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# In[1]: | |
get_ipython().magic(u'matplotlib inline') | |
from matplotlib import pyplot as plt | |
from __future__ import division, print_function, absolute_import | |
import pandas as pd | |
import numpy as np | |
import time as t | |
import numpy as np | |
from PIL import Image | |
import tflearn | |
from tflearn.data_utils import shuffle, to_categorical | |
from tflearn.layers.core import input_data, dropout, fully_connected | |
from tflearn.layers.conv import conv_2d, max_pool_2d | |
from tflearn.layers.estimator import regression | |
from tflearn.data_preprocessing import ImagePreprocessing | |
from tflearn.data_augmentation import ImageAugmentation | |
# In[ ]: | |
def load_data(): | |
X = [] | |
Y = [] | |
# dataset 1 | |
for i in range(1, 196 - 1): | |
im_x = Image.open('images/1_before.jpg_' + str(i) + '.jpg') | |
X.append(np.asarray(im_x)) | |
im_x.close() | |
im_y = Image.open('images/1_after.jpg_' + str(i) + '.jpg') | |
Y.append(np.asarray(im_y)) | |
im_y.close() | |
# dataset 2 | |
for i in range(1, 64 - 1): | |
im_x = Image.open('images/2_before.jpg_' + str(i) + '.jpg') | |
X.append(np.asarray(im_x)) | |
im_x.close() | |
im_y = Image.open('images/2_after.jpg_' + str(i) + '.jpg') | |
Y.append(np.asarray(im_y)) | |
im_y.close() | |
# dataset 3 | |
for i in range(1, 441 - 1): | |
im_x = Image.open('images/3_before.jpg_' + str(i) + '.jpg') | |
X.append(np.asarray(im_x)) | |
im_x.close() | |
im_y = Image.open('images/3_after.jpg_' + str(i) + '.jpg') | |
Y.append(np.asarray(im_y)) | |
im_y.close() | |
return zip(X, Y) | |
# In[ ]: | |
dataset = np.asarray(load_data()) | |
np.random.shuffle(dataset) | |
# test 20% | |
test_percentage = int(len(dataset) * 0.8) | |
dataset, dataset_test = np.vsplit(dataset, [test_percentage]) | |
X, Y = dataset[:, 0], dataset[:, 1] | |
X_test, Y_test = dataset_test[:, 0], dataset_test[:, 1] | |
# In[ ]: | |
# create DNN | |
net = tflearn.input_data(shape=[None, 32, 32, 3]) | |
net = tflearn.dropout(net, 0.8) | |
net = tflearn.fully_connected(net, 32*32, activation='relu') | |
net = tflearn.dropout(net, 0.5) | |
net = tflearn.fully_connected(net, 32*8, activation='relu') | |
net = tflearn.dropout(net, 0.5) | |
net = tflearn.fully_connected(net, 32*32, activation='relu') | |
net = tflearn.dropout(net, 0.5) | |
net = tflearn.fully_connected(net, 32*32*3, activation='relu') | |
net = tflearn.reshape(net, (-1, 32, 32, 3)) | |
net = tflearn.regression(net, optimizer='adam', learning_rate=0.001, | |
loss='mean_square', metric=None) | |
model = tflearn.DNN(net, tensorboard_verbose=0) | |
# learn | |
batch_size = int(len(X) / 3) | |
n_epoch = batch_size * 3 * 3 * 3 | |
model.fit(X, Y, n_epoch=n_epoch, run_id="image_optimize", batch_size=batch_size, validation_set=(X_test, Y_test)) |
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