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binary_connect.py
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import tensorflow as tf
import numpy as np
import re
import basic_layers
import utils
# [dataset]
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train=np.reshape(x_train,newshape=[-1,28,28,1])
x_test=np.reshape(x_test,newshape=[-1,28,28,1])
x_train,x_test=x_train,x_test
classnum=10
identity=np.eye(classnum)
y_hinge_train,y_hinge_test=2*identity[y_train]-1,2*identity[y_test]-1
#x_train, x_test = x_train / 255.0, x_test / 255.0
## typical SGD manual implementation
with tf.variable_scope("mlp0") as scope:
x=tf.placeholder(dtype=tf.float32,shape=[None,28,28,1],name="x")
y=tf.placeholder(dtype=tf.float32,shape=[None,classnum],name="y")
#conv1=conv2d_layer_bi(x,bi_function=binarize_zero,kernel_size=3,filters=64,strides=[2,2],activation=None,name="conv1")
#bn1=tf.layers.batch_normalization(conv1,axis=-1,center=False,scale=False,name="bn1")
#conv2=conv2d_layer_bi(bn1,bi_function=binarize_zero,kernel_size=3,filters=64,strides=[2,2],activation=None,name="conv2")
#bn2=tf.layers.batch_normalization(conv2,axis=-1,center=False,scale=False,name="bn2")
#conv3=conv2d_layer_bi(bn2,bi_function=binarize_zero,kernel_size=3,filters=64,strides=[2,2],activation=None,name="conv3")
#bn3=tf.layers.batch_normalization(conv3,axis=-1,center=False,scale=False,name="bn3")
h1=basic_layers.fc_layer_bi(x,bi_function=basic_layers.binarize_zero,units=2048,activation=tf.nn.leaky_relu,name="h1")
h2=basic_layers.fc_layer_bi(h1,bi_function=basic_layers.binarize_zero,units=2048,activation=tf.nn.leaky_relu,name="h2")
h3=basic_layers.fc_layer_bi(h2,bi_function=basic_layers.binarize_zero,units=2048,activation=tf.nn.leaky_relu,name="h3")
fc2=basic_layers.fc_layer_bi(h3,bi_function=basic_layers.binarize_zero,units=10,name="fc2")
#loss=tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y,logits=fc2))
loss=tf.losses.hinge_loss(labels=y,logits=fc2)
varlist=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,scope="mlp0")
#p1=re.compile("conv\d+/kernels")
p1=re.compile("h\d+/W")
p2=re.compile("fc\d+/W")
# varlist_bi is a varlist with some variables substituted by their binarized version
varlist_bi=[]
for i,v in enumerate(varlist):
if p1.findall(v.name) or p2.findall(v.name):
varlist_bi.append(tf.get_default_graph().get_tensor_by_name(v.name[:v.name.find(':')]+'_bi'+v.name[v.name.find(':'):]))
else:
varlist_bi.append(v)
grads=tuple(tf.gradients(loss,varlist_bi,name="gradients_bi"))
lr_buffer=1e-4
decay_factor=0.5
decay_iter=10
lr=tf.placeholder_with_default(lr_buffer,shape=None)
print("varlist_bi:")
for var_bi in varlist_bi:
print(var_bi.name)
print("varlist")
updatesteps=[]
for i,var in enumerate(varlist):
print(var.name)
if "bi" in var.name:
updatesteps.append(tf.assign(var,tf.clip_by_value(var-lr*grads[i],-1,1)))
#updatesteps.append(tf.assign(var,var-lr*grads[i]))
else:
updatesteps.append(tf.assign(var,var-lr*grads[i]))
# end of model op definition
print('ok')
saver=tf.train.Saver()
sess=tf.Session()
init=tf.global_variables_initializer()
max_epoch=5000
batch_size=200
save_iter=10
test_iter=5
# training
sess.run(init)
test_acc_save=[]
restore_epoch=0
if restore_epoch>0:
print("restoring from epoch "+str(restore_epoch)+"...")
saver.restore(sess,"epoch_"+str(restore_epoch)+".ckpt")
for e in range(restore_epoch,max_epoch):
utils.shuffle_together(x_train,y_hinge_train)
epoch_loss=0
for b in range(np.shape(x_train)[0]//batch_size+1):
if b!=np.shape(x_train)[0]//batch_size:
batch_x=x_train[b*batch_size:(b+1)*batch_size]
batch_y=y_hinge_train[b*batch_size:(b+1)*batch_size]
else:
batch_x=x_train[b*batch_size:-1]
batch_y=y_hinge_train[b*batch_size:-1]
# calculate grads(model not changed)
grad_values=sess.run(grads,feed_dict={x:batch_x,y:batch_y})
# weights updating
sess.run(updatesteps,feed_dict={grads:grad_values,lr:lr_buffer})
# calculate loss value
batch_loss=sess.run(loss,feed_dict={x:batch_x,y:batch_y})
epoch_loss+=batch_loss
epoch_loss=epoch_loss/np.shape(x_train)[0]
print("epoch "+str(e+1)+": "+str(epoch_loss))
if not (e+1)%decay_iter:
lr_buffer=lr_buffer*decay_factor
print("updated lr: "+str(lr_buffer))
if not (e+1)%save_iter:
print("saving model...")
saver.save(sess,"./epoch_"+str(e+1)+".ckpt")
if not (e+1)%test_iter:
print("testing model...")
pred=sess.run(fc2,feed_dict={x:x_test,y:y_hinge_test})
accuracy=utils.acc(pred=pred,label=y_hinge_test)
test_acc_save.append(accuracy)
print("val accuracy: "+str(accuracy))
np.save("./val_acc.npy",np.array(test_acc_save))