def infererence(args): groups = 8 print('Loading image') image = Image.open(args.image) print('Preprocessing') transformer = get_transformer() input_data = preprocess(image, transformer) print('input_data ', input_data.shape) #conv layer w = np.load('./data/' + 'module.conv1.weight.npy') b = np.load('./data/' + 'module.conv1.bias.npy') conv_layer = Convolution(w, b, stride=2, pad=1) out = conv_layer.forward(input_data) #savetxt('./dump/' + 'conv1_out.txt', out) #max pooling maxpool_layer = Pooling(3, 3, 2, 1) out = maxpool_layer.forward(out) #savetxt('./dump/' + 'maxpool_out.txt', out) out = stage_shuffle(out, stage2_str, 3, groups) #savetxt('./dump/' + 'stage2.txt', out) out = stage_shuffle(out, stage3_str, 7, groups) #savetxt('./dump/' + 'stage3.txt', out) out = stage_shuffle(out, stage4_str, 3, groups) #savetxt('./dump/' + 'stage4.txt', out) h, w = out.shape[-2:] avgpool_layer = AVGPooling(h, w, 1, 0) out = avgpool_layer.forward(out).reshape(1, -1) w = np.load('./data/' + 'module.fc.weight.npy') b = np.load('./data/' + 'module.fc.bias.npy') w = w.transpose(1, 0) fc_layer = Affine(w, b) out = fc_layer.forward(out) softmax_layer = Softmax() out = softmax_layer.forward(out).reshape(-1) result = [] with open(args.idx_to_class) as json_file: json_data = json.load(json_file) ''' for key in json_data: print(key, json_data[key]) ''' for i in range(0, 1000): item = (out[i], json_data[str(i)]) result.append(item) result = sorted(result, key=lambda item: item[0], reverse=True) for i in range(0, 10): print(result[i])
def __init__(self, dim_in=(1, 28, 28), par={'num_filter': 30, 'size_filter': 5, 'pad': 0, 'stride': 1}, s_hidden=100, s_out=10, std_w_init=0.01): n_f = par['num_filter'] s_f = par['size_filter'] pad = par['pad'] stride = par['stride'] size_in = dim_in[1] size_out_conv = int((size_in + 2 * pad - s_f) / stride) + 1 size_out_pool = int(n_f * (size_out_conv / 2) ** 2) self.params = {} self.params['W1'] =\ std_w_init * np.random.randn(n_f, dim_in[0], s_f, s_f) self.params['b1'] = np.zeros(n_f) self.params['W2'] = std_w_init * np.random.randn(size_out_pool, s_hidden) self.params['b2'] = np.zeros(s_hidden) self.params['W3'] = std_w_init * np.random.randn(s_hidden, s_out) self.params['b3'] = np.zeros(s_out) self.layers = OrderedDict() self.layers['Conv'] = Convolution(self.params['W1'], self.params['b1'], stride, pad) self.layers['Relu1'] = Relu() self.layers['Pool'] = Pooling(2, 2, 2) self.layers['Affine1'] = Affine(self.params['W2'], self.params['b2']) self.layers['Relu'] = Relu() self.layers['Affine2'] = Affine(self.params['W3'], self.params['b3']) self.last_layer = SoftmaxWithLoss()
def __init__(self, input_dim=(1, 28, 28), conv_param=None, hidden_size=100, output_size=10, weight_init_std=0.01, regularizer_lambda=0.1): # 卷积层的默认参数:默认情况下滤波器个数为30个,大小为5x5,不填充,步长1 if conv_param is None: conv_param = {'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1} filter_num = conv_param['filter_num'] filter_size = conv_param['filter_size'] filter_pad = conv_param['pad'] filter_stride = conv_param['stride'] input_size = input_dim[1] # 输入层的矩阵大小:单通道下二维矩阵的宽/高 conv_output_size = int((input_size + 2 * filter_pad - filter_size) / filter_stride + 1) # 卷积层输出的单个特征图的大小 # 最大池化层的输出大小:池化后保持特征图个数不变,由于使用的是2x2的最大 # 池化层,因此宽/高都变为原来的一半。 # 总的输出元素个数为:特征图个数 * (卷积层输出 / 2) * (卷积层输出 / 2) # 因为这里的简单CNN中池化层后面接全连接层, # 需要将池化层的节点拉平成一个一维数组 pool_output_size = int(filter_num * (conv_output_size / 2) ** 2) self.regularizer_lambda = regularizer_lambda # 正则化强度 # 初始化神经网络各层的参数:卷积层、(池化层)、全连接层、全连接层 # 其中池化层没有需要训练的参数,因此不需要初始化。 self.params = {} # 第一层(卷积层):滤波器的参数(权重参数) + 偏置参数 # 滤波器的参数有4个:滤波器个数、通道数、高、宽 self.params['W1'] = weight_init_std * np.random.randn(filter_num, input_dim[0], filter_size, filter_size) # 卷积层的偏置参数:一个滤波器需要一个偏置,因此一共filter_num个偏置 self.params['b1'] = np.zeros(filter_num) # 全连接层(在这里是一个隐藏层)权重参数: # 输入节点数为池化层的所有节点个数,输出为隐藏层大小 self.params['W2'] = weight_init_std * np.random.randn(pool_output_size, hidden_size) self.params['b2'] = np.zeros(hidden_size) # 全连接层(在这里是输出层)权重参数: # 输入节点数为隐藏层的所有节点个数,输出为输出层大小 self.params['W3'] = weight_init_std * np.random.randn(hidden_size, output_size) self.params['b3'] = np.zeros(output_size) # 构造神经网络: # 卷积层、激活层(ReLU层)、最大池化层、 # 仿射层(隐藏层)、激活层(ReLU层)、仿射层(输出层) self.layers = OrderedDict() self.layers['Conv1'] = Convolution(self.params['W1'], self.params['b1'], conv_param['stride'], conv_param['pad']) self.layers['ReLU1'] = ReLU() self.layers['Pool1'] = Pooling(pool_h=2, pool_w=2, stride=2) self.layers['Affine1'] = Affine(self.params['W2'], self.params['b2']) self.layers['ReLU2'] = ReLU() self.layers['Affine2'] = Affine(self.params['W3'], self.params['b3']) # 最后加入一层SoftmaxWithLoss层用于计算交叉熵误差,帮助训练神经网络 self.last_layer = SoftmaxWithLoss()
def __init__(self, input_dim=(1, 28, 28), conv_param={'filter_num:': 30, 'filter_size': 5, 'pad': 0, 'stride': 1}, hidden_size=100, output_size=10, weight_init_std=0.01): """인스턴스 초기화 - CNN 구성, 변수들 초기화 input_dim: 입력 데이터 차원. MNIST인 경우 (1, 28, 28) conv_param: Convolution 레이어의 파라미터(filter, bias)를 생성하기 위해 필요한 값들 필터 개수(filter_num), 필터 크기(filter_size = filter_height = filter_width), 패딩 개수(pad), 보폭(stride) hidden_size: Affine 계층에서 사용할 뉴런의 개수 -> W 행렬의 크기 output_size: 출력값의 원소의 개수. MNIST인 경우 10 weight_init_std: 가중치(weight) 행렬을 난수로 초기화할 때 사용할 표준편차 """ # CNN layer(계층) 생성, 연결 self.layers = OrderedDict() self.layers['Conv1'] = Convolution(W1, b1) # CNN layer에서 필요한 파라미터들 self.params = dict() self.params['W1'] = W1 self.params['b1'] = b1
def __init__(self, input_dim=(1, 28, 28), conv_param={'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1}, hidden_size=100, output_size=10, weight_init_std=0.01): filter_num = conv_param['filter_num'] filter_size = conv_param['filter_size'] filter_pad = conv_param['pad'] filter_stride = conv_param['stride'] input_size = input_dim[1] conv_output_size = (input_size - filter_size + 2 * filter_pad) / filter_stride + 1 pool_output_size = int(filter_num * (conv_output_size / 2) * (conv_output_size / 2)) # 初始化权重 self.params = {'W1': weight_init_std * \ np.random.randn(filter_num, input_dim[0], filter_size, filter_size), 'b1': np.zeros(filter_num), 'W2': weight_init_std * \ np.random.randn(pool_output_size, hidden_size), 'b2': np.zeros(hidden_size), 'W3': weight_init_std * \ np.random.randn(hidden_size, output_size), 'b3': np.zeros(output_size)} # 生成层 self.layers = OrderedDict() self.layers['Conv1'] = Convolution(self.params['W1'], self.params['b1'], conv_param['stride'], conv_param['pad']) self.layers['Relu1'] = ReLU() self.layers['Pool1'] = Pooling(pool_h=2, pool_w=2, stride=2) self.layers['Affine1'] = Affine(self.params['W2'], self.params['b2']) self.layers['Relu2'] = ReLU() self.layers['Affine2'] = Affine(self.params['W3'], self.params['b3']) self.last_layer = SoftmaxWithLoss()
def __init__(self, input_dim = (1, 28, 28), conv_params = {'filter_num':30,'filter_size': 5, 'pad': 0, 'stride':1}, hidden_size = 100, output_size = 10, weight_init_std = 0.01): """ 인스턴스 초기화 (변수들의 초기값을 줌) - CNN 구성, 변수들 초기화 input_dim: 입력 데이터 차원, MINIST인 경우(1, 28, 28) conv_param: Convolution 레이어의 파라미터(filter, bias)를 생성하기 위해 필요한 값들 필터 개수 (filter_num), 필터 크기(filter_size = filter_height = filter_width), 패딩 개수(pad), 보폭(stride) hidden_size: Affine 계층에서 사용할 뉴런의 개수 -> W 행렬의 크기 output_size: 출력값의 원소의 개수. MNIST인 경우 10 weight_init_std: 가중치(weight) 행렬을 난수로 초기화 할 때 사용할 표준편차 """ filter_num = conv_params['filter_num'] filter_size = conv_params['filter_size'] filter_pad = conv_params['pad'] filter_stride = conv_params['stride'] input_size = input_dim[1] conv_output_size = (input_size - filter_size + 2 * filter_pad) / \ filter_stride + 1 pool_output_size = int(filter_num * (conv_output_size / 2) * (conv_output_size / 2)) # CNN Layer에서 필요한 파라미터들 self.params = dict() self.params['W1'] = weight_init_std * np.random.randn(filter_num, input_dim[0], filter_size, filter_size) self.params['b1'] = np.zeros(filter_num) self.params['W2'] = weight_init_std * np.random.randn(pool_output_size, hidden_size) self.params['b2'] = np.zeros(hidden_size) self.params['W'] = weight_init_std * np.random.randn(hidden_size, output_size) self.params['b3'] = np.zeros(output_size) # CNN Layer(계층) 생성, 연결 self.layers = OrderedDict() # 방법 1 __init__(self,W,b) 라고 주고, self.W = W, self.b = b 를 선언 # self.W = W # 난수로 생성하려고 해도 데이터의 크기(size)를 알아야 필터를 생성할 수 있다 # self.b = b # bias의 크기는 필터의 크기와 같다. 마찬가지로 난수로 생성해도 크기를 알아야한다 => dimension 결정 # 방법 2 # input_dim = (1, 28, 28) = MNIST를 위한 클래스 # dimension을 주도록 설정 + 필터갯수가 있도록 설정해줘야한다 # convolution 할 때 필터를 몇번 만들 것인가 -> 난수로 만들어서 넣어줄 수 있다 # key값 self.layers['Conv1'] = Convolution(self.params['W1'], self.params['b1'], conv_params['stride'], conv_params['pad']) # W와 b를 선언 self.layers['ReLu1'] = Relu() # x -> Convolution에서 전해주는 값 self.layers['Pool1'] = Pooling(pool_h = 2, pool_w =2, stride =2) self.layers['Affine1'] = Affine(self.params['W2'], self.params['b2']) self.layers['Relu2'] = Relu() self.layers['Affine2'] = Affine(self.params['W3'], self.params['b3']) self.last_layer = SoftmaxWithLoss()
def __init__(self, input_dim=(1, 28, 28), conv_param={ 'filter_num:': 30, 'filter_size': 5, 'pad': 0, 'stride': 1 }, hidden_size=100, output_size=10, weight_init_std=0.01): ''' 인스턴스 초기화 - CNN 구성, 변수들 초기화 input_dim: 입력 데이터 차원. MNIST인 경우 (1, 28, 28) conv_param: Convolution 레이어의 파라미터(filter, bias)를 생성하기 위해 필요한 값들 필터 개수(filter_num), 필터 크기(filter_size = filter_height = filter_width), 패딩 개수(pad), 보폭(stride) hidden_size: Affine 계층에서 사용할 뉴런의 개수 output_size: 출력값의 원소의 개수. MNIST인 경우 10 weight_init_std: 가중치(weight) 행렬을 난수로 초기화할 때 사용할 표준편차 ''' c, h, w = input_dim fn, fh, fw = conv_param['filter_num'], conv_param[ 'filter_size'], conv_param['filter_size'] s, p = conv_param['stride'], conv_param['pad'] oh = (h - fh + 2 * p) // s + 1 ow = (w - fw + 2 * p) // s + 1 W1 = np.random.randn(fn, c, fh, fw) b1 = np.zeros(shape=(fn, )) # CNN layer(계층) 생성, 연결 self.layers = OrderedDict() self.layers['Conv1'] = Convolution(W1, b1) # CNN layer 에서 필요한 파라미터들 self.params = dict() self.params['W1'] = W1 self.params['b1'] = b1 def predict(self): pass def loss(self): pass def accuracy(self): pass def gradient(self): pass
def make_layers(self): # レイヤの生成 self.layers = OrderedDict() self.layers['Conv1'] = Convolution( self.params['W1'], self.params['b1'], 1, 0) # W1が畳み込みフィルタの重み, b1が畳み込みフィルタのバイアスになる self.layers['ReLU1'] = ReLU() self.layers['Pool1'] = MaxPooling(pool_h=2, pool_w=2, stride=2) self.layers['Affine1'] = Affine(self.params['W2'], self.params['b2']) self.layers['ReLU2'] = ReLU() self.layers['Affine2'] = Affine(self.params['W3'], self.params['b3']) self.last_layer = SoftmaxWithLoss()
def __init__(self, input_dim=(1, 28, 28), conv_param_1={'filter_num': 16, 'filter_size': 3, 'pad': 1, 'stride': 1}, conv_param_2={'filter_num': 16, 'filter_size': 3, 'pad': 1, 'stride': 1}, conv_param_3={'filter_num': 32, 'filter_size': 3, 'pad': 1, 'stride': 1}, conv_param_4={'filter_num': 32, 'filter_size': 3, 'pad': 2, 'stride': 1}, conv_param_5={'filter_num': 64, 'filter_size': 3, 'pad': 1, 'stride': 1}, conv_param_6={'filter_num': 64, 'filter_size': 3, 'pad': 1, 'stride': 1}, hidden_size=50, output_size=10): pre_node_nums = np.array([1*3*3, 16*3*3, 16*3*3, 32*3*3, 32*3*3, 64*3*3, 64*4*4, hidden_size]) weight_init_scale = np.sqrt(2.0 / pre_node_nums) # weights init self.params = {} pre_channel_num = input_dim[0] for idx, conv_param in enumerate([conv_param_1, conv_param_2, conv_param_3, conv_param_4, conv_param_5, conv_param_6]): self.params['w'+str(idx+1)] = weight_init_scale[idx] *\ np.random.randn( conv_param['filter_num'], pre_channel_num, conv_param['filter_size'], conv_param['filter_size']) self.params['b'+str(idx+1)] = np.zeros(conv_param['filter_num']) pre_channel_num = conv_param['filter_num'] self.params['w7'] = weight_init_scale[6] * np.random.randn(64*4*4, hidden_size) self.params['b7'] = np.zeros(hidden_size) self.params['w8'] = weight_init_scale[7] * np.random.randn(hidden_size, output_size) self.params['b8'] = np.zeros(output_size) # gen layers self.layers = [] self.layers.append(Convolution(self.params['w1'], self.params['b1'], conv_param_1['stride'], conv_param_1['pad'])) self.layers.append(Relu()) self.layers.append(Convolution(self.params['w2'], self.params['b2'], conv_param_2['stride'], conv_param_2['pad'])) self.layers.append(Relu()) self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) self.layers.append(Convolution(self.params['w3'], self.params['b3'], conv_param_3['stride'], conv_param_3['pad'])) self.layers.append(Relu()) self.layers.append(Convolution(self.params['w4'], self.params['b4'], conv_param_4['stride'], conv_param_4['pad'])) self.layers.append(Relu()) self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) self.layers.append(Convolution(self.params['w5'], self.params['b5'], conv_param_5['stride'], conv_param_5['pad'])) self.layers.append(Relu()) self.layers.append(Convolution(self.params['w6'], self.params['b6'], conv_param_6['stride'], conv_param_6['pad'])) self.layers.append(Relu()) self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) self.layers.append(Affine(self.params['w7'], self.params['b7'])) self.layers.append(Relu()) self.layers.append(Dropout(0.5)) self.layers.append(Affine(self.params['w8'], self.params['b8'])) self.layers.append(Dropout(0.5)) self.last_layer = SoftmaxWithLoss()
def __init__(self, input_dim=(1, 28, 28), conv_param_1 = {'filter_num':16, 'filter_size':3, 'pad':1, 'stride':1}, conv_param_2 = {'filter_num':16, 'filter_size':3, 'pad':1, 'stride':1}, conv_param_3 = {'filter_num':32, 'filter_size':3, 'pad':1, 'stride':1}, conv_param_4 = {'filter_num':32, 'filter_size':3, 'pad':2, 'stride':1}, conv_param_5 = {'filter_num':64, 'filter_size':3, 'pad':1, 'stride':1}, conv_param_6 = {'filter_num':64, 'filter_size':3, 'pad':1, 'stride':1}, hidden_size=50, output_size=10): # 重みの初期化=========== # 各層のニューロンひとつあたりが、前層のニューロンといくつのつながりがあるか(TODO:自動で計算する) pre_node_nums = np.array([1*3*3, 16*3*3, 16*3*3, 32*3*3, 32*3*3, 64*3*3, 64*4*4, hidden_size]) weight_init_scales = np.sqrt(2.0 / pre_node_nums) # ReLUを使う場合に推奨される初期値 self.params = {} pre_channel_num = input_dim[0] for idx, conv_param in enumerate([conv_param_1, conv_param_2, conv_param_3, conv_param_4, conv_param_5, conv_param_6]): self.params['W' + str(idx+1)] = weight_init_scales[idx] * np.random.randn(conv_param['filter_num'], pre_channel_num, conv_param['filter_size'], conv_param['filter_size']) self.params['b' + str(idx+1)] = np.zeros(conv_param['filter_num']) pre_channel_num = conv_param['filter_num'] self.params['W7'] = weight_init_scales[6] * np.random.randn(64*4*4, hidden_size) self.params['b7'] = np.zeros(hidden_size) self.params['W8'] = weight_init_scales[7] * np.random.randn(hidden_size, output_size) self.params['b8'] = np.zeros(output_size) # レイヤの生成=========== self.layers = [] self.layers.append(Convolution(self.params['W1'], self.params['b1'], conv_param_1['stride'], conv_param_1['pad'])) self.layers.append(Relu()) self.layers.append(Convolution(self.params['W2'], self.params['b2'], conv_param_2['stride'], conv_param_2['pad'])) self.layers.append(Relu()) self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) self.layers.append(Convolution(self.params['W3'], self.params['b3'], conv_param_3['stride'], conv_param_3['pad'])) self.layers.append(Relu()) self.layers.append(Convolution(self.params['W4'], self.params['b4'], conv_param_4['stride'], conv_param_4['pad'])) self.layers.append(Relu()) self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) self.layers.append(Convolution(self.params['W5'], self.params['b5'], conv_param_5['stride'], conv_param_5['pad'])) self.layers.append(Relu()) self.layers.append(Convolution(self.params['W6'], self.params['b6'], conv_param_6['stride'], conv_param_6['pad'])) self.layers.append(Relu()) self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) self.layers.append(Affine(self.params['W7'], self.params['b7'])) self.layers.append(Relu()) self.layers.append(Dropout(0.5)) self.layers.append(Affine(self.params['W8'], self.params['b8'])) self.layers.append(Dropout(0.5)) self.last_layer = SoftmaxWithLoss()
def __init__(self, input_dim=(1, 28, 28), conv_param={ 'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1 }, hidden_size=100, output_size=10, weight_init_std=0.01): filter_num = conv_param['filter_num'] filter_size = conv_param['filter_size'] filter_pad = conv_param['pad'] filter_stride = conv_param['stride'] input_size = input_dim[1] conv_output_size = (input_size - filter_size + 2 * filter_pad) / filter_stride + 1 pool_output_size = int(filter_num * (conv_output_size / 2) * (conv_output_size / 2)) """ 인스턴스 초기화 - CNN 구성, 변수들 초기화""" # CNN layer에서 필요한 파라미터들 self.params = dict() self.params['W1'] = weight_init_std * \ np.random.randn(filter_num, input_dim[0], filter_size, filter_size) self.params['b1'] = np.zeros(filter_num) self.params['W2'] = weight_init_std * \ np.random.randn(pool_output_size, hidden_size) self.params['b2'] = np.zeros(hidden_size) self.params['W3'] = weight_init_std * \ np.random.randn(hidden_size, output_size) self.params['b3'] = np.zeros(output_size) # CNN layer(계층) 생성, 연결 self.layers = OrderedDict() self.layers['Conv1'] = Convolution(self.params['W1'], self.params['b1'], conv_param['stride'], conv_param['pad']) self.layers['Relu1'] = Relu() self.layers['Pool1'] = Pooling(pool_h=2, pool_w=2, stride=2) self.layers['Relu2'] = Relu() self.layers['Affine2'] = Affine(self.params['W3'], self.params['b3']) self.last_layer = SoftmaxWithLoss()
def stage_shuffle(input_data, stage, repeat_num, groups): avgpool_layer = AVGPooling(3, 3, 2, 1) residual = avgpool_layer.forward(input_data) #savetxt('./dump/' + 'avg_pool.txt', residual) w = np.load(stage + '0.g_conv_1x1_compress.conv1x1.weight.npy') b = np.load(stage + '0.g_conv_1x1_compress.conv1x1.bias.npy') if 'Stage2' in stage: conv_layer = Convolution(w, b, stride=1, pad=0) else: conv_layer = GroupConvolution(w, b, stride=1, pad=0, groups=groups) out = conv_layer.forward(input_data) out_N, out_C, out_H, out_W = out.shape gamma = np.load(stage + '0.g_conv_1x1_compress.batch_norm.weight.npy').reshape( (-1, 1)) beta = np.load(stage + '0.g_conv_1x1_compress.batch_norm.bias.npy').reshape( (-1, 1)) mean = np.load( stage + '0.g_conv_1x1_compress.batch_norm.running_mean.npy').reshape( (-1, 1)) var = np.load(stage + '0.g_conv_1x1_compress.batch_norm.running_var.npy').reshape( (-1, 1)) bn_layer = BatchNormalization(gamma, beta, running_mean=mean, running_var=var) out = bn_layer.forward(out.reshape(out_C, -1), train_flg=False) relu_layer = Relu() out = relu_layer.forward(out).reshape(out_N, out_C, out_H, out_W) #savetxt('./dump/' + '1x1_comp.txt', out) out = channel_shuffle(out, groups) #savetxt('./dump/' + 'channel_shuffle.txt', out) w = np.load(stage + '0.depthwise_conv3x3.weight.npy').transpose(1, 0, 2, 3) b = np.load(stage + '0.depthwise_conv3x3.bias.npy') dwconv_layer = DWConvolution(w, b, stride=2, pad=1) out = dwconv_layer.forward(out) #savetxt('./dump/' + 'dwconv.txt', out) gamma = np.load(stage + '0.bn_after_depthwise.weight.npy').reshape((-1, 1)) beta = np.load(stage + '0.bn_after_depthwise.bias.npy').reshape((-1, 1)) mean = np.load(stage + '0.bn_after_depthwise.running_mean.npy').reshape( (-1, 1)) var = np.load(stage + '0.bn_after_depthwise.running_var.npy').reshape( (-1, 1)) bn_layer = BatchNormalization(gamma, beta, running_mean=mean, running_var=var) out_N, out_C, out_H, out_W = out.shape out = bn_layer.forward(out.reshape(out_C, -1), train_flg=False).reshape(out_N, out_C, out_H, out_W) #savetxt('./dump/' + 'after_bn.txt', out) w = np.load(stage + '0.g_conv_1x1_expand.conv1x1.weight.npy') b = np.load(stage + '0.g_conv_1x1_expand.conv1x1.bias.npy') groupconv_layer = GroupConvolution(w, b, stride=1, pad=0, groups=groups) out = groupconv_layer.forward(out) gamma = np.load(stage + '0.g_conv_1x1_expand.batch_norm.weight.npy').reshape( (-1, 1)) beta = np.load(stage + '0.g_conv_1x1_expand.batch_norm.bias.npy').reshape( (-1, 1)) mean = np.load(stage + '0.g_conv_1x1_expand.batch_norm.running_mean.npy').reshape( (-1, 1)) var = np.load(stage + '0.g_conv_1x1_expand.batch_norm.running_var.npy').reshape( (-1, 1)) bn_layer = BatchNormalization(gamma, beta, running_mean=mean, running_var=var) out_N, out_C, out_H, out_W = out.shape out = bn_layer.forward(out.reshape(out_C, -1), train_flg=False).reshape(out_N, out_C, out_H, out_W) #savetxt('./dump/' + 'gconv.txt', out) out = np.concatenate((residual, out), 1) #savetxt('./dump/' + 'combine.txt', out) relu_layer = Relu() out_N, out_C, out_H, out_W = out.shape out = relu_layer.forward(out).reshape(out_N, out_C, out_H, out_W) #savetxt('./dump/' + 'stage2.txt', out) for i in range(1, repeat_num + 1): residual = out w = np.load(stage + str(i) + '.g_conv_1x1_compress.conv1x1.weight.npy') b = np.load(stage + str(i) + '.g_conv_1x1_compress.conv1x1.bias.npy') groupconv_layer = GroupConvolution(w, b, stride=1, pad=0, groups=groups) out = groupconv_layer.forward(out) out_N, out_C, out_H, out_W = out.shape gamma = np.load(stage + str(i) + '.g_conv_1x1_compress.batch_norm.weight.npy').reshape( (-1, 1)) beta = np.load(stage + str(i) + '.g_conv_1x1_compress.batch_norm.bias.npy').reshape( (-1, 1)) mean = np.load( stage + str(i) + '.g_conv_1x1_compress.batch_norm.running_mean.npy').reshape( (-1, 1)) var = np.load( stage + str(i) + '.g_conv_1x1_compress.batch_norm.running_var.npy').reshape((-1, 1)) bn_layer = BatchNormalization(gamma, beta, running_mean=mean, running_var=var) out = bn_layer.forward(out.reshape(out_C, -1), train_flg=False) relu_layer = Relu() out = relu_layer.forward(out).reshape(out_N, out_C, out_H, out_W) #savetxt('./dump/' + str(i) + '_1x1_comp.txt', out) out = channel_shuffle(out, groups) #savetxt('./dump/' + 'channel_shuffle.txt', out) w = np.load(stage + str(i) + '.depthwise_conv3x3.weight.npy').transpose(1, 0, 2, 3) b = np.load(stage + str(i) + '.depthwise_conv3x3.bias.npy') dwconv_layer = DWConvolution(w, b, stride=1, pad=1) out = dwconv_layer.forward(out) #savetxt('./dump/' + 'dwconv.txt', out) gamma = np.load(stage + str(i) + '.bn_after_depthwise.weight.npy').reshape((-1, 1)) beta = np.load(stage + str(i) + '.bn_after_depthwise.bias.npy').reshape((-1, 1)) mean = np.load(stage + str(i) + '.bn_after_depthwise.running_mean.npy').reshape((-1, 1)) var = np.load(stage + str(i) + '.bn_after_depthwise.running_var.npy').reshape((-1, 1)) bn_layer = BatchNormalization(gamma, beta, running_mean=mean, running_var=var) out_N, out_C, out_H, out_W = out.shape out = bn_layer.forward(out.reshape(out_C, -1), train_flg=False).reshape( out_N, out_C, out_H, out_W) #savetxt('./dump/' + 'after_bn.txt', out) w = np.load(stage + str(i) + '.g_conv_1x1_expand.conv1x1.weight.npy') b = np.load(stage + str(i) + '.g_conv_1x1_expand.conv1x1.bias.npy') groupconv_layer = GroupConvolution(w, b, stride=1, pad=0, groups=groups) out = groupconv_layer.forward(out) gamma = np.load(stage + str(i) + '.g_conv_1x1_expand.batch_norm.weight.npy').reshape( (-1, 1)) beta = np.load(stage + str(i) + '.g_conv_1x1_expand.batch_norm.bias.npy').reshape( (-1, 1)) mean = np.load( stage + str(i) + '.g_conv_1x1_expand.batch_norm.running_mean.npy').reshape((-1, 1)) var = np.load(stage + str(i) + '.g_conv_1x1_expand.batch_norm.running_var.npy').reshape( (-1, 1)) bn_layer = BatchNormalization(gamma, beta, running_mean=mean, running_var=var) out_N, out_C, out_H, out_W = out.shape out = bn_layer.forward(out.reshape(out_C, -1), train_flg=False).reshape( out_N, out_C, out_H, out_W) #savetxt('./dump/' + 'gconv.txt', out) out = np.add(residual, out) #savetxt('./dump/' + str(i) + '_combine.txt', out) relu_layer = Relu() out_N, out_C, out_H, out_W = out.shape out = relu_layer.forward(out).reshape(out_N, out_C, out_H, out_W) #savetxt('./dump/' + str(i) + '_stage.txt', out) return out
network = SimpleConvNet(input_dim=(1,28,28), conv_param = {'filter_num':30, 'filter_size':5, 'pad':0, 'stride':1}, hidden_size=100, output_size=10, weight_init_std=0.01) # 学習後の重み network.load_params("params.pkl") filter_show(network.params['W1'], 16) img = imread('../dataset/lena_gray.png') img = img.reshape(1, 1, *img.shape) fig = plt.figure() w_idx = 1 for i in range(16): w = network.params['W1'][i] b = 0 # network.params['b1'][i] w = w.reshape(1, *w.shape) #b = b.reshape(1, *b.shape) conv_layer = Convolution(w, b) out = conv_layer.forward(img) out = out.reshape(out.shape[2], out.shape[3]) ax = fig.add_subplot(4, 4, i+1, xticks=[], yticks=[]) ax.imshow(out, cmap=plt.cm.gray_r, interpolation='nearest') plt.show()
}, hidden_size=100, output_size=10, weight_init_std=0.01) # 学习后的权重 network.load_params("params.pkl") filter_show(network.params['W1'], 16) img = imread('../dataset/lena_gray.png') img = img.reshape(1, 1, *img.shape) fig = plt.figure() w_idx = 1 for i in range(16): w = network.params['W1'][i] b = 0 # network.params['b1'][i] w = w.reshape(1, *w.shape) #b = b.reshape(1, *b.shape) conv_layer = Convolution(w, b) out = conv_layer.forward(img) out = out.reshape(out.shape[2], out.shape[3]) ax = fig.add_subplot(4, 4, i + 1, xticks=[], yticks=[]) ax.imshow(out, cmap=plt.cm.gray_r, interpolation='nearest') plt.show()
def __init__(self, input_dim=(1, 28, 28), conv_param={ 'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1 }, hidden_size=100, output_size=10, weight_init_std=0.01, weight_decay_lambda=0.01): """ input_size : 入力の配列形状(チャンネル数、画像の高さ、画像の幅) conv_param : 畳み込みの条件, dict形式 例、{'filter_num':30, 'filter_size':5, 'pad':0, 'stride':1} hidden_size : 隠れ層のノード数 output_size : 出力層のノード数 weight_init_std : 重みWを初期化する際に用いる標準偏差 """ self.hidden_layer_num = 3 self.weight_decay_lambda = weight_decay_lambda #filter_num = conv_param['filter_num'] #filter_size = conv_param['filter_size'] #filter_pad = conv_param['pad'] #filter_stride = conv_param['stride'] filter_num = 30 filter_size = 5 filter_pad = 0 filter_stride = 1 input_size = input_dim[1] conv_output_size = (input_size - filter_size + 2 * filter_pad) / filter_stride + 1 pool_output_size = int(filter_num * (conv_output_size / 2) * (conv_output_size / 2)) # 重みの初期化 self.params = {} std = weight_init_std self.params['W1'] = std * np.random.randn( filter_num, input_dim[0], filter_size, filter_size) # W1は畳み込みフィルターの重みになる self.params['b1'] = np.zeros(filter_num) #b1は畳み込みフィルターのバイアスになる #self.params['W2'] = std * np.random.randn(pool_output_size, hidden_size) self.params['b2'] = np.zeros(hidden_size) #self.params['W3'] = std * np.random.randn(hidden_size, output_size) self.params['b3'] = np.zeros(output_size) #Heの初期値を使用 self.params['W2'] = np.random.randn(pool_output_size, hidden_size) * he(pool_output_size) self.params['W3'] = np.random.randn(hidden_size, output_size) * he(hidden_size) # レイヤの生成 self.layers = OrderedDict() #self.layers['Conv1'] = Convolution(self.params['W1'], self.params['b1'], # conv_param['stride'], conv_param['pad']) # W1が畳み込みフィルターの重み, b1が畳み込みフィルターのバイアスになる self.layers['Conv1'] = Convolution( self.params['W1'], self.params['b1'], 1, 0) # W1が畳み込みフィルターの重み, b1が畳み込みフィルターのバイアスになる self.layers['ReLU1'] = ReLU() self.layers['Pool1'] = MaxPooling(pool_h=2, pool_w=2, stride=2) self.layers['Affine1'] = Affine(self.params['W2'], self.params['b2']) self.layers['ReLU2'] = ReLU() self.layers['Affine2'] = Affine(self.params['W3'], self.params['b3']) self.last_layer = SoftmaxWithLoss()
def __init__(self, input_dim=(1, 28, 28), conv_param_1=None, conv_param_2=None, conv_param_3=None, conv_param_4=None, conv_param_5=None, conv_param_6=None, hidden_size=50, output_size=10): # 第一个卷积层输入1x28x28,输出16x28x28 if conv_param_1 is None: conv_param_1 = { 'filter_num': 16, 'filter_size': 3, 'pad': 1, 'stride': 1 } # 第二个卷积层输入16x28x28,输出16x28x28 if conv_param_2 is None: conv_param_2 = { 'filter_num': 16, 'filter_size': 3, 'pad': 1, 'stride': 1 } # 第二个卷积层之后接最大池化层,池化层大小为2x2,步长为2,即高、宽减半 # 第三个卷积层输入16x14x14,输出32x14x14 if conv_param_3 is None: conv_param_3 = { 'filter_num': 32, 'filter_size': 3, 'pad': 1, 'stride': 1 } # 第四个卷积层输入32x14x14,但由于pad2个,因此输出32x16x16 if conv_param_4 is None: conv_param_4 = { 'filter_num': 32, 'filter_size': 3, 'pad': 2, 'stride': 1 } # 第四个卷积层之后接最大池化层,池化层大小为2x2,步长为2,即高、宽减半 # 第五个卷积层输入32x8x8,输出64x8x8 if conv_param_5 is None: conv_param_5 = { 'filter_num': 64, 'filter_size': 3, 'pad': 1, 'stride': 1 } # 第五个卷积层输入64x8x8,输出64x8x8 if conv_param_6 is None: conv_param_6 = { 'filter_num': 64, 'filter_size': 3, 'pad': 1, 'stride': 1 } """ 卷积层的每个节点只与前一层的filter_size个节点连接, 即本层卷积层的卷积核 高x宽有多少,就和前一层的多少个节点连接。 如果有多个通道,那还要乘上通道数(深度) 这里的所有卷积层都用3x3的大小 各层输出如下: 卷积层1: 16 28 28 卷积层2 | 池化层1: 16 28 28 | 16 14 14 卷积层3: 32 14 14 卷积层4 | 池化层2: 32 16 16 | 32 8 8 卷积层5: 64 8 8 卷积层6: 64 8 8 | 64 4 4 """ pre_node_nums = np.array([ 1 * 3 * 3, # 卷积层1:前一层(输入层)通道数(深度)为1 16 * 3 * 3, # 卷积层2:前一层(卷积层1)通道数(深度)为16 16 * 3 * 3, # 卷积层3:前一层(卷积层2)通道数(深度)为16 32 * 3 * 3, # 卷积层4:前一层(卷积层3)通道数(深度)为32 32 * 3 * 3, # 卷积层5:前一层(卷积层4)通道数(深度)为32 64 * 3 * 3, # 卷积层6:前一层(卷积层5)通道数(深度)为64 # 隐藏层:前一层(池化层),池化层接全连接层需要拉直成一维数组, # 因此隐藏层与前一层(池化层)的连接数为池化层的输出节点总数 64 * 4 * 4, # 输出层:前一层(隐藏层),全连接与前一层全部节点相连,即隐藏层大小 hidden_size ]) # 权重初始化时的标准差。由于使用ReLU激活函数,因此使用He初始化方式 weight_init_scales = np.sqrt(2.0 / pre_node_nums) """初始化权重参数和偏置""" self.params = {} pre_channel_num = input_dim[0] # 记录上一层的通道数(即滤波器的通道数) for idx, conv_param in enumerate([ conv_param_1, conv_param_2, conv_param_3, conv_param_4, conv_param_5, conv_param_6 ]): # 卷积层滤波器的形状:滤波器个数、通道数、高度、宽度 self.params['W'+str(idx+1)] = weight_init_scales[idx] *\ np.random.randn( conv_param['filter_num'], pre_channel_num, conv_param['filter_size'], conv_param['filter_size']) self.params['b' + str(idx + 1)] = np.zeros( conv_param['filter_num']) pre_channel_num = conv_param['filter_num'] # 更新上一层的通道数 self.params['W7'] = weight_init_scales[6] * np.random.randn( 64 * 4 * 4, hidden_size) self.params['b7'] = np.zeros(hidden_size) self.params['W8'] = weight_init_scales[7] * np.random.randn( hidden_size, output_size) self.params['b8'] = np.zeros(output_size) """ 构造神经网络: 书上没有用到之前用的有序字典,其实我觉得很好用,所以就实现了有序字典版本 Conv1->ReLU1->Conv2->ReLU2->Pool1-> Conv3->ReLU3->Conv4->ReLU4->Pool2-> Conv5->ReLU5->Conv6->ReLU6->Pool3-> Affine1(Hidden Layer1)->ReLU7->Dropout1-> Affine2(Output Layer1)->Dropout2------->SoftmaxWithLoss """ self.layers = OrderedDict() self.layers['Conv1'] = Convolution(self.params['W1'], self.params['b1'], stride=conv_param_1['stride'], pad=conv_param_1['pad']) self.layers['ReLU1'] = ReLU() self.layers['Conv2'] = Convolution(self.params['W2'], self.params['b2'], stride=conv_param_2['stride'], pad=conv_param_2['pad']) self.layers['ReLU2'] = ReLU() self.layers['Pool1'] = Pooling(pool_h=2, pool_w=2, stride=2, pad=0) self.layers['Conv3'] = Convolution(self.params['W3'], self.params['b3'], stride=conv_param_3['stride'], pad=conv_param_3['pad']) self.layers['ReLU3'] = ReLU() self.layers['Conv4'] = Convolution(self.params['W4'], self.params['b4'], stride=conv_param_4['stride'], pad=conv_param_4['pad']) self.layers['ReLU4'] = ReLU() self.layers['Pool2'] = Pooling(pool_h=2, pool_w=2, stride=2, pad=0) self.layers['Conv5'] = Convolution(self.params['W5'], self.params['b5'], stride=conv_param_5['stride'], pad=conv_param_5['pad']) self.layers['ReLU5'] = ReLU() self.layers['Conv6'] = Convolution(self.params['W6'], self.params['b6'], stride=conv_param_6['stride'], pad=conv_param_6['pad']) self.layers['ReLU6'] = ReLU() self.layers['Pool3'] = Pooling(pool_h=2, pool_w=2, stride=2, pad=0) self.layers['Affine1'] = Affine(self.params['W7'], self.params['b7']) self.layers['ReLU7'] = ReLU() self.layers['Dropout1'] = Dropout(dropout_ratio=0.5) self.layers['Affine2'] = Affine(self.params['W8'], self.params['b8']) self.layers['Dropout2'] = Dropout(dropout_ratio=0.5) self.last_layer = SoftmaxWithLoss()
# pickle 파일에 저장된 파라미터를 cnn의 필드로 로드 cnn.load_params('cnn_params.pkl') after_filters = cnn.params['W1'] # 학습 끝난 후 갱신(업데이트)된 파라미터를 그래프로 출력 show_filters(after_filters, 16, 4) # 학습 끝난 후 갱신된 파라미터를 실제 이미지 파일에 적용 lena = plt.imread('lena_gray.png') # imread는 png만 바로 shape으로 적용하게 해준다 # jpg는 PIL를 사용해서 열어야한다 print(lena.shape) #(256, 256) #이미지 파일이 numpy array로 변환됨 -> 2차원 # 이미지 데이터를 convolution layer의 forward()메소드에 전달 # 우리의 convolution 레이어는 오직 4차원만 받음 -> 레나 이미지를 4차원으로 만들어줘야한다 # 2차원 배열을 4차원 배열로 변환 lena = lena.reshape(1, 1, *lena.shape) # *lena.shape = (256, 256) for i in range(16): # 필터 16개에 대해서 반복 w = cnn.params['W1'][i] # 갱신된 필터w # b = cnn.params['b1'][i] #갱신된 bias b = 0 # did not use bias because the image might get distorted w = w.reshape(1, *w.shape) #3d -> 4d conv = Convolution(w, b) # convolution 레이어 생성 # 필터 - 학습이 끝난 상태의 필터 (5x5의 작은 필터를 보낸것) out = conv.forward(lena) #이미지 필터에 적용 # pyplot을 사용하기 위해서 4d-> 2d out = out.reshape(out.shape[2], out.shape[3]) # subplot에 그림 plt.subplot(4, 4, i + 1, xticks=[], yticks=[]) plt.imshow(out, cmap='gray') plt.show()
show_filters(before_filters, num_filters=16, ncols=4) # 학습이 끝난 후 파라미터 cnn.load_params('cnn_params.pkl') after_filters = cnn.params['W1'] # 학습 끝난 후 갱신된 파라미터 그래프로 출력 show_filters(after_filters, 16, 4) # 학습 끝난 후 갱신 된 파라미터를 실제 이미지 파일에 적용 lena = plt.imread( 'lena_gray.png' ) # numpy array 로 이미지 열어진다. 단, png 파일만. jpeg 의 경우 외부 패키지 필요 print('lena shape =', lena.shape) # (256, 256) ndarray # 이미지 데이터를 Convolution 레이어의 forward() 메소드에 전달하기 위해서 2차원 배열을 4차원 배열로 변환 lena = lena.reshape(1, 1, *lena.shape) # * : 튜플 내용을 하나씩 꺼내준다. (256, 256) print('lena shape =', lena.shape) for i in range(16): # 필터 16개에 대해 반복 w = cnn.params['W1'][i] # 갱신된 필터 b = 0 # 바이어스 사용 안함 w = w.reshape(1, *w.shape) # 3차원 -> 4차원 conv = Convolution(w, b) # Convolution 레이어 생성 out = conv.forward(lena) # 이미지에 필터 적용 # pyplot 을 사용하기 위해거 4차원을 2차원으로 변환 out = out.reshape(out.shape[2], out.shape[3]) plt.subplot(4, 4, i + 1, xticks=[], yticks=[]) plt.imshow(out, cmap='gray') plt.show()
def __init__(self, input_dim=(1, 28, 28), conv_param={'filter_num':30, 'filter_size':5, 'pad':0, 'stride':1}, hidden_size=100, output_size=10, weight_init_std=0.01): """ input_size : 入力の配列形状(チャンネル数、画像の高さ、画像の幅) conv_param : 畳み込みの条件, dict形式 例、{'filter_num':30, 'filter_size':5, 'pad':0, 'stride':1} hidden_size : 隠れ層のノード数 output_size : 出力層のノード数 weight_init_std : 重みWを初期化する際に用いる標準偏差 """ filter_num = conv_param['filter_num'] filter_size = conv_param['filter_size'] filter_pad = conv_param['pad'] filter_stride = conv_param['stride'] input_size = input_dim[1] conv_output_size = (input_size - filter_size + 2*filter_pad) / filter_stride + 1 pool_output_size = int(filter_num * (conv_output_size/2) * (conv_output_size/2)) # 重みの初期化 self.params = {} std = weight_init_std self.params['W1_1'] = std * np.random.randn(16, 1, 3,3) self.params['b1_1'] = np.zeros(16) self.params['W1_3'] = std * np.random.randn(16, 16, 3,3) self.params['b1_3'] = np.zeros(16) self.params['W2_1'] = std * np.random.randn(32, 16, 3,3) self.params['b2_1'] = np.zeros(32) self.params['W2_3'] = std * np.random.randn(32, 32, 3,3) self.params['b2_3'] = np.zeros(32) self.params['W3_1'] = std * np.random.randn(64, 32, 3,3) self.params['b3_1'] = np.zeros(64) self.params['W3_3'] = std * np.random.randn(64, 64, 3,3) self.params['b3_3'] = np.zeros(64) self.params['W4_1'] = std * np.random.randn(64*4*4, hidden_size) self.params['b4_1'] = np.zeros(hidden_size) self.params['W5_1'] = std * np.random.randn(hidden_size, output_size) self.params['b5_1'] = np.zeros(output_size) # レイヤの生成 self.layers = OrderedDict() self.layers['Conv1_1'] = Convolution(self.params['W1_1'], self.params['b1_1'],1,1) self.layers['ReLU1_2'] = ReLU() self.layers['Conv1_3'] = Convolution(self.params['W1_3'], self.params['b1_3'],1,1) self.layers['ReLU1_4'] = ReLU() self.layers['Pool1_5'] = MaxPooling(pool_h=2, pool_w=2, stride=2) self.layers['Conv2_1'] = Convolution(self.params['W2_1'], self.params['b2_1'],1,1) self.layers['ReLU2_2'] = ReLU() self.layers['Conv2_3'] = Convolution(self.params['W2_3'], self.params['b2_3'],1,2) self.layers['ReLU2_4'] = ReLU() self.layers['Pool2_5'] = MaxPooling(pool_h=2, pool_w=2, stride=2) self.layers['Conv3_1'] = Convolution(self.params['W3_1'], self.params['b3_1'],1,1) self.layers['ReLU3_2'] = ReLU() self.layers['Conv3_3'] = Convolution(self.params['W3_3'], self.params['b3_3'],1,1) self.layers['ReLU3_4'] = ReLU() self.layers['Pool3_5'] = MaxPooling(pool_h=2, pool_w=2, stride=2) self.layers['Affine4_1'] = Affine(self.params['W4_1'], self.params['b4_1']) self.layers['ReLU4_2'] = ReLU() self.layers['Dropout4_3'] = Dropout(0.5) self.layers['Affine5_1'] = Affine(self.params['W5_1'], self.params['b5_1']) self.layers['Dropout5_2'] = Dropout(0.5) self.last_layer = SoftmaxWithLoss()
def __init__(self, input_dim=(1, 28, 28), use_conv2=True, use_affine2=True, conv_param={ 'filter_num': 128, 'filter_size': 3, 'pad': 1, 'stride': 1 }, pool_param={ 'pool_size': 2, 'pad': 1, 'stride': 2 }, conv_param2={ 'filter_num2': 128, 'filter_size2': 3, 'pad2': 1, 'stride2': 1 }, pool_param2={ 'pool_size2': 2, 'pad2': 1, 'stride2': 2 }, hidden_size=128, hidden_size2=128, output_size=15, weight_init_std=0.01, use_batchnorm_C1=False, use_batchnorm_C2=False, use_batchnorm_A1=False, use_batchnorm_A2=False, use_dropout_A1=False, dropout_ratio_A1=0.5, use_dropout_A2=False, dropout_ratio_A2=0.5, use_succession=False, data_num=1, prediction_mode=False): filter_num = conv_param['filter_num'] filter_size = conv_param['filter_size'] filter_pad = conv_param['pad'] filter_stride = conv_param['stride'] pool_size = pool_param['pool_size'] pool_pad = pool_param['pad'] pool_stride = pool_param['stride'] filter_num2 = conv_param2['filter_num2'] filter_size2 = conv_param2['filter_size2'] filter_pad2 = conv_param2['pad2'] filter_stride2 = conv_param2['stride2'] pool_size2 = pool_param2['pool_size2'] pool_pad2 = pool_param2['pad2'] pool_stride2 = pool_param2['stride2'] input_size = input_dim[1] conv_output_size = (input_size + 2 * filter_pad - filter_size ) // filter_stride + 1 # 畳み込み後のサイズ(H,W共通) pool_output_size = (conv_output_size + 2 * pool_pad - pool_size) // pool_stride + 1 # プーリング後のサイズ(H,W共通) pool_output_pixel = filter_num * pool_output_size * pool_output_size # プーリング後のピクセル総数 input_size2 = pool_output_size conv_output_size2 = (input_size2 + 2 * filter_pad2 - filter_size2 ) // filter_stride2 + 1 # 畳み込み後のサイズ(H,W共通) pool_output_size2 = (conv_output_size2 + 2 * pool_pad2 - pool_size2 ) // pool_stride2 + 1 # プーリング後のサイズ(H,W共通) pool_output_pixel2 = filter_num2 * pool_output_size2 * pool_output_size2 # プーリング後のピクセル総数 self.use_conv2 = use_conv2 self.use_affine2 = use_affine2 self.use_batchnorm_C1 = use_batchnorm_C1 self.use_batchnorm_C2 = use_batchnorm_C2 self.use_batchnorm_A1 = use_batchnorm_A1 self.use_batchnorm_A2 = use_batchnorm_A2 self.use_dropout_A1 = use_dropout_A1 self.use_dropout_A2 = use_dropout_A2 self.dropout_ratio_A1 = dropout_ratio_A1 self.dropout_ratio_A2 = dropout_ratio_A2 self.use_succession = use_succession self.data_num = data_num self.prediction_mode = prediction_mode # if W1 == []: self.params = {} self.paramsB = {} std = weight_init_std if self.use_succession: #----------重みをpickleから代入-------------- with open("params_" + str(self.data_num) + ".pickle", "rb") as f: params_s = pickle.load(f) with open("params_BN" + str(self.data_num) + ".pickle", "rb") as f: params_BN = pickle.load(f) # self.params = {} # self.paramsB = {} self.params['W1'] = params_s['W1'] # W1は畳み込みフィルターの重みになる self.params['b1'] = params_s['b1'] if self.use_batchnorm_C1: self.paramsB["BC1_moving_mean"] = params_BN["BC1_moving_mean"] self.paramsB["BC1_moving_var"] = params_BN["BC1_moving_var"] if self.use_conv2: self.params['W1_2'] = params_s['W1_2'] self.params['b1_2'] = params_s['b1_2'] if self.use_batchnorm_C2: self.paramsB["BC2_moving_mean"] = params_BN[ "BC2_moving_mean"] self.paramsB["BC2_moving_var"] = params_BN[ "BC2_moving_var"] self.params['W2'] = params_s['W2'] self.params['b2'] = params_s['b2'] if self.use_batchnorm_A1: self.paramsB["BA1_moving_mean"] = params_BN["BA1_moving_mean"] self.paramsB["BA1_moving_var"] = params_BN["BA1_moving_var"] if self.use_affine2: self.params['W2_2'] = params_s['W2_2'] self.params['b2_2'] = params_s['b2_2'] if self.use_batchnorm_A2: self.paramsB["BA2_moving_mean"] = params_BN[ "BA2_moving_mean"] self.paramsB["BA2_moving_var"] = params_BN[ "BA2_moving_var"] self.params['W3'] = params_s['W3'] self.params['b3'] = params_s['b3'] #----------重みをpickleから代入-------------- else: # 重みの初期化 #----第1層Conv---- self.params['W1'] = std * np.random.randn( filter_num, input_dim[0], filter_size, filter_size) # W1は畳み込みフィルターの重みになる self.params['b1'] = np.zeros(filter_num) #b1は畳み込みフィルターのバイアスになる #----第2層Conv---- if self.use_conv2: self.params['W1_2'] = std * np.random.randn( filter_num2, filter_num, filter_size2, filter_size2) #-----追加------ self.params['b1_2'] = np.zeros(filter_num2) #-----追加------ #----第3層Affine---- self.params['W2'] = std * np.random.randn( pool_output_pixel2, hidden_size) else: self.params['W2'] = std * np.random.randn( pool_output_pixel, hidden_size) self.params['b2'] = np.zeros(hidden_size) #----第4層Affine---- if self.use_affine2: self.params['W2_2'] = std * np.random.randn( hidden_size, hidden_size2) #-----追加------ self.params['b2_2'] = np.zeros(hidden_size2) #-----追加------ #----第5層出力---- self.params['W3'] = std * np.random.randn( hidden_size2, output_size) #--変更-- else: self.params['W3'] = std * np.random.randn( hidden_size, output_size) #--変更-- self.params['b3'] = np.zeros(output_size) # レイヤの生成 self.layers = OrderedDict() #----第1層Conv---- self.layers['Conv1'] = Convolution( self.params['W1'], self.params['b1'], conv_param['stride'], conv_param['pad']) # W1が畳み込みフィルターの重み, b1が畳み込みフィルターのバイアスになる if self.use_batchnorm_C1: print(conv_output_size) print(conv_output_size ^ 2) batch_num = conv_output_size * conv_output_size if self.prediction_mode: self.layers['BatchNormalization_C1'] = BatchNormalization( np.ones(batch_num, filter_num), np.zeros(filter_num), moving_mean=self.paramsB["BC1_moving_mean"], moving_var=self.paramsB["BC1_moving_var"]) else: self.layers['BatchNormalization_C1'] = BatchNormalization( np.ones(batch_num), np.zeros(batch_num), DataNum=self.data_num, LayerNum="C1") self.paramsB["BC1_moving_mean"] = self.layers[ 'BatchNormalization_C1'].moving_mean self.paramsB["BC1_moving_var"] = self.layers[ 'BatchNormalization_C1'].moving_var self.layers['ReLU1'] = ReLU() self.layers['Pool1'] = MaxPooling(pool_h=pool_size, pool_w=pool_size, stride=pool_stride, pad=pool_pad) #----第2層Conv---- if self.use_conv2: self.layers['Conv1_2'] = Convolution( self.params['W1_2'], self.params['b1_2'], conv_param2['stride2'], conv_param2['pad2']) #-----追加------ if self.use_batchnorm_C2: batch_num2 = conv_output_size2 * conv_output_size2 * filter_num2 if self.prediction_mode: self.layers['BatchNormalization_C2'] = BatchNormalization( np.ones(batch_num), np.zeros(batch_num), moving_mean=self.paramsB["BC2_moving_mean"], moving_var=self.paramsB["BC12moving_var"]) else: self.layers['BatchNormalization_C2'] = BatchNormalization( np.ones(batch_num), np.zeros(batch_num), DataNum=self.data_num, LayerNum="C2") self.paramsB["BC2_moving_mean"] = self.layers[ 'BatchNormalization_C2'].moving_mean self.paramsB["BC2_moving_var"] = self.layers[ 'BatchNormalization_C2'].moving_var self.layers['ReLU1_2'] = ReLU() #-----追加------ self.layers['Pool1_2'] = MaxPooling(pool_h=pool_size2, pool_w=pool_size2, stride=pool_stride2, pad=pool_pad2) #-----追加------ #----第3層Affine---- self.layers['Affine1'] = Affine(self.params['W2'], self.params['b2']) if self.use_batchnorm_A1: if self.prediction_mode: self.layers['BatchNormalization_A1'] = BatchNormalization( np.ones(hidden_size), np.zeros(hidden_size), moving_mean=self.paramsB["BA1_moving_mean"], moving_var=self.paramsB["BA1_moving_var"]) else: self.layers['BatchNormalization_A1'] = BatchNormalization( np.ones(hidden_size), np.zeros(hidden_size), DataNum=self.data_num, LayerNum="A1") self.paramsB["BA1_moving_mean"] = self.layers[ 'BatchNormalization_A1'].moving_mean self.paramsB["BA1_moving_var"] = self.layers[ 'BatchNormalization_A1'].moving_var if self.use_dropout_A1: self.layers['DropoutA1'] = Dropout(self.dropout_ratio_A1) self.layers['ReLU2'] = ReLU() # ----第4層Affine---- if self.use_affine2: self.layers['Affine2'] = Affine( self.params['W2_2'], self.params['b2_2']) #-----追加------ if self.use_batchnorm_A2: if self.prediction_mode: self.layers['BatchNormalization_A2'] = BatchNormalization( np.ones(hidden_size2), np.zeros(hidden_size2), moving_mean=self.paramsB["BA2_moving_mean"], moving_var=self.paramsB["BA2_moving_var"]) else: self.layers['BatchNormalization_A2'] = BatchNormalization( np.ones(hidden_size2), np.zeros(hidden_size2), DataNum=self.data_num, LayerNum="A2") self.paramsB["BA2_moving_mean"] = self.layers[ 'BatchNormalization_A2'].moving_mean self.paramsB["BA2_moving_var"] = self.layers[ 'BatchNormalization_A2'].moving_var if self.use_dropout_A2: self.layers['DropoutA2'] = Dropout(self.dropout_ratio_A2) self.layers['ReLU3'] = ReLU() #-----追加------ #----第5層出力---- self.layers['Affine3'] = Affine(self.params['W3'], self.params['b3']) self.last_layer = SoftmaxWithLoss()