machine-learning - CNN学习停滞

Question

我已经创建了 CNN 的模拟，并尝试在视频数据集上使用。我将测试数据设置为所有帧上的所有一张图像(对于正例)和 0(对于负例)。我认为这会学得很快。但它根本不动。在 Windows 10 64 位上使用当前版本的 Keras 和 Tensorflow。

第一个问题，我的逻辑有问题吗？我是否应该期望这些测试数据的学习能够快速达到高精度？

我的模型或参数有问题吗？我已经尝试了很多改变，但仍然遇到同样的问题。

样本量(56)是否太小？

# testing  feature extraction model. 
import time
import numpy as np, cv2
import sys
import os
import keras
import tensorflow as tf

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization
from keras.layers import Conv3D, MaxPooling3D

from keras.optimizers import SGD,rmsprop, adam

from keras import regularizers
from keras.initializers import Constant

from keras.models import Model

#set gpu options
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=.99, allocator_type = 'BFC') 
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True, gpu_options=gpu_options))
config = tf.ConfigProto()

batch_size = 5
num_classes = 1
epochs = 50
nvideos = 56
nframes = 55
nchan = 3
nrows = 480
ncols = 640

#load any single image, resize if needed
img = cv2.imread('C:\\Users\\david\\Documents\\AutonomousSS\\single frame.jpg',cv2.IMREAD_COLOR)
img = cv2.resize(img,(640,480))

x_learn = np.random.randint(0,255,(nvideos,nframes,nrows,ncols,nchan),dtype=np.uint8)
y_learn = np.array([[1],[1],[1],[0],[1],[0],[1],[0],[1],[0],
                    [1],[0],[0],[1],[0],[0],[1],[0],[1],[0],
                    [1],[0],[1],[1],[0],[1],[0],[0],[1],[1],
                    [1],[0],[1],[0],[1],[0],[1],[0],[1],[0],
                    [0],[1],[0],[0],[1],[0],[1],[0],[1],[0],
                    [1],[1],[0],[1],[0],[0]],np.uint8)

#each sample, each frame is either the single image for postive examples or 0 for negative examples.

for i in range (nvideos):
    if y_learn[i] == 0 : 
        x_learn[i]=0
    else:
        x_learn[i,:nframes]=img



#build model     
m_loss = 'mean_squared_error'
m_opt = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
m_met = 'acc' 


model = Sequential()

# 1st layer group
model.add(Conv3D(32, (3, 3,3), activation='relu',padding="same", name="conv1a", strides=(3, 3, 3),
                 kernel_initializer = 'glorot_normal',
                 trainable=False,
                 input_shape=(nframes,nrows,ncols,nchan)))
#model.add(BatchNormalization(axis=1))
model.add(Conv3D(32, (3, 3, 3), trainable=False, strides=(1, 1, 1), padding="same", name="conv1b", activation="relu"))
#model.add(BatchNormalization(axis=1))
model.add(MaxPooling3D(padding="valid", trainable=False, pool_size=(1, 5, 5), name="pool1", strides=(2, 2, 2)))


# 2nd layer group
model.add(Conv3D(128, (3, 3, 3), trainable=False, strides=(1, 1, 1), padding="same", name="conv2a", activation="relu"))
model.add(Conv3D(128, (3, 3, 3), trainable=False, strides=(1, 1, 1), padding="same", name="conv2b", activation="relu"))
#model.add(BatchNormalization(axis=1))
model.add(MaxPooling3D(padding="valid", trainable=False, pool_size=(1, 5, 5), name="pool2", strides=(2, 2, 2)))

# 3rd layer group
model.add(Conv3D(256, (3, 3, 3), trainable=False, strides=(1, 1, 1), padding="same", name="conv3a", activation="relu"))
model.add(Conv3D(256, (3, 3, 3), trainable=False, strides=(1, 1, 1), padding="same", name="conv3b", activation="relu"))
#model.add(BatchNormalization(axis=1))
model.add(MaxPooling3D(padding="valid", trainable=False, pool_size=(1, 5, 5), name="pool3", strides=(2, 2, 2)))

# 4th layer group
model.add(Conv3D(512, (3, 3, 3), trainable=False, strides=(1, 1, 1), padding="same", name="conv4a", activation="relu"))
model.add(Conv3D(512, (3, 3, 3), trainable=False, strides=(1, 1, 1), padding="same", name="conv4b", activation="relu"))
#model.add(BatchNormalization(axis=1))
model.add(MaxPooling3D(padding="valid", trainable=False, pool_size=(1, 5, 5), name="pool4", strides=(2, 2, 2)))

model.add(Flatten(name='flatten',trainable=False))

model.add(Dense(512,activation='relu', trainable=True,name='den0'))

model.add(Dense(num_classes,activation='softmax',name='den1'))
print (model.summary())

#compile model
model.compile(loss=m_loss,
              optimizer=m_opt,
              metrics=[m_met])
print ('compiled')


#set callbacks
from keras import backend as K
K.set_learning_phase(0) #set learning phase
tb = keras.callbacks.TensorBoard(log_dir=sample_root_path+'logs', histogram_freq=0,
                          write_graph=True, write_images=False)
tb.set_model(model)
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.2,verbose=1,
              patience=2, min_lr=0.000001)
reduce_lr.set_model(model)
ear_stop = keras.callbacks.EarlyStopping(monitor='loss', min_delta=0, patience=4, verbose=1, mode='auto')
ear_stop.set_model(model)


#fit

history = model.fit(x_learn, y_learn,
                    batch_size=batch_size,
                    callbacks=[reduce_lr,tb, ear_stop],
                    verbose=1,
                    validation_split=0.1,
                    shuffle = True,
                    epochs=epochs)


score = model.evaluate(x_learn, y_learn, batch_size=batch_size)
print(str(model.metrics_names) + ": " + str(score))

像往常一样，感谢您提供的所有帮助。

添加输出...

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv1a (Conv3D)              (None, 19, 160, 214, 32)  2624      
_________________________________________________________________
conv1b (Conv3D)              (None, 19, 160, 214, 32)  27680     
_________________________________________________________________
pool1 (MaxPooling3D)         (None, 10, 78, 105, 32)   0         
_________________________________________________________________
conv2a (Conv3D)              (None, 10, 78, 105, 128)  110720    
_________________________________________________________________
conv2b (Conv3D)              (None, 10, 78, 105, 128)  442496    
_________________________________________________________________
pool2 (MaxPooling3D)         (None, 5, 37, 51, 128)    0         
_________________________________________________________________
conv3a (Conv3D)              (None, 5, 37, 51, 256)    884992    
_________________________________________________________________
conv3b (Conv3D)              (None, 5, 37, 51, 256)    1769728   
_________________________________________________________________
pool3 (MaxPooling3D)         (None, 3, 17, 24, 256)    0         
_________________________________________________________________
conv4a (Conv3D)              (None, 3, 17, 24, 512)    3539456   
_________________________________________________________________
conv4b (Conv3D)              (None, 3, 17, 24, 512)    7078400   
_________________________________________________________________
pool4 (MaxPooling3D)         (None, 2, 7, 10, 512)     0         
_________________________________________________________________
flatten (Flatten)            (None, 71680)             0         
_________________________________________________________________
den0 (Dense)                 (None, 512)               36700672  
_________________________________________________________________
den1 (Dense)                 (None, 1)                 513       
=================================================================
Total params: 50,557,281
Trainable params: 36,701,185
Non-trainable params: 13,856,096
_________________________________________________________________
None
compiled
Train on 50 samples, validate on 6 samples
Epoch 1/50
50/50 [==============================] - 20s - loss: 0.5000 - acc: 0.5000 - val_loss: 0.5000 - val_acc: 0.5000
Epoch 2/50
50/50 [==============================] - 16s - loss: 0.5000 - acc: 0.5000 - val_loss: 0.5000 - val_acc: 0.5000
Epoch 3/50
50/50 [==============================] - 16s - loss: 0.5000 - acc: 0.5000 - val_loss: 0.5000 - val_acc: 0.5000
Epoch 4/50
45/50 [==========================>...] - ETA: 1s - loss: 0.5111 - acc: 0.4889
Epoch 00003: reducing learning rate to 0.00020000000949949026.
50/50 [==============================] - 16s - loss: 0.5000 - acc: 0.5000 - val_loss: 0.5000 - val_acc: 0.5000
Epoch 5/50
50/50 [==============================] - 16s - loss: 0.5000 - acc: 0.5000 - val_loss: 0.5000 - val_acc: 0.5000
Epoch 6/50
45/50 [==========================>...] - ETA: 1s - loss: 0.5111 - acc: 0.4889
Epoch 00005: reducing learning rate to 4.0000001899898055e-05.
50/50 [==============================] - 16s - loss: 0.5000 - acc: 0.5000 - val_loss: 0.5000 - val_acc: 0.5000
Epoch 7/50
50/50 [==============================] - 16s - loss: 0.5000 - acc: 0.5000 - val_loss: 0.5000 - val_acc: 0.5000
Epoch 8/50
45/50 [==========================>...] - ETA: 1s - loss: 0.4889 - acc: 0.5111
Epoch 00007: reducing learning rate to 8.000000525498762e-06.
50/50 [==============================] - 16s - loss: 0.5000 - acc: 0.5000 - val_loss: 0.5000 - val_acc: 0.5000
Epoch 9/50
50/50 [==============================] - 16s - loss: 0.5000 - acc: 0.5000 - val_loss: 0.5000 - val_acc: 0.5000
Epoch 00008: early stopping
56/56 [==============================] - 12s    
['loss', 'acc']: [0.50000001516725334, 0.5000000127724239]

Answer 1

您的图层设置为trainable=False(除了最后一个密集层)。因此你的 CNN 无法学习。此外，您将无法仅对单个样本进行训练。

如果您的 GPU 遇到性能问题，请切换到 CPU 或 AWS 或减小图像大小。