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train_cdbn_crbm2crbm_deconv.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import sys
import time
import numpy as np
import re
import datetime
import utils
import tensorflow as tf
import PIL
from PIL import Image
import global_define as gd
from utils import tile_raster_images
import scipy.io as sio
np.set_printoptions(threshold='nan')
TRAIN_FILE = 'train.tfrecords'
VALIDATION_FILE = 'validation.tfrecords'
TEST_FILE='test.tfrecords'
weightcost=0.0000002
flags = tf.app.flags
FLAGS = flags.FLAGS
#FLAGS = None
time_value=re.sub(r'[^0-7]','',str(datetime.datetime.now()))
flags.DEFINE_string('tfrecord_dir',
'/media/scw4750/25a01ed5-a903-4298-87f2-a5836dcb6888/Liuhongkun/zooscan_2/shuffled/224_224/', 'Directory to put the training data.')
flags.DEFINE_string('filename', 'train.tfrecords', 'Directory to put the training data.')
flags.DEFINE_integer('batch_size', 100, 'Batch size. '
'Must divide evenly into the dataset sizes.')
flags.DEFINE_integer('num_epochs', None, 'Batch size. '
'Must divide evenly into the dataset sizes.')
flags.DEFINE_integer('learning_rate', 0.1,'balabala')
flags.DEFINE_integer('max_steps', 50000,'balabala')
flags.DEFINE_string('model_dir','Modal/model'+str(time_value)+'/','balabala')
flags.DEFINE_string('tensorevents_dir','tensorboard_event/event_wth'+str(time_value)+'/','balabala')
flags.DEFINE_string('log_dir','Log_data/log'+str(time_value)+'/','balabala')
flags.DEFINE_string('pic_dir','Pic/Pictures_input'+str(time_value)+'/','balabala')
flags.DEFINE_string('Weight_dir','Weight/weight'+str(time_value)+'/','balabala')
if not os.path.exists(FLAGS.log_dir):
os.makedirs(FLAGS.log_dir)
if not os.path.exists(FLAGS.tensorevents_dir):
os.makedirs(FLAGS.tensorevents_dir)
if not os.path.exists(FLAGS.model_dir):
os.makedirs(FLAGS.model_dir)
if not os.path.exists(FLAGS.pic_dir):
os.makedirs(FLAGS.pic_dir)
if not os.path.exists(FLAGS.Weight_dir):
os.makedirs(FLAGS.Weight_dir)
def weight_variable(name,shape,initial_value):
# initial=tf.truncated_normal(shape,stddev=0.1)
# return tf.Variable(initial)
return tf.get_variable(name=name,shape=shape,initializer=tf.random_normal_initializer(stddev=initial_value))
def bias_variable(name,shape,initial_value):
# initial=tf.constant(0.1,shape=shape)
# return tf.Variable(initial)
return tf.get_variable(name=name,shape=shape,initializer=tf.constant_initializer(initial_value))
def conv2d(x,W,stride,padding):
return tf.nn.conv2d(x,W,strides=[1,stride,stride,1],padding=padding)
def max_pool(x,ksize,stride,padding):
return tf.nn.max_pool(x,ksize=[1,ksize,ksize,1],strides=[1,stride,stride,1],padding=padding)
def to_image(flat_tensor):
if not isinstance(flat_tensor,np.ndarray):
flat_array=tf.squeeze(flat_tensor).eval()
else:
flat_array=flat_tensor
#print(flat_array.shape)
if len(flat_array.shape)==1:
flat_length=flat_array.shape
square_length=int(np.sqrt(flat_length))
square_array=flat_array.reshape([square_length,square_length])
elif len(flat_array.shape)==2:
batch_size,flat_length=flat_array.shape
square_length=int(np.sqrt(flat_length))
square_array=flat_array.reshape([batch_size,square_length,square_length])
elif len(flat_array.shape)==3:
square_array=flat_array
else :
batch_size,square_width,square_height,depth=flat_array.shape
# print(flat_array.shape)
# print('size of the image input is %d,%d,%d,%d' %(batch_size,square_width,square_height,depth))
square_array=flat_array[0,:,:,:]
return square_array
def read_and_decode(filename_queue):
reader=tf.TFRecordReader()
_,serialized_exampe=reader.read(filename_queue)
features=tf.parse_single_example(serialized_exampe,
features={
'image_raw':tf.FixedLenFeature([],tf.string),
'label':tf.FixedLenFeature([],tf.int64)
})
image=tf.decode_raw(features['image_raw'],tf.uint8)
image.set_shape([gd.IMAGE_PIXELS])
image=tf.cast(image,tf.float32)*(1./255)-0.5
label=tf.cast(features['label'],tf.int32)
return image,label
def do_eval(sess,eval_correct,log_name):
true_count=0
for step in xrange(FLAGS.batch_size):
#print(sess.run(eval_correct))
true_count+=sess.run(eval_correct)
precision=float(true_count)/(FLAGS.batch_size*FLAGS.batch_size)
# true_count=sess.run(eval_correct)
# precision=float(true_count)/FLAGS.batch_size
print(' Num examples: %d Num correct: %d Precision @ 1: %0.04f' %
(FLAGS.batch_size*FLAGS.batch_size, true_count, precision))
logfile=open(log_name,'a')
logfile.write(' Num examples: %d Num correct: %d Precision : %0.04f' %
(FLAGS.batch_size, true_count, precision))
logfile.close()
return precision
def inputs(train,batch_size,num_epochs):
if not num_epochs:num_epochs=None
if train=='train':
filename=os.path.join(FLAGS.tfrecord_dir,TRAIN_FILE)
elif train=='validation':
filename=os.path.join(FLAGS.tfrecord_dir,VALIDATION_FILE)
else:
filename=os.path.join(FLAGS.tfrecord_dir,TEST_FILE)
with tf.name_scope('input'):
filename_queue=tf.train.string_input_producer([filename],num_epochs=None)
# example_list=[read_and_decode(filename_queue) for _ in range(10)]
# images,sparse_labels=tf.train.shuffle_batch_join(
# example_list,batch_size=batch_size,capacity=1000+3*batch_size,
# min_after_dequeue=1000)
#print(filename)
image,label=read_and_decode(filename_queue)
images, sparse_labels = tf.train.shuffle_batch(
[image, label], batch_size=batch_size,
capacity=1000 + 3 * batch_size,num_threads=1,
min_after_dequeue=1000)
#print("images shape:"+str(image))
# print(image.shape)
return images, sparse_labels
def train_crbm2crbm(log_name,conv_size,input_size,chanl_input,chanl_output,parameters):
images_input,labels_input=inputs(train='train',batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs)
#images=tf.reshape(images,[-1,input_size,input_size,chanl_input])
W_conv1=tf.placeholder("float",[conv_size,conv_size,chanl_input,chanl_output])
a_conv1=tf.placeholder("float",[chanl_input])
b_conv1=tf.placeholder("float",[chanl_output])
W_inc1=tf.placeholder("float",[conv_size,conv_size,chanl_input,chanl_output])
a_inc1=tf.placeholder("float",[chanl_input])
b_inc1=tf.placeholder("float",[chanl_output])
W_extra1=tf.placeholder("float",[11,11,1,96])
#a_extra1=tf.placeholder("float",[1])
b_extra1=tf.placeholder("float",[96])
images_placeholder=tf.placeholder(tf.float32,shape=(gd.BATCH_SIZE,227*227))
images_extra=tf.reshape(images_placeholder,[-1,227,227,1])
h_conv1=1./(1+tf.exp(-conv2d(images_extra,W_extra1,4,'VALID')-b_extra1))
norm1=tf.nn.lrn(h_conv1,5,bias=1.0,alpha=0.0001,beta=0.75,name='norm1')
h_pool1=max_pool(norm1,3,2,'VALID')
images=h_pool1
print(images)
pos_conv1_prob=1./(1+tf.exp(-conv2d(h_pool1,W_conv1,1,'VALID')-b_conv1))
pos_conv1_trans=tf.expand_dims(tf.reduce_mean(pos_conv1_prob,0),2)
images_mean=tf.reduce_mean(images,0)
images_trans=tf.expand_dims(tf.reshape(tf.transpose(tf.reshape(
tf.reduce_mean(images,0),[-1,chanl_input])),[chanl_input,input_size,input_size]),3)
pos_prods_origin=conv2d(images_trans,pos_conv1_trans,1,'VALID')
pos_prods_trans=tf.transpose(pos_prods_origin,[1,2,0,3])
print('pos_prods_trans:'+str(pos_prods_trans))
pos_hid_act=tf.reduce_mean(pos_conv1_prob,0)
pos_vis_act=tf.reduce_mean(images,0)
#########################################################################3
pos_hid_states=tf.to_float(tf.less_equal(tf.random_uniform(
shape=tf.shape(pos_conv1_prob)),pos_conv1_prob))
#if pad_choose=="VALID":
pos_hid_states_addpad=tf.pad(pos_hid_states,[[0,0],[conv_size-1,conv_size-1],[conv_size-1,conv_size-1],[0,0]],"CONSTANT")
#else:
W_transpose=tf.matrix_transpose(tf.reverse(W_conv1,[True,True,False,False]))
print('pos_conv1_prob:'+str(pos_conv1_prob))
neg_data=1./(1+tf.exp(-tf.nn.conv2d_transpose(pos_conv1_prob,W_conv1,[gd.BATCH_SIZE,input_size,input_size,chanl_input],strides=[1,1,1,1],padding='VALID')-a_conv1))
#neg_data=1./(1+tf.exp(-conv2d_s1_valid(pos_hid_states_addpad,W_transpose)-a_conv1))
#neg_data=
print('neg_data'+str(neg_data))
neg_hid_probs=1./(1+tf.exp(-conv2d(neg_data,W_conv1,1,'VALID')-b_conv1))
neg_data_trans=tf.expand_dims(tf.reshape(tf.transpose(tf.reshape(
tf.reduce_mean(neg_data,0),[-1,chanl_input])),[chanl_input,input_size,input_size]),3)
neg_hid_probs_trans=tf.expand_dims(tf.reduce_mean(neg_hid_probs,0),2)
neg_prods_origin=conv2d(neg_data_trans,neg_hid_probs_trans,1,'VALID')
neg_prods_trans=tf.transpose(neg_prods_origin,[1,2,0,3])
print('neg_prods_trans'+str(neg_prods_trans))
neg_hid_act=tf.reduce_mean(neg_hid_probs,0)
neg_vis_act=tf.reduce_mean(neg_data,0)
err_sum=tf.reduce_sum(tf.square(images-neg_data))
#reshaped_W=tf.transpose(tf.reshape(tf.transpose(tf.reshape(tf.squeeze(W_conv1),[-1,chanl_output])),[chanl_output*chanl_input,conv_size*conv_size]))
W_inc_update=gd.momentum*W_inc1 + gd.epsilonw*((pos_prods_trans-neg_prods_trans)/gd.BATCH_SIZE - weightcost*W_conv1)
a_inc_update=gd.momentum*a_inc1 + (gd.epsilona/gd.BATCH_SIZE) * tf.reduce_mean(pos_vis_act-neg_vis_act)
b_inc_update=gd.momentum*b_inc1 + (gd.epsilonb/gd.BATCH_SIZE) * tf.reduce_mean(tf.reduce_mean((pos_hid_act - neg_hid_act),0),0)
init_op=tf.initialize_all_variables()
tf.scalar_summary('loss',err_sum)
tf.scalar_summary('a',a_conv1[0])
tf.scalar_summary('b',b_conv1[0])
tf.scalar_summary('W',W_conv1[0][0][0][0])
summary_op=tf.merge_all_summaries()
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:
sess.run(init_op)
# summary_writer=tf.train.SummaryWriter(FLAGS.train_dir,sess.graph)
# coord=tf.train.Coordinator()
# threads=tf.train.start_queue_runners(sess=sess,coord=coord)
summary_writer=tf.train.SummaryWriter(FLAGS.tensorevents_dir,sess.graph)
coord=tf.train.Coordinator()
threads=tf.train.start_queue_runners(sess=sess,coord=coord)
W_update_0=np.random.normal(0,0.1,[conv_size,conv_size,chanl_input,chanl_output])
a_update_0=np.zeros([chanl_input],np.float32)
b_update_0=np.zeros([chanl_output],np.float32)
W_inc_update_0=np.zeros([conv_size,conv_size,chanl_input,chanl_output],np.float32)
a_inc_update_0=np.zeros([chanl_input],np.float32)
b_inc_update_0=np.zeros([chanl_output],np.float32)
W_extra1_0=parameters[0]
a_extra1_0=parameters[1]
b_extra1_0=parameters[2].reshape(chanl_input)
try:
step=0
while step<10000:
start_time=time.time()
#print(images_input.eval(session=sess).shape)
#print(a_update_0)
# logfile=open(log_name,'a')
# logfile.write("epoch: "+str(step)+'\n')
# logfile.write("W:\n"+str(W_update_0[0])+'\n')
images_wtf=images_input.eval(session=sess)
# concat_img=Image.fromarray(
# tile_raster_images(
# X=images_wtf,
# img_shape=(32, 32),
# tile_shape=(10, 10)
# ))
# concat_img.save(FLAGS.pic_dir+str(step)+'_train'+'.jpg')
# logfile.close()
W_inc_update_0,a_update_0,b_inc_update_0,loss,neg_data_out,images_out=sess.run(
[W_inc_update,a_inc_update,b_inc_update,err_sum,neg_data,images],
feed_dict={images_placeholder:images_wtf,
W_conv1:W_update_0,a_conv1:a_update_0,b_conv1:b_update_0,W_inc1:W_inc_update_0,
a_inc1:a_inc_update_0,b_inc1:b_inc_update_0,
W_extra1:W_extra1_0,b_extra1:b_extra1_0})
W_update_0=W_update_0+W_inc_update_0
a_update_0=a_update_0+a_inc_update_0
b_update_0=b_update_0+b_inc_update_0
# logfile=open(log_name,'a')
# logfile.write("epoch: "+str(step)+'\n')
# logfile.write("W_inc:\n"+str(W_inc_update_0[0])+'\n')
# logfile.close()
#print('step '+str(step)+": loss="+str(loss)+'\n')
print("step %d: loss = %d" %(step,loss))
if step % 10== 0:
logfile=open(log_name,'a')
logfile.write('step '+str(step)+": loss="+str(loss)+'\n')
logfile.write("W:\n"+str(W_update_0[0])+'\n')
#logfile.write("W_inc:\n"+str(W_inc_update_0[0])+'\n')
logfile.close()
# print(to_image(neg_data_out).shape)
# print("images_out"+str(to_image(images_out).shape))
# weight_img=Image.fromarray(
# tile_raster_images(
# X=reshaped_W_out.T,
# img_shape=(conv_size, conv_size),
# tile_shape=(chanl_input, chanl_output),
# ))
# weight_img.save(FLAGS.Weight_dir+'weight_'+str(step)+'.jpg')
summary_str = sess.run(summary_op,
feed_dict={images_placeholder:images_wtf,
W_conv1:W_update_0,a_conv1:a_update_0,b_conv1:b_update_0,W_inc1:W_inc_update_0,
a_inc1:a_inc_update_0,b_inc1:b_inc_update_0,
W_extra1:W_extra1_0,b_extra1:b_extra1_0})
summary_writer.add_summary(summary_str, step)
if step % 50==0:
save_fn=FLAGS.log_dir+'/parameters_layer2_epoch_'+str(step)+'.mat'
sio.savemat(save_fn,{'W1':W_extra1_0,'b1':b_extra1_0,
'W2':W_update_0,'b2':b_update_0})
saveimg=Image.fromarray(255*to_image(images_out)[:,:,0])
#print(to_image(images_input.eval(session=sess).reshape(gd.BATCH_SIZE,input_size,input_size,in)).shape)
saveimg=saveimg.convert('RGB')
saveimg.save(FLAGS.pic_dir+'imag_layer1_epoch'+str(step)+'.jpg')
saveimg_negv=Image.fromarray(255*to_image(neg_data_out)[:,:,0])
#print(to_image(neg_data_out).shape)
saveimg_negv=saveimg_negv.convert('RGB')
saveimg_negv.save(FLAGS.pic_dir+'negv_layer1_epoch'+str(step)+'.jpg')
step+=1
# if step==100:
# return W_update_0,a_update_0,b_update_0
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (1001, step))
finally:
coord.request_stop()
coord.join(threads)
sess.close()
return W_update_0,a_update_0,b_update_0
if __name__=="__main__":
log_name=str(FLAGS.log_dir)+'zooscan_224_224_20_'+'layer1'+'_'+re.sub(r'[^0-7]','',str(datetime.datetime.now()))+'.txt'
f=open(log_name,'w')
f.close()
# print('here 1')
# train_input2crbm(log_name,conv_size,input_size,chanl_input,chanl_output)
W1,a1,b1=train_input2crbm(log_name,11,227,1,96)
print("returned b1 shape:"+str(b1.shape))
save_fn=FLAGS.log_dir+'/parameters_layer1.mat'
sio.savemat(save_fn,{'W1':W1,'a1':a1,'b1':b1})
# #print(b1)
# log_name=str(FLAGS.log_dir)+'zooscan_227_227_20modified_'+'layer2'+'.txt'
# load_fn='/home/scw4750/Liuhongkun/tfrecord/zooscan/Alex_cdbn/data_record/experiment1/layer1/log2017021173725417716/parameters_layer1_epoch_200.mat'
# load_data=sio.loadmat(load_fn)
# W1=load_data['W1']
# a1=load_data['a1']
# b1=load_data['b1']
# parameters_layer1=[W1,a1,b1]
# #print("reloaded b1 shape:"+str(b1.shape))
# #train_crbm2crbm(log_name,conv_size,input_size,chanl_input,chanl_output,parameters)
# conv_size=5
# input_size=27
# chanl_input=96
# chanl_output=256
# W2,a2,b2=train_crbm2crbm(log_name,conv_size,input_size,chanl_input,chanl_output,parameters_layer1)
# # # # print(a2)
# # save_fn='/home/scw4750/Liuhongkun/tfrecord/kaggle_zooplankton/cdbn2/parameter_saved/vs_lenet/experiment1/parameters_layer2.mat'
# # sio.savemat(save_fn,{'W1':W1,'a1':a1,'b1':b1,
# # 'W2':W2,'a2':a2,'W3':b2})