具有低/恒定精度的 Keras CNN

数据挖掘 机器学习 Python 深度学习 张量流 喀拉斯
2021-09-29 21:25:30

我正在处理街景门牌号识别问题。我正在尝试用 Keras 训练 CNN。

这是我准备输入的方式：

from PIL import Image
from PIL import ImageFilter
train_folders = 'sv_train/train'
test_folders = 'test'
extra_folders = 'extra'
SV_IMG_SIZE = 28
SV_CHANNELS = 1
train_imsize = np.ndarray([len(train_data),2])
k = 500
sv_images = []
max_images = 20000#len(train_data)
max_digits = 5
sv_labels = np.ones([max_images, max_digits], dtype=int) * 10 # init to 10 cause it would be no digit
nboxes = [[] for i in range(max_images)]
print ("%d to load" % len(train_data))
def getBBox(i,perc):
    '''
    Given i, the desired i.png, returns
    x_min, y_min, x_max, y_max,
    the four numbers which define the small rectangular bounding
    box that contains all individual character bounding boxes
    '''
    boxes = train_data[i]['boxes'] 
    x_min=9990
    y_min=9990
    x_max=0
    y_max=0
    for bid,b in enumerate(boxes):
        x_min = b['left'] if b['left'] <= x_min else x_min
        y_min = b['top'] if b['top'] <= y_min else y_min
        x_max = b['left']+b['width'] if  b['left']+b['width'] >= x_max else x_max
        y_max = b['top']+b['height'] if b['top']+b['height'] >= y_max else y_max

    dy = y_max-y_min
    dx = x_max-x_min
    dpy = dy*perc
    dpx = dx*perc
    nboxes[i]=[dpx,dpy,dx,dy]
    return x_min-dpx, y_min-dpy, x_max+dpx, y_max+dpy

for i in range(max_images):
    print (" \r%d" % i ,end="")
    filename = train_data[i]['filename']
    fullname = os.path.join(train_folders, filename)
    boxes = train_data[i]['boxes']
    label = [10,10,10,10,10]
    lb = len(boxes)
    if lb <= max_digits:
        im = Image.open(fullname)
        x_min, y_min, x_max, y_max = getBBox(i,0.3)
        im = im.crop([x_min,y_min,x_max,y_max])
        owidth, oheight = im.size
        wr = SV_IMG_SIZE/float(owidth)
        hr = SV_IMG_SIZE/float(oheight)
        for bid,box in  enumerate(boxes):
            sv_labels[i][max_digits-lb+bid] = int(box['label'])

        box = nboxes[i]
        box[0]*=wr
        box[1]*=wr
        box[2]*=hr
        box[3]*=hr
        im = im.resize((SV_IMG_SIZE,SV_IMG_SIZE),Image.ANTIALIAS)
        img = img - np.mean(img)
        im = im.filter(ImageFilter.EDGE_ENHANCE)
        img = img - np.mean(img)

        array = np.asarray(im)
        array =  array.reshape((SV_IMG_SIZE,SV_IMG_SIZE,3)).astype(np.float32)
        na = np.zeros([SV_IMG_SIZE,SV_IMG_SIZE],dtype=int)
        for x in range (array.shape[0]):
            for y in range (array.shape[1]):
                na[x][y]=np.average(array[x][y][:])
        na = na.reshape(SV_IMG_SIZE,SV_IMG_SIZE,1)
        #print(array.shape)
        sv_images.append(na.astype(np.float32))

sv_train, sv_validation, svt_labels, svv_labels = train_test_split(sv_images, sv_labels, test_size=0.33, random_state=42)

以下是我创建和训练模型的方式：

model = Sequential()
x = Input((28, 28,1))

y = Convolution2D(16, 3, 3, border_mode="same")(x)
#y = MaxPooling2D(pool_size = (2, 2), strides = (2, 2)) (y)
#y = Dropout(0.25)(y)

y = Convolution2D(32, 4, 4, border_mode="same")(y)
y = MaxPooling2D(pool_size = (3, 3)) (y)
#y = Dropout(0.25)(y)

y = Convolution2D(64, 5, 5, border_mode="same", activation="relu")(y)
y = MaxPooling2D((2, 2))(y)
#y = Dropout(0.25)(y)

y = Convolution2D(128, 5, 5, border_mode="same", activation="relu")(y)
y = MaxPooling2D((2, 2))(y)
#y = Dropout(0.25)(y)


y = Flatten()(y)
y = Dense(1024, activation="relu")(y)

digit1 = Dense(11, activation="softmax")(y)
digit2 = Dense(11, activation="softmax")(y)
digit3 = Dense(11, activation="softmax")(y)
digit4 = Dense(11, activation="softmax")(y)
digit5 = Dense(11, activation="softmax")(y)
model = Model(input=x, output=[digit1, digit2, digit3,digit4,digit5])


model.compile(optimizer='adam',
          loss='sparse_categorical_crossentropy',
          metrics=['accuracy'])
print(model.layers[0].output_shape)
print(model.layers[2].output_shape)
print(model.layers[4].output_shape)
print(model.layers[6].output_shape)
print(model.layers[8].output_shape)

sv_train_labels = [svt_labels[:,0],svt_labels[:,1],svt_labels[:,2],svt_labels[:,3],svt_labels[:,4]]
sv_validation_labels = [svv_labels[:,0],svv_labels[:,1],svv_labels[:,2],svv_labels[:,3],svv_labels[:,4]]

model.fit(sv_train, sv_train_labels, nb_epoch=10, batch_size=64,validation_data=(sv_validation, sv_validation_labels))

问题是我得到的精度非常低，在每个时期都保持相同的值：

Train on 13400 samples, validate on 6600 samples
Epoch 1/10
13400/13400 [==============================] - 78s - loss: 34.7407 - dense_740_loss: 0.1161 - dense_741_loss: 0.6879 - dense_742_loss: 4.7988 - dense_743_loss: 14.7893 - dense_744_loss: 14.3486 - dense_740_acc: 0.9902 - dense_741_acc: 0.9542 - dense_742_acc: 0.7001 - dense_743_acc: 0.0810 - dense_744_acc: 0.1055 - val_loss: 34.7760 - val_dense_740_loss: 0.0049 - val_dense_741_loss: 0.7131 - val_dense_742_loss: 4.8721 - val_dense_743_loss: 14.8091 - val_dense_744_loss: 14.3769 - val_dense_740_acc: 0.9997 - val_dense_741_acc: 0.9558 - val_dense_742_acc: 0.6977 - val_dense_743_acc: 0.0812 - val_dense_744_acc: 0.1080
Epoch 2/10
13400/13400 [==============================] - 70s - loss: 34.7032 - dense_740_loss: 0.0036 - dense_741_loss: 0.6760 - dense_742_loss: 4.7861 - dense_743_loss: 14.8118 - dense_744_loss: 14.4257 - dense_740_acc: 0.9998 - dense_741_acc: 0.9581 - dense_742_acc: 0.7031 - dense_743_acc: 0.0810 - dense_744_acc: 0.1050 - val_loss: 34.7760 - val_dense_740_loss: 0.0049 - val_dense_741_loss: 0.7131 - val_dense_742_loss: 4.8721 - val_dense_743_loss: 14.8091 - val_dense_744_loss: 14.3769 - val_dense_740_acc: 0.9997 - val_dense_741_acc: 0.9558 - val_dense_742_acc: 0.6977 - val_dense_743_acc: 0.0812 - val_dense_744_acc: 0.1080
1个回答

由于我没有你的数据，我只能给你一些建议。

  1. 10 个 epoch 可能太少了。在我的实践中，将应用 100 多个 epoch。我认为 100 个 epoch 对你来说是一个好的开始。
  2. 假设您的数据准备充分，CNN 的一些建议：
    1. 第一个和第二个卷积层应该有 ReLU 激活，至少没有线性激活。
    2. 为什么你的输出层有 11 个神经元？只有10位数，对吧？
    3. sparse_categorical_crossentropy用于稀疏输入。您sv_train_labels似乎是一个非稀疏数组，因此categorical_crossentropy更好。
    4. 你的 3x3、4x4、5x5 卷积层看起来很奇怪，但我不能给你一个理由（也许是其他人？）。如果我是你，我会使用 3x3 层。
    5. 如果要打印模型的输出形状，请尝试model.summary(). 这很棒！
    6. 如果我是你，我会尝试RMSprop而不是Adam。

如果你想尝试我的模型：

x = Input((28, 28, 1))

y = Convolution2D(16, 3, 3, activation='relu', border_mode='same')(x)
y = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(y)
y = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(y)
y = MaxPooling2D((2, 2))(y)
y = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(y)
y = MaxPooling2D((2, 2))(y)
y = Flatten()(y)
y = Dense(1024, activation='relu')(y)

digit1 = Dense(10, activation="softmax")(y)
digit2 = Dense(10, activation="softmax")(y)
digit3 = Dense(10, activation="softmax")(y)
digit4 = Dense(10, activation="softmax")(y)
digit5 = Dense(10, activation="softmax")(y)
model = Model(input=x, output=[digit1, digit2, digit3, digit4, digit5])

model.compile('RMSprop', 'categorical_crossentropy', ['accuracy'])
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