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2layer_cnn.py
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import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from PIL import Image
import theano
from sklearn.cross_validation import train_test_split
from keras.utils import np_utils, generic_utils
import time
from keras.regularizers import l2, activity_l2
# Load Images Put them into Test and Train Set
t1=time.clock()
im1=Image.open('Rain_B_1.png')
im2=Image.open('Rain_R_1.png')
im3=Image.open('Rain_G_1.png')
im4=Image.open('Rain_P_1.png')
im5=Image.open('Rain_M_1.png')
x1=np.array(im1)
m_val=float(x1.max())
x1=x1/m_val
x1=x1[None,:,:]
x2=np.array(im2)
m_val=float(x2.max())
x2=x2/m_val
x2=x2[None,:,:]
X=np.append(x1,x2,axis=0)
x3=np.array(im3)
m_val=float(x3.max())
x3=x3/m_val
x3=x3[None,:,:]
X=np.append(X,x3,axis=0)
x4=np.array(im4)
m_val=float(x4.max())
x4=x4/m_val
x4=x4[None,:,:]
X=np.append(X,x4,axis=0)
x5=np.array(im5)
m_val=float(x5.max())
x5=x5/m_val
x5=x5[None,:,:]
X=np.append(X,x5,axis=0)
X=X[None,:,:,:]
N=15219
for i in range(2,N):
f1='Rain_R_'+str(i)+'.png'
f2='Rain_B_'+str(i)+'.png'
f3='Rain_G_'+str(i)+'.png'
f4='Rain_P_'+str(i)+'.png'
f5='Rain_M_'+str(i)+'.png'
im1=Image.open(f1)
im2=Image.open(f2)
im3=Image.open(f3)
im4=Image.open(f4)
im5=Image.open(f5)
x1=np.array(im1)
m_val=float(x1.max())
x1=x1/m_val
x1=x1[None,:,:]
x2=np.array(im2)
m_val=float(x2.max())
x2=x2/m_val
x2=x2[None,:,:]
Xi=np.append(x1,x2,axis=0)
x3=np.array(im3)
m_val=float(x3.max())
x3=x3/m_val
x3=x3[None,:,:]
Xi=np.append(Xi,x3,axis=0)
x4=np.array(im4)
m_val=float(x4.max())
x4=x4/m_val
x4=x4[None,:,:]
Xi=np.append(Xi,x4,axis=0)
x5=np.array(im5)
m_val=float(x5.max())
x5=x5/m_val
x5=x5[None,:,:]
Xi=np.append(Xi,x5,axis=0)
Xi=Xi[None,:,:,:]
X=np.append(X,Xi,axis=0)
Y=np.genfromtxt('5_image_GUAGE_VALS.csv',delimiter=',')
# Break into test and train start with 900 images for train
vec=np.array(range(X.shape[0]))
vec.astype(np.int64)
tr=np.random.choice(vec,size=10000,replace=False)
tr.astype(np.int64)
tst=np.setdiff1d(vec,tr)
print(type(tst))
tst.astype(np.int64)
A=tr.tolist()
B=tst.tolist()
print(type(A))
X_train=X[A,:,:,:]
y_train=Y[A]
X_test=X[B,:,:,:]
y_test=Y[B]
#y_train, y_test = [np_utils.to_categorical(x) for x in (y_train, y_test)]
print('Data Load, test, train complete')
model=Sequential()
#Convolutional Layer
model.add(Convolution2D(10,3,3, border_mode='valid', input_shape=(5,23,23)))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Convolution2D(10,3,3))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(32))
model.add(Activation('tanh'))
model.add(Dropout(0.25))
model.add(Dense(1,input_dim=32,W_regularizer=l2(0.01),activity_regularizer=activity_l2(0.01)))
model.add(Activation('linear'))
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_absolute_error', optimizer='sgd')
model.fit(X_train,y_train, batch_size=1000, nb_epoch=20,show_accuracy=True)
train_vals=model.predict(X_train,batch_size=1000)
test_vals=model.predict(X_test, batch_size=1000)
#print(type(train_vals))
#print(test_vals)
np.savetxt('RAIN_Train_five_layer2_3.csv',np.array(train_vals))
np.savetxt('RAIN_Test_five_layer2_3.csv',np.array(test_vals))
np.savetxt('Y_test_five_layer2_3.csv',y_test)
np.savetxt('Y_train_five_layer2_3.csv',y_train)
#score=model.evaluate(X_test,y_test,batch_size=100,show_accuracy=True)
#print(score)
t2=time.clock()
print((t2-t1))