class CNN1(object):
def __init__(self, input_width, input_height, channel_number,
learning_rate, cost_function):
self.cost_function = cost_function
self.predict_output_list = []
self.conv1 = ConvLayer(input_width, input_height, channel_number, 3, 3,
64, 1, 1, ReluActivator(), learning_rate)
# self.conv2 = ConvLayer(input_width, input_height, 64,
# 3, 3, 64, input_width // 2 + 1, 2, ReluActivator(), learning_rate)
# self.conv2 = ConvLayer(input_width, input_height, 8,
# 3, 3, 8, 1, 1, ReluActivator(), learning_rate)
self.conv3 = MaxPoolingLayer(input_width, input_height, 64, 3, 3, 1, 2)
# self.conv4 = ConvLayer(input_width, input_height, 16,
# 3, 3, 16, 1, 1, ReluActivator(), learning_rate)
self.conv5 = ConvLayer(input_width // 2, input_height // 2, 64, 3, 3,
128, 1, 1, ReluActivator(), learning_rate)
self.conv6 = MaxPoolingLayer(input_width // 2, input_height // 2, 128,
3, 3, 1, 2)
self.conv7 = ConvLayer(input_width // 4, input_height // 4, 128, 3, 3,
256, 1, 1, ReluActivator(), learning_rate)
self.conv8 = UpsamplingLayer(input_width // 4, input_height // 4, 256,
3, 3, 128, 1, 1, learning_rate)
self.conv9 = ConvLayer(input_width // 2, input_height // 2, 128, 3, 3,
32, 1, 1, ReluActivator(), learning_rate)
self.conv10 = UpsamplingLayer(input_width // 2, input_height // 2, 32,
3, 3, 32, 1, 1, learning_rate)
# self.conv11 = ConvLayer(input_width, input_height, 8,
# 3, 3, 4, 1, 1, ReluActivator(), learning_rate)
self.conv12 = ConvLayer(input_width, input_height, 32, 3, 3, 2, 1, 1,
NoneActivator(), learning_rate)
def train_forward(self, input_array, new_batch):
if np.random.randint(2, size=1)[0] == 1 or new_batch == 1:
self.input_array = input_array
self.conv1.forward(input_array)
self.conv1_output_array = self.conv1.output_array
# self.conv2.forward(self.conv1_output_array)
# self.conv2_output_array = self.conv2.output_array
self.conv3.forward(self.conv1_output_array)
self.conv3_output_array = self.conv3.output_array
# self.conv4.forward(self.conv3_output_array)
# self.conv4_output_array = self.conv4.output_array
self.conv5.forward(self.conv3_output_array)
self.conv5_output_array = self.conv5.output_array
self.conv6.forward(self.conv5_output_array)
self.conv6_output_array = self.conv6.output_array
self.conv7.forward(self.conv6_output_array)
self.conv7_output_array = self.conv7.output_array
self.conv8.forward(self.conv7_output_array)
self.conv8_output_array = self.conv8.output_array
self.conv9.forward(self.conv8_output_array)
self.conv9_output_array = self.conv9.output_array
self.conv10.forward(self.conv9_output_array)
self.conv10_output_array = self.conv10.output_array
# self.conv11.forward(self.conv10_output_array)
# self.conv11_output_array = self.conv11.output_array
else:
self.conv1.forward(input_array)
# self.conv2.forward(self.conv1.output_array)
self.conv3.forward(self.conv1.output_array)
# self.conv4.forward(self.conv3.output_array)
self.conv5.forward(self.conv3.output_array)
self.conv6.forward(self.conv5.output_array)
self.conv7.forward(self.conv6.output_array)
self.conv8.forward(self.conv7.output_array)
self.conv9.forward(self.conv8.output_array)
self.conv10.forward(self.conv9.output_array)
# self.conv11.forward(self.conv10.output_array)
self.conv12.forward(self.conv10_output_array)
if new_batch == 1:
self.predict_output_list = []
self.predict_output_list.append(self.conv12.output_array)
def train_backward(self, actual_output_list):
delta_array = self.cost_function(actual_output_list,
self.predict_output_list)
self.conv12.backward(delta_array)
print(1)
# self.conv11.backward(self.conv12.delta_array)
# print(2)
self.conv10.backward(self.conv12.delta_array)
print(3)
self.conv9.backward(self.conv10.delta_array)
print(4)
self.conv8.backward(self.conv9.delta_array)
print(5)
self.conv7.backward(self.conv8.delta_array)
print(6)
self.conv6.backward(self.conv5_output_array, self.conv7.delta_array)
print(7)
self.conv5.backward(self.conv6.delta_array)
print(8)
# self.conv4.backward(self.conv5.delta_array)
# print(9)
self.conv3.backward(self.conv1_output_array, self.conv5.delta_array)
print(10)
# self.conv2.backward(self.conv3.delta_array)
# print(11)
self.conv1.update(self.input_array, self.conv3.delta_array)
# self.conv2.update(self.conv1_output_array, self.conv3.delta_array)
# self.conv3.update(self.conv1_output_array, self.conv5.delta_array)
# self.conv4.update(self.conv3_output_array, self.conv5.delta_array)
self.conv5.update(self.conv3_output_array, self.conv6.delta_array)
# self.conv6.update(self.conv5_output_array, self.conv7.delta_array)
self.conv7.update(self.conv6_output_array, self.conv8.delta_array)
self.conv8.update(self.conv7_output_array, self.conv9.delta_array)
self.conv9.update(self.conv8_output_array, self.conv10.delta_array)
self.conv10.update(self.conv9_output_array, self.conv12.delta_array)
# self.conv11.update(self.conv10_output_array, self.conv12.delta_array)
self.conv12.update(self.conv10_output_array, delta_array)
def output(self, input_array):
self.conv1.forward(input_array)
# self.conv2.forward(self.conv1.output_array)
self.conv3.forward(self.conv1.output_array)
# self.conv4.forward(self.conv3.output_array)
self.conv5.forward(self.conv3.output_array)
self.conv6.forward(self.conv5.output_array)
self.conv7.forward(self.conv6.output_array)
self.conv8.forward(self.conv7.output_array)
self.conv9.forward(self.conv8.output_array)
self.conv10.forward(self.conv9.output_array)
# self.conv11.forward(self.conv10.output_array)
self.conv12.forward(self.conv10_output_array)
return self.conv12.output_array
class CNN(object):
def __init__(self, input_width, input_height, channel_number,
learning_rate, cost_function):
self.cost_function = cost_function
self.predict_output_list = []
self.conv1 = ConvLayer(input_width, input_height, channel_number, 3, 3,
16, 1, 1, ReluActivator(), learning_rate)
self.conv3 = MaxPoolingLayer(input_width, input_height, 16, 3, 3, 1, 2)
self.conv4 = ConvLayer(input_width // 2, input_height // 2, 16, 3, 3,
32, 1, 1, ReluActivator(), learning_rate)
self.conv5 = MaxPoolingLayer(input_width // 2, input_height // 2, 32,
3, 3, 1, 2)
self.conv6 = ConvLayer(input_width // 4, input_height // 4, 32, 3, 3,
32, 1, 1, ReluActivator(), learning_rate)
self.conv8 = UpsamplingLayer(input_width // 4, input_height // 4, 32)
self.conv9 = ConvLayer(input_width // 2, input_height // 2, 32, 3, 3,
16, 1, 1, ReluActivator(), learning_rate)
self.conv10 = UpsamplingLayer(input_width // 2, input_height // 2, 16)
self.conv12 = ConvLayer(input_width, input_height, 16, 3, 3, 2, 1, 1,
TanhActivator(), learning_rate)
def train_forward(self, input_array, new_batch):
if np.random.randint(2, size=1)[0] == 1 or new_batch == 1:
self.input_array = input_array
self.conv1.forward(input_array)
self.conv1_output_array = np.array(self.conv1.output_array)
self.conv3.forward(self.conv1.output_array)
self.conv3_output_array = np.array(self.conv3.output_array)
self.conv4.forward(self.conv3.output_array)
self.conv4_output_array = np.array(self.conv4.output_array)
self.conv5.forward(self.conv4.output_array)
self.conv5_output_array = np.array(self.conv5.output_array)
self.conv6.forward(self.conv5.output_array)
self.conv6_output_array = np.array(self.conv6.output_array)
self.conv8.forward(self.conv6.output_array)
self.conv8_output_array = np.array(self.conv8.output_array)
self.conv9.forward(self.conv8.output_array)
self.conv9_output_array = np.array(self.conv9.output_array)
self.conv10.forward(self.conv9.output_array)
self.conv10_output_array = np.array(self.conv10.output_array)
else:
self.conv1.forward(input_array)
self.conv3.forward(self.conv1.output_array)
self.conv4.forward(self.conv3.output_array)
self.conv5.forward(self.conv4.output_array)
self.conv6.forward(self.conv5.output_array)
self.conv8.forward(self.conv6.output_array)
self.conv9.forward(self.conv8.output_array)
self.conv10.forward(self.conv9.output_array)
self.conv12.forward(self.conv10.output_array)
if new_batch == 1:
self.predict_output_list = []
conv12_output_array = np.array(self.conv12.output_array)
self.predict_output_list.append(conv12_output_array)
def train_backward(self, actual_output_list):
delta_array = self.cost_function(actual_output_list,
self.predict_output_list)
self.conv12.backward(delta_array)
self.conv10.backward(self.conv12.delta_array)
self.conv9.backward(self.conv10.delta_array)
self.conv8.backward(self.conv9.delta_array)
self.conv6.backward(self.conv8.delta_array)
self.conv5.backward(self.conv4_output_array, self.conv6.delta_array)
self.conv4.backward(self.conv5.delta_array)
self.conv3.backward(self.conv1_output_array, self.conv4.delta_array)
self.conv1.update(self.input_array, self.conv3.delta_array)
self.conv4.update(self.conv3_output_array, self.conv5.delta_array)
self.conv6.update(self.conv5_output_array, self.conv8.delta_array)
self.conv9.update(self.conv8_output_array, self.conv10.delta_array)
self.conv12.update(self.conv10_output_array, delta_array)
def output(self, input_array):
self.conv1.forward(input_array)
self.conv3.forward(self.conv1.output_array)
self.conv4.forward(self.conv3.output_array)
self.conv5.forward(self.conv4.output_array)
self.conv6.forward(self.conv5.output_array)
self.conv8.forward(self.conv6.output_array)
self.conv9.forward(self.conv8.output_array)
self.conv10.forward(self.conv9.output_array)
self.conv12.forward(self.conv10_output_array)
return self.conv12.output_array
def save(self, path):
fo = open(path, "w")
for filter in self.conv1.filters:
for i in range(0, filter.weights.shape[0]):
for j in range(0, filter.weights.shape[1]):
for k in range(0, filter.weights.shape[2]):
fo.write(str(filter.weights[i, j, k]) + ' ')
fo.write(str(filter.bias) + '\n')
for filter in self.conv4.filters:
for i in range(0, filter.weights.shape[0]):
for j in range(0, filter.weights.shape[1]):
for k in range(0, filter.weights.shape[2]):
fo.write(str(filter.weights[i, j, k]) + ' ')
fo.write(str(filter.bias) + '\n')
for filter in self.conv6.filters:
for i in range(0, filter.weights.shape[0]):
for j in range(0, filter.weights.shape[1]):
for k in range(0, filter.weights.shape[2]):
fo.write(str(filter.weights[i, j, k]) + ' ')
fo.write(str(filter.bias) + '\n')
for filter in self.conv9.filters:
for i in range(0, filter.weights.shape[0]):
for j in range(0, filter.weights.shape[1]):
for k in range(0, filter.weights.shape[2]):
fo.write(str(filter.weights[i, j, k]) + ' ')
fo.write(str(filter.bias) + '\n')
for filter in self.conv12.filters:
for i in range(0, filter.weights.shape[0]):
for j in range(0, filter.weights.shape[1]):
for k in range(0, filter.weights.shape[2]):
fo.write(str(filter.weights[i, j, k]) + ' ')
fo.write(str(filter.bias) + '\n')
fo.close()
def load(self, path):
fi = open(path, 'r')
data = fi.readlines()
for l in range(0, 16):
para_list = data[l].split()
for i in range(0, self.conv1.filters[l].weights.shape[0]):
for j in range(0, self.conv1.filters[l].weights.shape[1]):
for k in range(0, self.conv1.filters[l].weights.shape[2]):
self.conv1.filters[l].weights[i, j, k] = float(
para_list[i * 9 + j * 3 + k])
self.conv1.filters[l].bias = float(para_list[-1])
for l in range(16, 48):
para_list = data[l].split()
for i in range(0, self.conv4.filters[l - 16].weights.shape[0]):
for j in range(0, self.conv4.filters[l - 16].weights.shape[1]):
for k in range(0, self.conv4.filters[l -
16].weights.shape[2]):
self.conv4.filters[l - 16].weights[i, j, k] = float(
para_list[i * 9 + j * 3 + k])
self.conv4.filters[l - 16].bias = float(para_list[-1])
for l in range(48, 80):
para_list = data[l].split()
for i in range(0, self.conv6.filters[l - 16].weights.shape[0]):
for j in range(0, self.conv6.filters[l - 16].weights.shape[1]):
for k in range(0, self.conv6.filters[l -
16].weights.shape[2]):
self.conv6.filters[l - 16].weights[i, j, k] = float(
para_list[i * 9 + j * 3 + k])
self.conv6.filters[l - 16].bias = float(para_list[-1])
for l in range(80, 96):
para_list = data[l].split()
for i in range(0, self.conv9.filters[l - 80].weights.shape[0]):
for j in range(0, self.conv9.filters[l - 80].weights.shape[1]):
for k in range(0, self.conv9.filters[l -
80].weights.shape[2]):
self.conv9.filters[l - 80].weights[i, j, k] = float(
para_list[i * 9 + j * 3 + k])
self.conv9.filters[l - 80].bias = float(para_list[-1])
for l in range(96, 98):
para_list = data[l].split()
for i in range(0, self.conv12.filters[l - 96].weights.shape[0]):
for j in range(0,
self.conv12.filters[l - 96].weights.shape[1]):
for k in range(
0, self.conv12.filters[l - 96].weights.shape[2]):
self.conv12.filters[l - 96].weights[i, j, k] = float(
para_list[i * 9 + j * 3 + k])
self.conv12.filters[l - 96].bias = float(para_list[-1])