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])