CNN Lernen Stagnation

Question

Ich habe eine Simulation des CNN erstellt Ich versuche, auf Videodatensatz zu verwenden. Ich setze die Testdaten auf ein einziges Bild in allen Frames für positive Beispiele und 0 für negative Beispiele. Ich dachte, das würde sehr schnell lernen. Aber es bewegt sich überhaupt nicht. Mit aktuellen Versionen von Keras & Tensorflow unter Windows 10 64bit.

Erste Frage, ist meine Logik falsch? Sollte ich erwarten, dass das Erlernen dieser Testdaten schnell eine hohe Genauigkeit erreicht?

Ist etwas falsch mit meinem Modell oder Parameter? Ich habe eine Reihe von Änderungen versucht, aber immer noch das gleiche Problem.

Ist die Stichprobengröße (56) zu klein?

# 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)) 

Wie immer, vielen Dank für jede Hilfe.

addierte Ausgabe ...

_________________________________________________________________ 
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

Ihre Schichten werden auf trainable=False (abgesehen von der letzten dichten Schicht). Daher kann Ihr CNN nicht lernen. Außerdem können Sie nicht nur an einer einzigen Probe trainieren.

Wenn Sie Leistungsprobleme auf Ihrem GPU auftreten, wechseln Sie zu CPU oder AWS oder reduzieren Sie Ihre Bildgröße.