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LABCifar.py
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LABCifar.py
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#Make LAB for recurrent-LSTM, CIFAR-10, MNIST
import random
import cv2
import numpy as np
import tensorflow as tf
from keras import backend as K
class LAB(object):
def __init__(self,sess,no_layers = 6,input_size = (32,32,3), output_size = 10,learning_rate = 0.01,batch_size = 10,train_size =100,no_epochs = 10,beta1 = 0.9,beta2 = 0.9,epsilon = 1.0):
self.no_layers = no_layers
self.input_shape = input_size
self.no_classes = output_size
self.learning_rate = learning_rate
self.filters = []
val = 128
self.batch_size = batch_size
self.no_epochs = no_epochs
self.train_size = train_size
self.filter_size = [(3,3),(3,3),(3,3),(3,3),(3,3),(3,3)]
self.beta1 = beta1
self.beta2 = beta2
self.epsilon = epsilon
self.sess = sess
for i in range(self.no_layers):
self.filters.append(val)
self.filters.append(val)
val = int(val*2)
self.D = {}
self.m1 = {}
self.m2 = {}
self.initialize_matrices()
self.build_model()
def initialize_matrices(self):
for i in range(self.no_layers):
if(i==0):
size = self.filter_size[i] + (self.input_shape[2],self.filters[i])
else:
size = self.filter_size[i] + (self.filters[i-1],self.filters[i])
diag_matrix = np.random.normal(0,0.1,size)
moments = np.random.random_sample(size)
name = 'conv_layer_' + str(i+1)
self.D[name] = diag_matrix
self.m1[name] = moments
self.m2[name] = moments
def _get_variable(self,name,shape,initializer,weight_decay=0.0,dtype='float',trainable=True):
if(weight_decay>0):
regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
else:
regularizer = None
return tf.get_variable(name,shape = shape,initializer = initializer,dtype = dtype,regularizer = regularizer,trainable= trainable)
def discretize_function(self,Wi):
if(Wi>= 0 ):
return 1
else:
return -1
def conv2D(self,padding,input_,filter_shape,stride,name = "conv2d"):
with tf.variable_scope(name):
w = self._get_variable('w',shape = filter_shape, initializer = tf.contrib.layers.xavier_initializer(),weight_decay = 0.0,dtype='float',trainable=False)
w_binary = self._get_variable('binary_w',shape = filter_shape,initializer = tf.contrib.layers.xavier_initializer(),weight_decay = 0.0001,dtype='float',trainable = True)
alpha = self._get_variable('scaling_factor',shape = filter_shape[3],initializer = tf.contrib.layers.xavier_initializer(),weight_decay =0.0, dtype = 'float',trainable = False)
binarized_weights = alpha * w_binary
conv = tf.nn.conv2d(input_,binarized_weights,stride,padding=padding)
return conv
def batch_norm_relu(self,input_):
p = tf.nn.moments(input_,axes = [0],keep_dims = False)
norm = tf.nn.batch_normalization(input_,p[0],p[1],offset=None,scale=None,variance_epsilon =1e-4)
return tf.nn.relu(norm)
def convolution(self,input_,filters,kernel_size,name,strides=(1,1),padding = "SAME"):
kernel_size = kernel_size + (input_.shape[3],filters)
strides = [1,strides[0],strides[1],1]
return self.conv2D(padding,input_, filter_shape=kernel_size,stride=strides,name=name)
def pooling(self,input_,pool_size,strides,type="MAX"):
pool_size = [1,pool_size[0],pool_size[1],1]
strides = [1,strides[0],strides[0],1]
if(type == "MAX"):
return tf.nn.max_pool(input_,pool_size,strides,padding = "SAME")
elif(type == "AVERAGE"):
return tf.nn.avg_pool(input_,pool_size,strides,padding = "SAME")
def build_model(self):
self.input_ = tf.placeholder("float",[None,self.input_shape[0],self.input_shape[1],self.input_shape[2]])
for i in range(self.no_layers):
if(i==0):
self.conv = self.batch_norm_relu(self.convolution(self.input_,filters = self.filters[i],kernel_size = self.filter_size[i],name = "conv_layer_"+str(i+1),strides = (1,1),padding="SAME"))
elif(i%2!=0):
self.conv = self.batch_norm_relu(self.convolution(self.conv,filters = self.filters[i],kernel_size = self.filter_size[i],name = "conv_layer_"+str(i+1),strides = (1,1),padding = "SAME"))
self.pool = self.pooling(self.conv,pool_size = (2,2),strides = (2,2),type = "MAX")
else:
self.conv = self.batch_norm_relu(self.convolution(self.pool,filters = self.filters[i],kernel_size = self.filter_size[i],name = "conv_layer_"+str(i+1),strides = (1,1),padding="SAME"))
self.flatten = tf.contrib.layers.flatten(self.pool)
flatten_shape = K.int_shape(self.flatten)
self.weights_FCLayer1 = self._get_variable("weights_final1",[flatten_shape[1],1024],tf.contrib.layers.xavier_initializer())
self.weights_FCLayer2 = self._get_variable("weights_final2",[1024,1024],tf.contrib.layers.xavier_initializer())
self.weights_FCLayer = self._get_variable("weights_final",[1024,self.no_classes],tf.contrib.layers.xavier_initializer())
self.output1 = tf.matmul(self.flatten,self.weights_FCLayer1)
self.output2 = tf.matmul(self.output1,self.weights_FCLayer2)
self.output = tf.nn.softmax(tf.matmul(self.output2,self.weights_FCLayer))
#print(K.int_shape(self.output))
self.actual_output = tf.placeholder("float",[None,self.no_classes])
def generate_data(self):
return (np.random.randint(0,256,size = (self.batch_size,self.input_shape[0],self.input_shape[1],self.input_shape[2])), np.random.randint(2,size = (self.batch_size,self.no_classes)))
def L1_norm(self,x):
return np.sum(np.absolute(x))
def element_wise_mult(self,x,y):
return np.multiply(x,y)
def update_learning_rate(self,x,i):
if(i%15==0 and i!=0):
return ((x*1.0)/0.5)
return x
def train(self):
self.t_vars = tf.global_variables()
self.original_weights = [var for var in self.t_vars if ('conv_layer' in var.name and '/w' in var.name)]
self.binary_weights = [ var for var in self.t_vars if ('conv_layer' in var.name and 'binary' in var.name)]
self.scaling_factors = [var for var in self.t_vars if 'scaling_factor' in var.name]
self.training_vars = tf.trainable_variables()
self.loss_value = tf.reduce_mean(tf.abs(self.actual_output - self.output))
self.optimizer = tf.train.AdamOptimizer(self.learning_rate,self.beta1,self.beta2,self.epsilon)
tf.initialize_all_variables().run()
for i in range(self.no_epochs):
iterations = int(self.train_size/self.batch_size)
self.initialize_matrices()
print("Epoch",i+1)
for k in range(iterations):
print("Batch:",k+1)
print("Determining binarized_weights and optimal scaling factor")
for j in range(1,(len(self.original_weights)+1)):
string = 'conv_layer_' + str(j)
v1 = [v for v in self.original_weights if string in v.name][0]
v2 = [v for v in self.binary_weights if string in v.name][0]
scales = [v for v in self.scaling_factors if string in v.name][0]
v1 = v1.eval(session = sess)
sh = v1.shape
v3 = np.zeros((sh))
v4 = np.zeros((sh[3]))
for l in range(sh[3]):
for x in range(sh[0]):
for y in range(sh[1]):
for z in range(sh[2]):
v3[x][y][z][l] = self.discretize_function(v1[x][y][z][l])
v4[l] = self.L1_norm(self.element_wise_mult(self.D[string][:,:,:,l],v1[:,:,:,l]))
v4[l] = (v4[l]*1.0)/(self.L1_norm(self.D[string][:,:,:,l]))
v2.assign(v3).eval()
scales.assign(v4).eval()
print("Training................")
(images,output) = self.generate_data()
vals = self.sess.run(self.optimizer.compute_gradients(self.loss_value, var_list= self.training_vars),feed_dict={self.actual_output:output,self.input_:images}) # Here vals is a list of (gradient,variable) pairs
indices = []
for j in range(len(vals)):
if(len(vals[j][1].shape)>=4):
indices.append(j)
for j in range(len(self.original_weights)):
gradient = vals[indices[j]][0]
weight = self.original_weights[j].eval()
#print (gradient.shape,weight.shape)
string = 'conv_layer_' + str(j+1)
self.m1[string] = self.beta1 * self.m1[string] + (1.0 - self.beta1)*gradient
self.m2[string] = self.beta2 * self.m2[string] + (1.0 - self.beta2)*(self.element_wise_mult(gradient,gradient))
m1_unbiased = ((self.m1[string]* 1.0)/(1.0 - self.beta1))
m2_unbiased = ((self.m2[string]* 1.0)/(1.0 - self.beta2))
#print(m2_unbiased)
#abc = input()
self.D[string] = (1.0/self.learning_rate) * (self.epsilon + np.sqrt(m2_unbiased))
#self.D[string] = np.nan_to_num(self.D[string])
#print(self.D[string])
#abc = input()
weight = weight - (np.divide(m1_unbiased,self.D[string]))
self.original_weights[j].assign(weight).eval()
weight = self.weights_FCLayer1.eval()
gradient = [x[0] for x in vals if (len(x[0].shape) == 2)][0]
weight = weight - (self.learning_rate*gradient)
self.weights_FCLayer1.assign(weight).eval()
weight = self.weights_FCLayer2.eval()
gradient = [x[0] for x in vals if (len(x[0].shape) == 2)][1]
weight = weight - (self.learning_rate*gradient)
self.weights_FCLayer2.assign(weight).eval()
weight = self.weights_FCLayer.eval()
gradient = [x[0] for x in vals if (len(x[0].shape) == 2)][2]
weight = weight - (self.learning_rate*gradient)
self.weights_FCLayer.assign(weight).eval()
self.learning_rate = self.update_learning_rate(self.learning_rate,i)
sess = tf.Session()
with sess as sess:
network = LAB(sess)
network.train()