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与Theano的Keras：损失减少但准确性没有变化

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这是我的代码 . 我尝试构建一个VGG 11层网络，混合了ReLu和ELu激活以及许多关于内核和活动的规范化 . 结果真的令人困惑：代码处于第10个时代 . 我在火车和火箭上的损失从2000减少到1.5，但我对火车和火箭的损失保持不变，为50％ . 有人可以向我解释一下吗？

# VGG 11
from keras.regularizers import l2
from keras.layers.advanced_activations import ELU
from keras.optimizers import Adam
model = Sequential()

model.add(Conv2D(64, (3, 3), kernel_initializer='he_normal', 
          kernel_regularizer=l2(0.0001), activity_regularizer=l2(0.0001), 
          input_shape=(1, 96, 96), activation='relu'))
model.add(Conv2D(64, (3, 3), kernel_initializer='he_normal', 
          kernel_regularizer=l2(0.0001), activity_regularizer=l2(0.0001), 
          activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, (3, 3), kernel_initializer='he_normal', 
          kernel_regularizer=l2(0.0001),activity_regularizer=l2(0.0001), 
          activation='relu'))
model.add(Conv2D(128, (3, 3), kernel_initializer='he_normal',     
          kernel_regularizer=l2(0.0001), activity_regularizer=l2(0.0001), 
          activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(256, (3, 3), kernel_initializer='he_normal',     
          kernel_regularizer=l2(0.0001), activity_regularizer=l2(0.0001), 
          activation='relu'))
model.add(Conv2D(256, (3, 3), kernel_initializer='he_normal',     
          kernel_regularizer=l2(0.0001), activity_regularizer=l2(0.0001), 
          activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(512, (3, 3), kernel_initializer='he_normal', 
          kernel_regularizer=l2(0.0001), activity_regularizer=l2(0.0001), 
          activation='relu'))
model.add(Conv2D(512, (3, 3), kernel_initializer='he_normal', 
          kernel_regularizer=l2(0.0001), activity_regularizer=l2(0.0001), 
          activation='relu'))
model.add(Conv2D(512, (3, 3), kernel_initializer='he_normal', 
          kernel_regularizer=l2(0.0001), activity_regularizer=l2(0.0001),     
          activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

# convert convolutional filters to flat so they can be feed to fully connected layers
model.add(Flatten())

model.add(Dense(2048, kernel_initializer='he_normal',
               kernel_regularizer=l2(0.0001), activity_regularizer=l2(0.01)))
model.add(ELU(alpha=1.0))
model.add(Dropout(0.5))

model.add(Dense(1024, kernel_initializer='he_normal',
               kernel_regularizer=l2(0.0001), activity_regularizer=l2(0.01)))
model.add(ELU(alpha=1.0))
model.add(Dropout(0.5))

model.add(Dense(2))
model.add(Activation('softmax'))

adammo = Adam(lr=0.0008, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy', optimizer=adammo, metrics=['accuracy'])
hist = model.fit(X_train, y_train, batch_size=48, epochs=20, verbose=1, validation_data=(X_val, y_val))
deep-learning keras loss acc

1 回答

  • 0

    这不是缺陷，事实上，它完全有可能！

    分类cross entropy loss不要求精度随损失减少而增加 . 这不是keras或theano中的错误，而是网络或数据问题 .

    对于您可能尝试执行的操作，此网络结构可能过于复杂 . 您应该删除一些正则化，仅使用ReLu，使用更少的图层，使用标准的adam优化器，更大的批次等 . 首先尝试使用像VGG16这样的keras'default models，

    如果你想看到他们的实现，为不同的VGG11结构编辑它 . 是这里：

    def VGG_16(weights_path=None):
    model = Sequential()
    model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(Flatten())
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1000, activation='softmax'))

    if weights_path:
        model.load_weights(weights_path)

    return model

    你可以看到它更简单 . 它只使用依赖（这些日子已经流行）没有正规化，不同的卷积结构等 . 根据您的需要修改它！

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