def testingCNN(self, img_test):
print("Proses")
bar = progressbar.ProgressBar(maxval=2,
widgets=[
progressbar.Bar('=', '[', ']'), ' ',
progressbar.Percentage()
])
bar.start()
## Panggil filter 12345
filter_load = np.load("bobot/filter.npz")
l1_filter = filter_load['f1']
l2_filter = filter_load['f2']
# l3_filter = filter_load['f3']
# l4_filter = filter_load['f4']
# l5_filter = filter_load['f5']
## Panggil bobot+bias
bobot_simpan = np.load("bobot/0,000125/bobot200.npz")
weight = bobot_simpan['bobot']
bias = bobot_simpan['biass']
## Layer 1 ##
# C1 #
l1_feature_map = numpycnn.conv(img_test, l1_filter)
l1_feature_map_relu = numpycnn.relu(l1_feature_map)
# S1 #
l1_feature_map_relu_pool = numpycnn.pooling(l1_feature_map_relu)
bar.update(1)
time.sleep(0.1)
## Layer 2 ##
# C2 #
l2_feature_map = numpycnn.conv(l1_feature_map_relu_pool, l2_filter)
l2_feature_map_relu = numpycnn.relu(l2_feature_map)
# S2 #
l2_feature_map_relu_pool = numpycnn.pooling(l2_feature_map_relu)
bar.update(2)
time.sleep(0.1)
# ## Layer 3 ##
# # C3 #
# l3_feature_map = numpycnn.conv(l2_feature_map_relu_pool, l3_filter)
# l3_feature_map_relu = numpycnn.relu(l3_feature_map)
#
# # S3 #
# l3_feature_map_relu_pool = numpycnn.pooling(l3_feature_map_relu)
#
# bar.update(3)
# time.sleep(0.1)
#
# ## Layer 4 ##
# # C4 #
# l4_feature_map = numpycnn.conv(l3_feature_map_relu_pool, l4_filter)
# l4_feature_map_relu = numpycnn.relu(l4_feature_map)
#
# # S4 #
# l4_feature_map_relu_pool = numpycnn.pooling(l4_feature_map_relu)
#
# bar.update(4)
# time.sleep(0.1)
#
# ## Layer 5 ##
# # C5 #
# l5_feature_map = numpycnn.conv(l4_feature_map_relu_pool, l5_filter)
# l5_feature_map_relu = numpycnn.relu(l5_feature_map)
# npad = ((0,1), (0,1), (0,0))
# l5_feature_map_relu = np.pad(l5_feature_map_relu, pad_width=npad, mode='constant', constant_values=0)
#
# # S5 #
# l5_feature_map_relu_pool = numpycnn.pooling(l5_feature_map_relu)
#
# bar.update(5)
# time.sleep(0.1)
bar.finish()
### Vektorisasi ###
vektor = np.zeros((4, 1, 5184))
for i in range(0, 4):
vektor[i, :, :] = l2_feature_map_relu_pool[:, :, i].flatten()
flatten = np.reshape(vektor, 20736).flatten()
flatten = flatten.reshape((-1, 1))
### Fully-connected ###
kelas = ["Optimis", "Pesimis", "Seimbang"]
fully = []
for i in range(0, len(kelas)):
fully.append(np.sum(flatten * weight[i, :]) + bias[i])
## Exponensial ##
eksponen = []
for i in range(0, len(kelas)):
eksponen.append(math.exp(fully[i]))
## Softmax ##
softmax = []
for i in range(0, len(kelas)):
softmax.append(eksponen[i] / sum(eksponen))
print("\n")
# print("--> Hasil prediksi :",softmax)
print("Maka, kepribadian yang diprediksi adalah :",
kelas[np.argmax(softmax)])
self.label.setText(kelas[np.argmax(softmax)])
def trainingCNN(self):
## Timer ##
start = time.time()
# ##### Siapin Data #####
training_data = []
DATADIR = "D:/Datasets/Benar/TulisanTanganTest/"
CATEGORIES = ["Optimis", "Pesimis", "Seimbang"]
list1 = os.listdir(os.path.join(DATADIR, "Optimis"))
list2 = os.listdir(os.path.join(DATADIR, "Pesimis"))
list3 = os.listdir(os.path.join(DATADIR, "Seimbang"))
jumlah_file = len(list1) + len(list2) + len(list3)
print("Preprocessing")
print(jumlah_file)
bar = progressbar.ProgressBar(maxval=50,
widgets=[
progressbar.Bar('=', '[', ']'), ' ',
progressbar.Percentage()
])
bar.start()
x = 0
for category in CATEGORIES:
path = os.path.join(
DATADIR,
category) # Ambil Folder Kelas['Optimis','Pesimis','Seimbang']
class_num = CATEGORIES.index(category)
for gambar in os.listdir(path):
img = cv2.imread(os.path.join(path, gambar))
gray = self.grayscale(img)
thres = self.threshold(gray)
crop = self.segmentasi(thres, img)
res = self.resize(crop)
training_data.append([res, class_num])
random.shuffle(training_data)
bar.update(x + 1)
time.sleep(0.1)
x = x + 1
bar.finish()
end = time.time()
cetak = "Lamanya Proses : " + str(int(round(end - start))) + "s"
self.textBrowser.append(cetak)
# Split data training
X = []
y = []
for features, label in training_data:
X.append(features)
y.append(label)
for i in range(0, len(y)):
ohl = [0, 0, 0]
ohl[y[i]] = 1
y[i] = ohl
y = np.array(y)
# np.savez("bobot/draft/data.npz", matriks=X, label=y)
# data_load = np.load("bobot/data.npz")
# X = data_load['matriks']
# y = data_load['label']
# print(y)
##### END #####
##### CNN #####
#### Inisialisasi Awal ####
## Panggil bobot awal
## Panggil filter 12345
filter_load = np.load("bobot/filter.npz")
l1_filter = filter_load['f1']
l2_filter = filter_load['f2']
# l3_filter = filter_load['f3']
# l4_filter = filter_load['f4']
# l5_filter = filter_load['f5']
## Panggil weight
# weight = np.load("bobot/weight_awal.npy")
weight = np.zeros((3, 20736, 1))
for i in range(0, 2):
for j in range(0, 20735):
angka = round(random.uniform(-0.05, 0.05), 2)
weight[i, j, :] = angka
## Panggil Flatten
flatten = np.load("bobot/flatten.npy")
#### Feedforward ####
print("Training CNN")
# bar.start()
x = 0
bias = [0, 0, 0]
epoch = 200
lrate = 0.000125
for ep in range(0, epoch):
for image_i in range(0, len(X)):
cetak = "Input Ke-" + str(image_i + 1)
print(cetak)
self.textBrowser.append(cetak)
### Layer 1 ###
# C1
l1_feature_map = numpycnn.conv(X[image_i], l1_filter)
l1_feature_map_relu = numpycnn.relu(l1_feature_map)
# S1
l1_feature_map_relu_pool = numpycnn.pooling(
l1_feature_map_relu)
# ### Layer 2 ###
# C2
l2_feature_map = numpycnn.conv(l1_feature_map_relu_pool,
l2_filter)
l2_feature_map_relu = numpycnn.relu(l2_feature_map)
# S2
l2_feature_map_relu_pool = numpycnn.pooling(
l2_feature_map_relu)
# ### Layer 3 ###
# # C3
# l3_feature_map = numpycnn.conv(l2_feature_map_relu_pool, l3_filter)
# l3_feature_map_relu = numpycnn.relu(l3_feature_map)
#
# # S3
# l3_feature_map_relu_pool = numpycnn.pooling(l3_feature_map_relu)
#
# ### Layer 4 ###
# # C4
# l4_feature_map = numpycnn.conv(l3_feature_map_relu_pool, l4_filter)
# l4_feature_map_relu = numpycnn.relu(l4_feature_map)
#
# # S4
# l4_feature_map_relu_pool = numpycnn.pooling(l4_feature_map_relu)
#
# ### Layer 5 ###
# # C5
# l5_feature_map = numpycnn.conv(l4_feature_map_relu_pool, l5_filter)
# l5_feature_map_relu = numpycnn.relu(l5_feature_map)
# npad = ((0,1), (0,1), (0,0))
# l5_feature_map_relu = np.pad(l5_feature_map_relu, pad_width=npad, mode='constant', constant_values=0)
#
# # S5
# l5_feature_map_relu_pool = numpycnn.pooling(l5_feature_map_relu)
#
### Vektorisasi ###
vektor = np.zeros((4, 1, 5184))
for i in range(0, 4):
vektor[i, :, :] = l2_feature_map_relu_pool[:, :,
i].flatten()
flatten = np.reshape(vektor, 20736).flatten()
flatten = flatten.reshape((-1, 1))
flatten_simpan.append(flatten)
### Fully-connected ###
kelas = 3
fully = []
for i in range(0, kelas):
fully.append(
np.sum(flatten[image_i] * weight[i, :]) + bias[i])
## Exponensial ##
eksponen = []
for i in range(0, kelas):
eksponen.append(math.exp(fully[i]))
## Softmax ##
softmax = []
for i in range(0, kelas):
softmax.append(eksponen[i] / sum(eksponen))
## Cross-entropy Loss ##
ohl = y[image_i]
loss = []
terror = 0.00001
for i in range(0, kelas):
loss.append(ohl[i] * math.log10(softmax[i]))
loss = sum(loss) * -1
## Backpropagation ##
if loss > terror:
cetak = "-> epoch ke" + str(ep + 1)
print(cetak)
self.textBrowser.append(cetak)
delta_y = []
for i in range(0, kelas):
delta_y.append(softmax[i] - ohl[i])
delta_w = np.zeros((kelas, flatten[image_i].shape[0]))
for i in range(0, kelas):
for j in range(0, flatten[image_i].shape[0]):
delta_w[i, j] = delta_y[i] * flatten[image_i][j]
delta_b = delta_y
## SGD ##
weight_baru = np.zeros_like(weight)
for i in range(0, kelas):
for j in range(0, weight.shape[1]):
weight_baru[i, j] = weight[i, j] - (lrate *
delta_w[i, j])
weight = weight_baru
bias_baru = bias
for i in range(0, kelas):
bias_baru[i] = bias[i] - (lrate * delta_b[i])
bias = bias_baru
# FULLY LAGI #
fully = []
for i in range(0, kelas):
fully.append(
np.sum(flatten[image_i] * weight[i, :]) + bias[i])
## Exponensial ##
eksponen = []
for i in range(0, kelas):
eksponen.append(math.exp(fully[i]))
## Softmax ##
softmax = []
for i in range(0, kelas):
softmax.append(eksponen[i] / sum(eksponen))
cetak = "--> Softmax" + str(softmax)
print(cetak)
self.textBrowser.append(cetak)
# np.savez("bobot/filter.npz",f1=l1_filter, f2=l2_filter, f3=l3_filter, f4=l4_filter, f5=l5_filter)
# lokasi_weight = "bobot/0,000125/bobot" + str(ep + 1) + ".npz"
#
# np.savez(lokasi_weight, bobot=weight, biass=bias)
# if(ep+1 == 50):
# np.save("bobot/0,001/ep50/weight.npy", weight)
# np.save("bobot/0,001/ep50/bias.npy", bias)
# elif(ep+1 == 100):
# np.save("bobot/0,001/ep100/weight.npy", weight)
# np.save("bobot/0,001/ep100/bias.npy", bias)
# elif(ep+1 == 150):
# np.save("bobot/0,001/ep150/weight.npy", weight)
# np.save("bobot/0,001/ep150/bias.npy", bias)
# elif(ep+1 == 200):
# np.save("bobot/0,001/ep200/weight.npy", weight)
# np.save("bobot/0,001/ep200/bias.npy", bias)
# elif(ep+1 == 250):
# np.save("bobot/0,000125/ep250/weight.npy", weight)
# np.save("bobot/0,000125/ep250/bias.npy", bias)
bar.update(x + 1)
time.sleep(0.1)
x = x + 1
bar.finish()
end = time.time()
cetak = "Lamanya Proses : " + str(int(round(end - start))) + "s"
print(cetak)
self.textBrowser.append(cetak)
def cnn_example(self, numpy_img, layer=1):
if layer == 1:
#**Working with conv layer 1**
l1_filter = numpy.zeros((2, 3, 3))
l1_filter[0, :, :] = numpy.array([[[-1, 0, 1], [-1, 0, 1],
[-1, 0, 1]]])
l1_filter[1, :, :] = numpy.array([[[1, 1, 1], [0, 0, 0],
[-1, -1, -1]]])
l1_feature_map = numpycnn.conv(numpy_img, l1_filter)
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 0]))
im.save("conv1_filter1.png")
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 1]))
im.save("conv1_filter2.png")
l1_feature_map_relu = numpycnn.relu(l1_feature_map)
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 0]))
im.save("conv1_relu1.png")
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 1]))
im.save("conv1_relu2.png")
l1_feature_map_relu_pool = numpycnn.pooling(
l1_feature_map_relu, 2, 2)
im = PIL.Image.fromarray(
numpy.uint8(l1_feature_map_relu_pool[:, :, 0]))
im.save("conv1_relu_pool1.png")
im = PIL.Image.fromarray(
numpy.uint8(l1_feature_map_relu_pool[:, :, 1]))
im.save("conv1_relu_pool2.png")
return l1_feature_map_relu_pool
elif layer == 2:
#**Working with conv layer 2**
l2_filter = numpy.random.rand(3, 5, 5, numpy_img.shape[-1])
l2_feature_map = numpycnn.conv(numpy_img, l2_filter)
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 0]))
im.save("conv2_filter1.png")
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 1]))
im.save("conv2_filter2.png")
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 2]))
im.save("conv2_filter3.png")
l2_feature_map_relu = numpycnn.relu(l2_feature_map)
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 0]))
im.save("conv2_relu1.png")
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 1]))
im.save("conv2_relu2.png")
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 2]))
im.save("conv2_relu3.png")
l2_feature_map_relu_pool = numpycnn.pooling(
l2_feature_map_relu, 2, 2)
im = PIL.Image.fromarray(
numpy.uint8(l2_feature_map_relu_pool[:, :, 0]))
im.save("conv2_relu_pool1.png")
im = PIL.Image.fromarray(
numpy.uint8(l2_feature_map_relu_pool[:, :, 1]))
im.save("conv2_relu_pool2.png")
im = PIL.Image.fromarray(
numpy.uint8(l2_feature_map_relu_pool[:, :, 2]))
im.save("conv2_relu_pool3.png")
return l2_feature_map_relu_pool
elif layer == 0: #0 corresponds to layer 3
#**Working with conv layer 3**
l3_filter = numpy.random.rand(1, 7, 7, numpy_img.shape[-1])
l3_feature_map = numpycnn.conv(numpy_img, l3_filter)
im = PIL.Image.fromarray(numpy.uint8(l3_feature_map[:, :, 0]))
im.save("conv3_filter1.png")
l3_feature_map_relu = numpycnn.relu(l3_feature_map)
im = PIL.Image.fromarray(numpy.uint8(l3_feature_map_relu[:, :, 0]))
im.save("conv3_relu1.png")
l3_feature_map_relu_pool = numpycnn.pooling(
l3_feature_map_relu, 2, 2)
im = PIL.Image.fromarray(
numpy.uint8(l3_feature_map_relu_pool[:, :, 0]))
im.save("conv3_relu_pool1.png")
return l3_feature_map_relu_pool
# Converting the image into gray.
img = color.rgb2gray(img)
# First conv layer
# l1_filter = numpy.random.rand(2,7,7)*20 # Preparing the filters randomly.
l1_filter = numpy.zeros((2, 3, 3))
l1_filter[0, :, :] = numpy.array([[[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]])
l1_filter[1, :, :] = numpy.array([[[1, 1, 1], [0, 0, 0], [-1, -1, -1]]])
print("\n**Working with conv layer 1**")
l1_feature_map = numpycnn.conv(img, l1_filter)
print("\n**ReLU**")
l1_feature_map_relu = numpycnn.relu(l1_feature_map)
print("\n**Pooling**")
l1_feature_map_relu_pool = numpycnn.pooling(l1_feature_map_relu, 2, 2)
print("**End of conv layer 1**\n")
# Second conv layer
l2_filter = numpy.random.rand(3, 5, 5, l1_feature_map_relu_pool.shape[-1])
print("\n**Working with conv layer 2**")
l2_feature_map = numpycnn.conv(l1_feature_map_relu_pool, l2_filter)
print("\n**ReLU**")
l2_feature_map_relu = numpycnn.relu(l2_feature_map)
print("\n**Pooling**")
l2_feature_map_relu_pool = numpycnn.pooling(l2_feature_map_relu, 2, 2)
print("**End of conv layer 2**\n")
# Third conv layer
l3_filter = numpy.random.rand(1, 7, 7, l2_feature_map_relu_pool.shape[-1])
print("\n**Working with conv layer 3**")
def cnn_example(self, numpy_img, layer=1):
img1 = self.ids.img1
img2 = self.ids.img2
img3 = self.ids.img3
img4 = self.ids.img4
img5 = self.ids.img5
img6 = self.ids.img6
img7 = self.ids.img7
img8 = self.ids.img8
img9 = self.ids.img9
lbl1 = self.ids.lbl1
lbl2 = self.ids.lbl2
lbl3 = self.ids.lbl3
lbl4 = self.ids.lbl4
lbl5 = self.ids.lbl5
lbl6 = self.ids.lbl6
lbl7 = self.ids.lbl7
lbl8 = self.ids.lbl8
lbl9 = self.ids.lbl9
if layer == 1:
#**Working with conv layer 1**
l1_filter = numpy.zeros((2, 3, 3))
l1_filter[0, :, :] = numpy.array([[[-1, 0, 1], [-1, 0, 1],
[-1, 0, 1]]])
l1_filter[1, :, :] = numpy.array([[[1, 1, 1], [0, 0, 0],
[-1, -1, -1]]])
l1_feature_map = numpycnn.conv(numpy_img, l1_filter)
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 0]))
im.save("conv1_filter1.png")
lbl2.text = "L1Map1"
img2.source = "conv1_filter1.png"
img2.reload()
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 1]))
im.save("conv1_filter2.png")
lbl3.text = "L1Map2"
img3.source = "conv1_filter2.png"
img3.reload()
l1_feature_map_relu = numpycnn.relu(l1_feature_map)
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 0]))
im.save("conv1_relu1.png")
lbl4.text = "L1Map1ReLU"
img4.source = "conv1_relu1.png"
img4.reload()
im = PIL.Image.fromarray(numpy.uint8(l1_feature_map[:, :, 1]))
im.save("conv1_relu2.png")
lbl5.text = "L1Map2ReLU"
img5.source = "conv1_relu2.png"
img5.reload()
l1_feature_map_relu_pool = numpycnn.pooling(
l1_feature_map_relu, 2, 2)
im = PIL.Image.fromarray(
numpy.uint8(l1_feature_map_relu_pool[:, :, 0]))
im.save("conv1_relu_pool1.png")
lbl6.text = "L1Map1ReLUPool"
img6.source = "conv1_relu_pool1.png"
img6.reload()
im = PIL.Image.fromarray(
numpy.uint8(l1_feature_map_relu_pool[:, :, 1]))
im.save("conv1_relu_pool2.png")
lbl7.text = "L1Map2ReLUPool"
img7.source = "conv1_relu_pool2.png"
img7.reload()
return l1_feature_map_relu_pool
elif layer == 2:
#**Working with conv layer 2**
l2_filter = numpy.random.rand(3, 5, 5, numpy_img.shape[-1])
l2_feature_map = numpycnn.conv(numpy_img, l2_filter)
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 0]))
im.save("conv2_filter1.png")
lbl1.text = "L2Map1"
img1.source = "conv2_filter1.png"
img1.reload()
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 1]))
im.save("conv2_filter2.png")
lbl2.text = "L2Map2"
img2.source = "conv2_filter2.png"
img2.reload()
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map[:, :, 2]))
im.save("conv2_filter3.png")
lbl3.text = "L2Map3"
img3.source = "conv2_filter3.png"
img3.reload()
l2_feature_map_relu = numpycnn.relu(l2_feature_map)
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 0]))
im.save("conv2_relu1.png")
lbl4.text = "L2Map1ReLU"
img4.source = "conv2_relu1.png"
img4.reload()
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 1]))
im.save("conv2_relu2.png")
lbl5.text = "L2Map3ReLU"
img5.source = "conv2_relu2.png"
img5.reload()
im = PIL.Image.fromarray(numpy.uint8(l2_feature_map_relu[:, :, 2]))
im.save("conv2_relu3.png")
lbl6.text = "L2Map3ReLU"
img6.source = "conv2_relu3.png"
img6.reload()
l2_feature_map_relu_pool = numpycnn.pooling(
l2_feature_map_relu, 2, 2)
im = PIL.Image.fromarray(
numpy.uint8(l2_feature_map_relu_pool[:, :, 0]))
im.save("conv2_relu_pool1.png")
lbl7.text = "L2Map1ReLU"
img7.source = "conv2_relu_pool1.png"
img7.reload()
im = PIL.Image.fromarray(
numpy.uint8(l2_feature_map_relu_pool[:, :, 1]))
im.save("conv2_relu_pool2.png")
lbl8.text = "L2Map2ReLU"
img8.source = "conv2_relu_pool2.png"
img8.reload()
im = PIL.Image.fromarray(
numpy.uint8(l2_feature_map_relu_pool[:, :, 2]))
im.save("conv2_relu_pool3.png")
lbl9.text = "L2Map3ReLU"
img9.source = "conv2_relu_pool3.png"
img9.reload()
return l2_feature_map_relu_pool
elif layer == 3:
lbl1.text = "Original"
img1.source = "input_image.jpg"
img1.reload()
#**Working with conv layer 3**
l3_filter = numpy.random.rand(1, 7, 7, numpy_img.shape[-1])
l3_feature_map = numpycnn.conv(numpy_img, l3_filter)
im = PIL.Image.fromarray(numpy.uint8(l3_feature_map[:, :, 0]))
im.save("conv3_filter1.png")
lbl2.text = "L3Map1"
img2.source = "conv3_filter1.png"
img2.reload()
l3_feature_map_relu = numpycnn.relu(l3_feature_map)
im = PIL.Image.fromarray(numpy.uint8(l3_feature_map_relu[:, :, 0]))
im.save("conv3_relu1.png")
lbl3.text = "L3Map1ReLU"
img3.source = "conv3_relu1.png"
img3.reload()
l3_feature_map_relu_pool = numpycnn.pooling(
l3_feature_map_relu, 2, 2)
im = PIL.Image.fromarray(
numpy.uint8(l3_feature_map_relu_pool[:, :, 0]))
im.save("conv3_relu_pool1.png")
lbl4.text = "L3Map1ReLUPool"
img4.source = "conv3_relu_pool1.png"
img4.reload()
img5.source = ""
img5.reload()
img6.source = ""
img6.reload()
img7.source = ""
img7.reload()
img8.source = ""
img8.reload()
img9.source = ""
img9.reload()
lbl5.text = ""
lbl6.text = ""
lbl7.text = ""
lbl8.text = ""
lbl9.text = ""
return l3_feature_map_relu_pool