#CorrMCNN: Gaurav Bhatt and Piyush Jha
# had to degrade numpy to 1.11.0 as 1.13.0 doesn't support float index type in arrays
import sys
import math
import random
import warnings
import numpy as np
from sklearn import svm
import keras.backend as K
from keras.models import Model
from theano import tensor as T
import matplotlib.pyplot as plt
from keras.layers import Input, Merge
from keras.engine.topology import Layer
from sklearn.metrics import accuracy_score
from keras.layers.core import Activation, Dense, Reshape
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Flatten
warnings.simplefilter("ignore")
hdim = 50 #hidden dimension
h_loss = 50
hdim_deep = 500
hdim_deep2 = 300
nb_epoch = 100 #epochs
batch_size = 100
dimx = 392 #image dimension
dimy = 392
lamda = 0.02
loss_type = 2 #dummy
def svm_classifier(train_x, train_y, valid_x, valid_y, test_x, test_y):
clf = svm.LinearSVC()
#print train_x.shape,train_y.shape
clf.fit(train_x,train_y)
pred = clf.predict(valid_x)
va = accuracy_score(np.ravel(valid_y),np.ravel(pred))
pred = clf.predict(test_x)
ta = accuracy_score(np.ravel(test_y),np.ravel(pred))
return va, ta
def split(train_l,train_r,label,ratio):
total = train_l.shape[0]
train_samples = int(total*(1-ratio))
test_samples = total-train_samples
tr_l,tst_l,tr_r,tst_r,l_tr,l_tst=[],[],[],[],[],[]
dat=random.sample(range(total),train_samples)
for a in dat:
tr_l.append(train_l[a,:])
tr_r.append(train_r[a,:])
l_tr.append(label[a])
for i in range(test_samples):
if i not in dat:
tst_l.append(train_l[i,:])
tst_r.append(train_r[i,:])
l_tst.append(label[i])
tr_l = np.array(tr_l)
tr_r = np.array(tr_r)
tst_l = np.array(tst_l)
tst_r = np.array(tst_r)
l_tr = np.array(l_tr)
l_tst = np.array(l_tst)
return tr_l,tst_l,tr_r,tst_r,l_tr,l_tst
class ZeroPadding(Layer):
def __init__(self, **kwargs):
super(ZeroPadding, self).__init__(**kwargs)
def call(self, x, mask=None):
return K.zeros_like(x)
def get_output_shape_for(self, input_shape):
return input_shape
class MultiplyBy2(Layer):
def __init__(self, **kwargs):
super(MultiplyBy2, self).__init__(**kwargs)
def call(self, x, mask=None):
return 2*x
def get_output_shape_for(self, input_shape):
return input_shape
class CorrnetCost(Layer):
def __init__(self,lamda, **kwargs):
super(CorrnetCost, self).__init__(**kwargs)
self.lamda = lamda
def cor(self,y1, y2, lamda):
y1_mean = K.mean(y1, axis=0)
y1_centered = y1 - y1_mean
y2_mean = K.mean(y2, axis=0)
y2_centered = y2 - y2_mean
corr_nr = K.sum(y1_centered * y2_centered, axis=0)
corr_dr1 = K.sqrt(K.sum(y1_centered * y1_centered, axis=0) + 1e-8)
corr_dr2 = K.sqrt(K.sum(y2_centered * y2_centered, axis=0) + 1e-8)
corr_dr = corr_dr1 * corr_dr2
corr = corr_nr / corr_dr
return K.sum(corr) * lamda
def call(self ,x ,mask=None):
h1=x[0]
h2=x[1]
corr = self.cor(h1,h2,self.lamda)
#self.add_loss(corr,x)
#we output junk but be sure to use it for the loss to be added
return corr
def get_output_shape_for(self, input_shape):
#print input_shape[0][0]
return (input_shape[0][0],input_shape[0][1])
def corr_loss(y_true, y_pred):
#print y_true.type,y_pred.type
#return K.zeros_like(y_pred)
return y_pred
def project(model,inp):
m = model.predict([inp[0],inp[1]])
return m[2]
def reconstruct_from_left(model,inp):
img_inp = inp.reshape((28,14))
f, axarr = plt.subplots(1,2,sharey=False)
pred = model.predict([inp,np.zeros_like(inp)])
img = pred[0].reshape((28,14))
axarr[0].imshow(img_inp)
axarr[1].imshow(img)
def reconstruct_from_right(model,inp):
img_inp = inp.reshape((28,14))
f, axarr = plt.subplots(1,2,sharey=False)
pred = model.predict([np.zeros_like(inp),inp])
img = pred[1].reshape((28,14))
axarr[1].imshow(img_inp)
axarr[0].imshow(img)
def sum_corr(model):
view1 = np.load("test_v1.npy")
view2 = np.load("test_v2.npy")
x = project(model,[view1,np.zeros_like(view1)])
y = project(model,[np.zeros_like(view2),view2])
print ("test correlation")
corr = 0
for i in range(0,len(x[0])):
x1 = x[:,i] - (np.ones(len(x))*(sum(x[:,i])/len(x)))
x2 = y[:,i] - (np.ones(len(y))*(sum(y[:,i])/len(y)))
nr = sum(x1 * x2)/(math.sqrt(sum(x1*x1))*math.sqrt(sum(x2*x2)))
corr+=nr
print (corr)
def transfer(model):
view1 = np.load("test_v1.npy")
view2 = np.load("test_v2.npy")
labels = np.load("test_l.npy")
view1 = project(model,[view1,np.zeros_like(view1)])
view2 = project(model,[np.zeros_like(view2),view2])
perp = len(view1) // 5
print ("view1 to view2")
acc = 0
for i in range(5):
test_x = view2[int(i*perp):int((i+1)*perp)]
test_y = labels[i*perp:(i+1)*perp]
if i==0:
train_x = view1[perp:len(view1)]
train_y = labels[perp:len(view1)]
elif i==4:
train_x = view1[0:4*perp]
train_y = labels[0:4*perp]
else:
train_x1 = view1[0:i*perp]
train_y1 = labels[0:i*perp]
train_x2 = view1[(i+1)*perp:len(view1)]
train_y2 = labels[(i+1)*perp:len(view1)]
train_x = np.concatenate((train_x1,train_x2))
train_y = np.concatenate((train_y1,train_y2))
va, ta = svm_classifier(train_x, train_y, test_x, test_y, test_x, test_y)
acc += ta
print (acc/5)
print ("view2 to view1")
acc = 0
for i in range(0,5):
test_x = view1[i*perp:(i+1)*perp]
test_y = labels[i*perp:(i+1)*perp]
if i==0:
train_x = view2[perp:len(view1)]
train_y = labels[perp:len(view1)]
elif i==4:
train_x = view2[0:4*perp]
train_y = labels[0:4*perp]
else:
train_x1 = view2[0:i*perp]
train_y1 = labels[0:i*perp]
train_x2 = view2[(i+1)*perp:len(view1)]
train_y2 = labels[(i+1)*perp:len(view1)]
train_x = np.concatenate((train_x1,train_x2))
train_y = np.concatenate((train_y1,train_y2))
va, ta = svm_classifier(train_x, train_y, test_x, test_y, test_x, test_y)
acc += ta
print (acc/5)
def prepare_data():
data_l = np.load('data_l.npy')
data_r = np.load('data_r.npy')
label = np.load('data_label.npy')
X_train_l, X_test_l, X_train_r, X_test_r,y_train,y_test = split(data_l,data_r,label,ratio=0.0)
return X_train_l, X_train_r
def buildModel(loss_type,lamda):
inpx = Input(shape=(dimx,))
inpy = Input(shape=(dimy,))
hx = Reshape((28,14,1))(inpx)
hx = Conv2D(128, (3, 3), activation='relu', padding='same')(hx)
hx = MaxPooling2D((2, 2), padding='same')(hx)
hx = Conv2D(64, (3, 3), activation='relu', padding='same')(hx)
hx = MaxPooling2D((2, 2), padding='same')(hx)
hx = Conv2D(49, (3, 3), activation='relu', padding='same')(hx)
hx = MaxPooling2D((2, 2), padding='same')(hx)
hx = Flatten()(hx)
hx1 = Dense(hdim_deep,activation='sigmoid')(hx)
hx2 = Dense(hdim_deep2, activation='sigmoid',name='hid_l1')(hx1)
hx = Dense(hdim, activation='sigmoid',name='hid_l')(hx2)
hy = Reshape((28,14,1))(inpy)
hy = Conv2D(128, (3, 3), activation='relu', padding='same')(hy)
hy = MaxPooling2D((2, 2), padding='same')(hy)
hy = Conv2D(64, (3, 3), activation='relu', padding='same')(hy)
hy = MaxPooling2D((2, 2), padding='same')(hy)
hy = Conv2D(49, (3, 3), activation='relu', padding='same')(hy)
hy = MaxPooling2D((2, 2), padding='same')(hy)
hy = Flatten()(hy)
hy1 = Dense(hdim_deep,activation='sigmoid')(hy)
hy2 = Dense(hdim_deep2, activation='sigmoid',name='hid_r1')(hy1)
hy = Dense(hdim, activation='sigmoid',name='hid_r')(hy2)
h = Merge(mode="sum")([hx,hy])
recx = Dense(dimx)(h)
recy = Dense(dimy)(h)
branchModel = Model( [inpx,inpy],[recx,recy,h,hx1,hy1,hx2,hy2])
[recx1,recy1,h1,_,_,_,_] = branchModel( [inpx, ZeroPadding()(inpy)])
[recx2,recy2,h2,_,_,_,_] = branchModel( [ZeroPadding()(inpx), inpy ])
#you may probably add a reconstruction from combined
[recx3,recy3,h3,hx_1,hy_1,hx_2,hy_2] = branchModel([inpx, inpy])
lamda2,lamda3 = 0.001,0.05
corr1=CorrnetCost(-lamda)([h1,h2])
corr2=CorrnetCost(-lamda2)([hx_1,hy_1])
corr3=CorrnetCost(-lamda3)([hx_2,hy_2])
model = Model( [inpx,inpy],[recy1,recx2,recx1,recy2,corr1,corr2,corr3])
model.compile( loss=["mse","mse","mse","mse",corr_loss,corr_loss,corr_loss],optimizer="rmsprop")
return model, branchModel
def trainModel(model,data_left,data_right,loss_type,nb_epoch,batch_size):
X_train_l = data_left
X_train_r = data_right
data_l = np.load('data_l.npy')
data_r = np.load('data_r.npy')
label = np.load('data_label.npy')
X_train_l, X_test_l, X_train_r, X_test_r,y_train,y_test = split(data_l,data_r,label,ratio=0.01)
print ('data split')
print ('L_Type: l2+l3-L4 h_dim:',hdim,' hdim_deep',hdim_deep,' lamda:',lamda)
model.fit([X_train_l,X_train_r], [X_train_r,X_train_l,X_train_l,X_train_r,
np.zeros((X_train_l.shape[0],h_loss)),
np.zeros((X_train_l.shape[0],hdim_deep)),np.zeros((X_train_l.shape[0],hdim_deep2))],
nb_epoch=nb_epoch,
batch_size=batch_size,verbose=1)
def testModel(b_model):
transfer(b_model)
sum_corr(b_model)
left_view, right_view = prepare_data()
model,branchModel = buildModel(loss_type=loss_type,lamda=lamda)
trainModel(model=model, data_left=left_view, data_right = right_view,
loss_type=loss_type,nb_epoch=nb_epoch,batch_size=batch_size)
testModel(branchModel)
