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MonaNet.py
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MonaNet.py
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import sys
import numpy as np
import cv2
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD, RMSprop
from random import randint, shuffle
image_file = cv2.imread("Mona Lisa.jpg")
h = image_file.shape[0]
w = image_file.shape[1]
model = Sequential()
model.add(Dense(500, input_dim=2, init='uniform'))
model.add(Activation('relu'))
model.add(Dense(500, init='uniform'))
model.add(Activation('relu'))
model.add(Dense(500, init='uniform'))
model.add(Activation('relu'))
model.add(Dense(500, init='uniform'))
model.add(Activation('relu'))
model.add(Dense(500, init='uniform'))
model.add(Activation('relu'))
model.add(Dense(3, init='uniform'))
model.add(Activation('linear'))
# sgd = SGD(lr=0.1, momentum=0.94)
model.compile(loss='mean_squared_error', optimizer=RMSprop())
x1 = []
y1 = []
for i in xrange(h):
for j in xrange(w):
x1.append([i,j])
y1.append(image_file[i,j,:].astype('float32')/255.0)
zip_1 = [i for i in zip(x1,y1)]
nb_epochs = 1000
for e in xrange(nb_epochs):
print "Epoch: ",e
shuffle(zip_1)
X_train = np.array([i for (i,j) in zip_1])
Y_train = np.array([j for (i,j) in zip_1])
model.fit(X_train, Y_train,
nb_epoch=1,
batch_size=500,
show_accuracy=True)
X = []
X.extend(x1)
X = sorted(X)
predictions = model.predict(np.array(X))*255.0
for i in xrange(len(predictions)):
for j in xrange(3):
predictions[i][j] = min(predictions[i][j],255.0)
predictions = predictions.astype('uint8')
output_image = []
index = 0
for i in xrange(h):
row = []
for j in xrange(w):
row.append(predictions[index])
index += 1
row = np.array(row)
output_image.append(row)
output_image = np.array(output_image)
cv2.imwrite('out_mona_500x5_e'+str(e)+'.png',output_image)