Question

我正在使用Keras 2.07，在Windows 10上使用Python 3.5，Tensorflow 1.3.0

我正在测试用于行动识别的纸张长期时间卷积中使用的架构。我希望将它用于我自己的数据。我使用了我认为会很快收敛的测试数据。学习率降至1.e-6但损失从未超过4.5549672的高值...

有人可以查看或试用此代码吗？我是错误的，编码错误还是不耐烦？

由于

## Attempt to implement based on ...
#Long-term Temporal Convolutionsfor Action Recognition
#
#Gul Varol, Ivan Laptev, and Cordelia Schmid, ¨ Fellow, IEEE
#
import time
import numpy as np,cv2
import sys
import os

import keras
import tensorflow as tf

from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten,  Input
from keras.layers import Conv3D, MaxPooling3D
from keras.layers import ZeroPadding3D, AveragePooling3D, MaxPooling3D,LeakyReLU
from keras.optimizers import SGD,rmsprop, adam, Adagrad, Nadam, Adadelta

from keras import regularizers


from keras import backend as K


K.clear_session()
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()


#path information
sample_root_path = "C:\\"

cur=os.chdir(sample_root_path )




batch_size = 15
num_classes = 1
epochs = 5000
my_lambda = 0.

nchan = 3

m_loss = 'binary_crossentropy'

m_opt = Nadam(lr=0.02, beta_1=0.9, beta_2=0.999, epsilon=1e-08, schedule_decay=0.004)
#
m_met = ['binary_accuracy'] 

#test data
nvideos = 300
nframes = 30
nrows = 60
ncols = 80
nchan = 3

x_flow = np.ones((nvideos, nframes,nrows,ncols,nchan),np.float32)
x_flow[150:] =-1.0
y_flow = np.ones((nvideos),np.float32)
y_flow[150:]= 0




t_inp =Input(shape=(nframes,nrows,ncols,nchan),name='t_inp')

t =  Conv3D(64, (3, 3,3), activation='relu',padding="same", name="conv1", strides=(1, 1, 1),
                 kernel_initializer = 'glorot_normal',
                 bias_initializer = 'glorot_normal', 
                 bias_regularizer = regularizers.l1(my_lambda), 
                 trainable=True) (t_inp)
t = LeakyReLU(alpha=0.3,trainable=True) (t)
t = MaxPooling3D(pool_size=(2,2,1), strides=(2, 2, 2),name='pool1') (t)


t =  Conv3D(128, (3, 3,3), activation='relu',padding="same", name="conv2", strides=(1, 1, 1), 
                  bias_regularizer = regularizers.l1(my_lambda),trainable=True) (t)
t = LeakyReLU(alpha=0.3,trainable=True) (t)

t = MaxPooling3D(pool_size=(2,2,2), strides=(2, 2, 2),name='pool2') (t)




t =  Conv3D(256, (3, 3,3), activation='relu',padding="same", name="conv3", strides=(1, 1, 1), 
                 bias_regularizer = regularizers.l1(my_lambda),trainable=True) (t)
t = LeakyReLU(alpha=0.3,trainable=True) (t)

t = MaxPooling3D(pool_size=(2,2,2), strides=(2, 2, 2),name='pool3') (t)



t =  Conv3D(256, (3, 3,3), activation='relu',padding="same", name="conv4", strides=(1, 1, 1), 
                 bias_regularizer = regularizers.l1(my_lambda), trainable=True) (t)
t = LeakyReLU(alpha=0.3,trainable=True) (t)

t = MaxPooling3D(pool_size=(2,2,2), strides=(2, 2, 2),name='pool4') (t)



t =  Conv3D(256, (3, 3,3), activation='relu',padding="same", name="conv5", strides=(1, 1, 1), 
                 bias_regularizer = regularizers.l1(my_lambda), trainable=True) (t)
t = LeakyReLU(alpha=0.3,trainable=True) (t)
#
t = MaxPooling3D(pool_size=(1,1,1), strides=(1, 1, 1),name='pool5') (t)
##*****************************

t = Flatten () (t)
t = Dense((2048),name='s_den00') (t)
t = Dropout(.5) (t)
t = Dense((2048),name='s_den0') (t)
t = Dropout(.5) (t)


t = Dense((num_classes),activation='softmax',name='s_den1') (t)


model = Model(inputs=t_inp,outputs=t)
print (model.summary())

model.compile(loss=m_loss, optimizer=m_opt, metrics=m_met)

print ('compiled model')
tb = keras.callbacks.TensorBoard(log_dir=sample_root_path+'logs', histogram_freq=0,
                          write_graph=True, write_grads=True, write_images=True)

reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.5,verbose=1, patience=3, min_lr=0.000001)

with tf.device('/gpu:0'):
  history = model.fit(x_flow, y_flow,
                      batch_size=batch_size,
                      callbacks=[reduce_lr,
                                 tb],
                      verbose=1,
                      validation_split=.3,
                      shuffle = True,
                      epochs=epochs)

print ('done')

Answer 1

问题是输出图层激活功能。如果输出的数组看起来像这样

，则只能使用softmax作为两类结构

[[1,0],
 [1,0],
 [0,1]]

softmax假设输出将总和为1，因此如果您预测输入值，您会注意到它们都是1，因此损失不会减少。您可以像上面那样设置输出，也可以保持相同，就像这样的单个二进制列

[[1],
 [1],
 [0]]

并使用sigmoid激活功能，该功能可优化0-1范围内的输出。所以你的最后一层将是

t = Dense((num_classes),activation='sigmoid',name='s_den1') (t)

Keras NN损失不减少

1 个答案: