此操作，并将它们添加到 train 中，并将'1'添加到 y_train中。这意味着火车由普通图片和异常图片组成。并且 y_train 由'0'秒组成，后跟'1'秒。

对验证文件夹执行另一个FOR循环，从而对我的数组进行 test 和 y_test 。

这是我的神经网络代码：

def vgg1():
    model = Sequential()
    model.add(Conv3D(16, (3, 3, 3), activation="relu", padding="same", name="block1_conv1", input_shape=(128, 128, 128, 1), data_format="channels_last")) # 64
    model.add(Conv3D(16, (3, 3, 3), activation="relu", padding="same", name="block1_conv2", data_format="channels_last")) # 64
    model.add(MaxPooling3D((2,2, 2), strides=(2,2, 2),padding='same', name='block1_pool'))
    model.add(Dropout(0.5)) 

    model.add(Conv3D(32, (3, 3, 3), activation="relu", padding="same", name="block2_conv1", data_format="channels_last")) # 128
    model.add(Conv3D(32, (3, 3, 3), activation="relu", padding="same", name="block2_conv2", data_format="channels_last")) # 128
    model.add(MaxPooling3D((2,2, 2), strides=(2,2, 2),padding='same', name='block2_pool'))
    model.add(Dropout(0.5)) 
    model.add(Conv3D(64, (3, 3, 3), activation="relu", padding="same", name="block3_conv1", data_format="channels_last")) # 256
    model.add(Conv3D(64, (3, 3, 3), activation="relu", padding="same", name="block3_conv2", data_format="channels_last")) # 256
    model.add(Conv3D(64, (3, 3, 3), activation="relu", padding="same", name="block3_conv3", data_format="channels_last")) # 256
    model.add(MaxPooling3D((2,2, 2), strides=(2,2, 2),padding='same', name='block3_pool'))
    model.add(Dropout(0.5)) 

    model.add(Conv3D(128, (3, 3, 3), activation="relu", padding="same", name="block4_conv1", data_format="channels_last")) # 512
    model.add(Conv3D(128, (3, 3, 3), activation="relu", padding="same", name="block4_conv2", data_format="channels_last")) # 512
    model.add(Conv3D(128, (3, 3, 3), activation="relu", padding="same", name="block4_conv3", data_format="channels_last")) # 512
    model.add(MaxPooling3D((2,2, 2), strides=(2,2, 2),padding='same', name='block4_pool'))
    model.add(Dropout(0.5)) 

    model.add(Conv3D(128, (3, 3, 3), activation="relu", padding="same", name="block5_conv1", data_format="channels_last")) # 512 
    model.add(Conv3D(128, (3, 3, 3), activation="relu", padding="same", name="block5_conv2", data_format="channels_last")) # 512 
    model.add(Conv3D(128, (3, 3, 3), activation="relu", padding="same", name="block5_conv3", data_format="channels_last")) # 512 
    model.add(MaxPooling3D((2,2, 2), strides=(2,2, 2),padding='same', name='block5_pool'))
    model.add(Dropout(0.5)) 

    model.add(Flatten(name='flatten'))
    model.add(Dense(4096, activation='relu',name='fc1')) 

    model.add(Dense(4096, activation='relu',name='fc2'))  

    model.add(Dense(2, activation='softmax', name='predictions'))  
    print(model.summary())
    return model

以下代码是 x_train ， y_train ， x_test ， y_test 和model.compile的初始化。

我也将标签转换为一键编码。

from keras.utils import to_categorical


model = vgg1()

model.compile(
  loss='categorical_crossentropy', 
  optimizer='adam',
  metrics=['accuracy']
)

x_train = np.load('/content/drive/My Drive/3D Dataset v2/x_train.npy')
y_train = np.load('/content/drive/My Drive/3D Dataset v2/y_train.npy')
y_train = to_categorical(y_train)
x_test = np.load('/content/drive/My Drive/3D Dataset v2/x_test.npy')
y_test = np.load('/content/drive/My Drive/3D Dataset v2/y_test.npy')
y_test = to_categorical(y_test)

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

这是我要强调的问题，这是恒定的训练准确性和验证准确性。

Train on 127 samples, validate on 31 samples
Epoch 1/25
127/127 [==============================] - 1700s 13s/step - loss: 1.0030 - accuracy: 0.7480 - val_loss: 0.5842 - val_accuracy: 0.7419
Epoch 2/25
127/127 [==============================] - 1708s 13s/step - loss: 0.5813 - accuracy: 0.7480 - val_loss: 0.5728 - val_accuracy: 0.7419
Epoch 3/25
127/127 [==============================] - 1693s 13s/step - loss: 0.5758 - accuracy: 0.7480 - val_loss: 0.5720 - val_accuracy: 0.7419
Epoch 4/25
127/127 [==============================] - 1675s 13s/step - loss: 0.5697 - accuracy: 0.7480 - val_loss: 0.5711 - val_accuracy: 0.7419
Epoch 5/25
127/127 [==============================] - 1664s 13s/step - loss: 0.5691 - accuracy: 0.7480 - val_loss: 0.5785 - val_accuracy: 0.7419
Epoch 6/25
127/127 [==============================] - 1666s 13s/step - loss: 0.5716 - accuracy: 0.7480 - val_loss: 0.5710 - val_accuracy: 0.7419
Epoch 7/25
127/127 [==============================] - 1676s 13s/step - loss: 0.5702 - accuracy: 0.7480 - val_loss: 0.5718 - val_accuracy: 0.7419
Epoch 8/25
127/127 [==============================] - 1664s 13s/step - loss: 0.5775 - accuracy: 0.7480 - val_loss: 0.5718 - val_accuracy: 0.7419
Epoch 9/25
127/127 [==============================] - 1660s 13s/step - loss: 0.5753 - accuracy: 0.7480 - val_loss: 0.5711 - val_accuracy: 0.7419
Epoch 10/25
127/127 [==============================] - 1681s 13s/step - loss: 0.5756 - accuracy: 0.7480 - val_loss: 0.5714 - val_accuracy: 0.7419
Epoch 11/25
127/127 [==============================] - 1679s 13s/step - loss: 0.5675 - accuracy: 0.7480 - val_loss: 0.5710 - val_accuracy: 0.7419
Epoch 12/25
127/127 [==============================] - 1681s 13s/step - loss: 0.5779 - accuracy: 0.7480 - val_loss: 0.5741 - val_accuracy: 0.7419
Epoch 13/25
127/127 [==============================] - 1682s 13s/step - loss: 0.5763 - accuracy: 0.7480 - val_loss: 0.5723 - val_accuracy: 0.7419
Epoch 14/25
127/127 [==============================] - 1685s 13s/step - loss: 0.5732 - accuracy: 0.7480 - val_loss: 0.5714 - val_accuracy: 0.7419
Epoch 15/25
127/127 [==============================] - 1685s 13s/step - loss: 0.5701 - accuracy: 0.7480 - val_loss: 0.5710 - val_accuracy: 0.7419
Epoch 16/25
127/127 [==============================] - 1678s 13s/step - loss: 0.5704 - accuracy: 0.7480 - val_loss: 0.5733 - val_accuracy: 0.7419
Epoch 17/25
127/127 [==============================] - 1663s 13s/step - loss: 0.5692 - accuracy: 0.7480 - val_loss: 0.5710 - val_accuracy: 0.7419
Epoch 18/25
127/127 [==============================] - 1657s 13s/step - loss: 0.5731 - accuracy: 0.7480 - val_loss: 0.5717 - val_accuracy: 0.7419
Epoch 19/25
127/127 [==============================] - 1674s 13s/step - loss: 0.5708 - accuracy: 0.7480 - val_loss: 0.5712 - val_accuracy: 0.7419
Epoch 20/25
127/127 [==============================] - 1666s 13s/step - loss: 0.5795 - accuracy: 0.7480 - val_loss: 0.5730 - val_accuracy: 0.7419
Epoch 21/25
127/127 [==============================] - 1671s 13s/step - loss: 0.5635 - accuracy: 0.7480 - val_loss: 0.5753 - val_accuracy: 0.7419
Epoch 22/25
127/127 [==============================] - 1672s 13s/step - loss: 0.5713 - accuracy: 0.7480 - val_loss: 0.5718 - val_accuracy: 0.7419
Epoch 23/25
127/127 [==============================] - 1672s 13s/step - loss: 0.5666 - accuracy: 0.7480 - val_loss: 0.5711 - val_accuracy: 0.7419
Epoch 24/25
127/127 [==============================] - 1669s 13s/step - loss: 0.5695 - accuracy: 0.7480 - val_loss: 0.5724 - val_accuracy: 0.7419
Epoch 25/25
127/127 [==============================] - 1663s 13s/step - loss: 0.5675 - accuracy: 0.7480 - val_loss: 0.5721 - val_accuracy: 0.7419

出什么问题了，我该怎么办才能纠正？我认为保持恒定的精度是不可取的。

从第一个纪元开始，培训准确性和验证准确性就保持不变

0 个答案: