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()
class TwoLayerNet: def __init__(self, input_size, hidden_size, output_size): I, H, O = input_size, hidden_size, output_size # 重みとバイアスの初期化 W1 = 0.01 * np.random.randn(I, H) # 属性の数を隠れ層のニューロン数に変換 b1 = np.zeros(H) W2 = 0.01 * np.random.randn(H, O) # 隠れ層のニューロン数をクラスの数に変換 b2 = np.zeros(O) # レイヤの生成 self.layers = [Affine(W1, b1), Sigmoid(), Affine(W2, b2)] self.loss_layer = SoftmaxWithLoss() # 全ての重みと勾配をリストにまとめる self.params, self.grads = [], [] for layer in self.layers: self.params += layer.params self.grads += layer.grads def predict(self, x): for layer in self.layers: x = layer.forward(x) return x def forward(self, x, t): score = self.predict(x) loss = self.loss_layer.forward(score, t) return loss def backward(self, dout=1): dout = self.loss_layer.backward(dout) for layer in reversed(self.layers): dout = layer.backward(dout) return dout
def __init__(self, input_size, hidden_size, output_size): """ class initializer Parameters -------- input_size : int the number of input neurons hidden_size : int the number of hidden neurons output_size : int the number of output neurons """ I, H, O = input_size, hidden_size, output_size # Initialize Weight & Bias W1 = np.random.randn(I, H) b1 = np.random.randn(H) W2 = np.random.randn(H, O) b2 = np.random.randn(O) # Generate Layers self.layers = [Affine(W1, b1), Sigmoid(), Affine(W2, b2)] self.loss_layer = SoftmaxWithLoss() # Store all layers' parameters (オリジナルとは異なる) self.params_list, self.grads_list = [], [] for layer in self.layers: self.params_list.append(layer.params) self.grads_list.append(layer.grads)
class SimpleSkipGram: def __init__(self, vocab_size, hidden_size): V, H = vocab_size, hidden_size W_in = 0.01 * np.random.randn(V, H).astype('f') W_out = 0.01 * np.random.randn(H, V).astype('f') self.in_layer = MatMul(W_in) self.out_layer = MatMul(W_out) self.loss_layer1 = SoftmaxWithLoss() self.loss_layer2 = SoftmaxWithLoss() layers = [self.in_layer, self.out_layer] self.params, self.grads = [], [] for layer in layers: self.params += layer.params self.grads += layer.grads self.word_vecs = W_in def forward(self, contexts, target): h = self.in_layer.forward(target) s = self.out_layer.forward(h) l1 = self.loss_layer1.forward(s, contexts[:, 0]) l2 = self.loss_layer2.forward(s, contexts[:, 1]) loss = l1 + l2 return loss def backward(self, dout=1): dl1 = self.loss_layer1.backward(dout) dl2 = self.loss_layer2.backward(dout) ds = dl1 + dl2 dh = self.out_layer.backward(ds) self.in_layer.backward(dh) return None
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, vocab_size, hidden_size): V, H = vocab_size, hidden_size # 가중치 초기화 W_in = 0.01 * np.random.randn(V, H).astype('f') W_out = 0.01 * np.random.randn(H, V).astype('f') # 계층 생성 # layer0, layer1은 weight-sharing self.in_layer0 = MatMul(W_in) ## 입력층은 윈도우 크기만큼 만들어야함, 인스턴스 생성. self.in_layer1 = MatMul(W_in) self.out_layer = MatMul(W_out) self.loss_layer = SoftmaxWithLoss() # 모든 가중치와 기울기를 리스트에 모음 layers = [ self.in_layer0, self.in_layer1, self.out_layer, self.loss_layer ] self.params, self.grads = [], [] for layer in layers: self.params += layer.params self.grads += layer.grads # 인스턴스 변수에 단어의 분산 표현 저장 self.word_vecs = W_in
def __init__(self, vocab_size, hidden_size): V, H = vocab_size, hidden_size # 重みの初期設定 W_in = 0.01 * np.random.randn(V, H).astype("f") W_out = 0.01 * np.random.randn(H, V).astype("f") # 各レイヤを作る。 # contextで使用する単語数分だけin_layerは作成する必要がある self.in_layer0 = MatMul(W_in) self.in_layer1 = MatMul(W_in) self.out_layer = MatMul(W_out) self.loss_layer = SoftmaxWithLoss() # 全てのlayer,重み,勾配をリストにまとめる layers = [ self.in_layer0, self.in_layer1, self.out_layer, self.loss_layer ] self.params, self.grads = [], [] for layer in layers: self.params += layer.params self.grads += layer.grads # メンバ変数に単語の分散表現を設定 self.word_vecs = W_in
def __init__(self, vocab_size, hidden_size): V, H = vocab_size, hidden_size # 가중치 초기화 W_in = 0.01 * np.random.randn(V, H).astype('f') W_out = 0.01 * np.random.randn(H, V).astype('f') # 계층 생성 # 입력층 1개 self.in_layer = MatMul(W_in) # 출력층 1개 self.out_layer = MatMul(W_out) # 맥락의 수만큼 손실 계층을 구한다 self.loss_layer1 = SoftmaxWithLoss() self.loss_layer2 = SoftmaxWithLoss() # 모든 가중치와 기울기를 리스트에 모은다 layers = [self.in_layer, self.out_layer] self.params, self.grads = [], [] for layer in layers: self.params += layer.params self.grads += layer.grads # 인스턴스 변수에 단어의 분산 표현을 저장한다 self.word_vecs = W_in
class TwoLayerNet: def __init__(self, input_size, hidden_size, output_size): I, H, O = input_size, hidden_size, output_size W1 = 0.01 * np.random.randn(I, H) b1 = np.zeros(H) W2 = 0.01 * np.random.randn(H, O) b2 = np.zeros(O) self.layers = [Affine(W1, b1), Sigmoid(), Affine(W2, b2)] self.loss_layer = SoftmaxWithLoss() self.params, self.grads = [], [] for layer in self.layers: self.params += layer.params self.grads += layer.grads def predict(self, x): for layer in self.layers: x = layer.forward(x) return x def forward(self, x, t): score = self.predict(x) loss = self.loss_layer.forward(score, t) return loss def backward(self, dout=1): dout = self.loss_layer.backward(dout) for layer in reversed(self.layers): dout = layer.backward(dout) return dout
def __init__(self, vocab_size, hidden_size): V, H = vocab_size, hidden_size # 重みの初期化 W_in = 0.01 * np.random.randn(V, H).astype('f') W_out = 0.01 * np.random.randn(H, V).astype('f') W_in = np.array( [[-1.0655735, 1.3231287, -1.1051644, -1.1049938, -1.0685176], [1.1559865, 0.08719956, 1.1672966, 1.1607609, 1.1567391], [-0.7532327, 0.6444376, -0.76896185, -0.71775854, -0.7918966], [0.9111972, 1.9940354, 0.6837302, 0.89859486, 0.87255], [-0.78328615, 0.6444221, -0.7729693, -0.7400077, -0.80646306], [-1.058986, 1.3268483, -1.1123687, -1.1059289, -1.0616288], [1.1203294, -1.6394324, 1.2104743, 1.1509397, 1.1612827]]).astype('f') # レイヤの生成 self.in_layer0 = MatMul(W_in) self.in_layer1 = MatMul(W_in) self.out_layer = MatMul(W_out) self.loss_layer = SoftmaxWithLoss() # 全ての重みと勾配をリストにまとめる layers = [self.in_layer0, self.in_layer1, self.out_layer] self.params, self.grads = [], [] for layer in layers: self.params += layer.params self.grads += layer.grads # メンバ変数に単語の分散表現を設定 self.word_vecs = W_in
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_size, hidden_sizes, output_size): I, O = input_size, output_size previous_H = I current_H = hidden_sizes[0] h_length = len(hidden_sizes) self.layers = [] for i in range(h_length): current_H = hidden_sizes[i] W = 0.01 * np.random.randn(previous_H, current_H) b = np.zeros(current_H) self.layers.append(Affine(W, b)) self.layers.append(Sigmoid()) #self.layers.append(ReLU()) previous_H = current_H W = 0.01 * np.random.randn(previous_H, O) b = np.zeros(O) self.layers.append(Affine(W, b)) self.loss_layer = SoftmaxWithLoss() # すべての重みと勾配をリストにまとめる self.params, self.grads = [], [] for layer in self.layers: self.params += layer.params self.grads += layer.grads
def __init__(self, n_features, n_output, n_hidden=30, l2=0.0, l1=0.0, epochs=50, eta=0.001, decrease_const=0.0, shuffle=True, n_minibatches=1, random_state=None): np.random.seed(random_state) self.n_features = n_features self.n_hidden = n_hidden self.n_output = n_output self.l2 = l2 self.l1 = l1 self.epochs = epochs self.eta = eta self.decrease_const = decrease_const self.shuffle = shuffle self.n_minibatches = n_minibatches self.params = {} self._init_weights() self.layers = {} self.layers['Affine_1'] = Affine(self.params['W1'], self.params['b1']) self.layers['Sigmoid'] = Sigmoid() self.layers['Affine_2'] = Affine(self.params['W2'], self.params['b2']) self.last_layer = SoftmaxWithLoss() self._loss = [] self._iter_t = 0
class SimpleCBOW: def __init__(self, vocab_size, hidden_size): V, H = vocab_size, hidden_size W_in = 0.01 * np.random.randn(V, H).astype('f') W_out = 0.01 * np.random.randn(H, V).astype('f') self.in_layer0 = MatMul(W_in) self.in_layer1 = MatMul(W_in) self.out_layer = MatMul(W_out) self.loss_layer = SoftmaxWithLoss() layers = [self.in_layer0, self.in_layer1, self.out_layer] self.params, self.grads = [], [] for layer in layers: self.params += layer.params self.grads += layer.grads self.word_vecs = W_in def forward(self, contexts, target): h0 = self.in_layer0.forward(contexts[:, 0]) h1 = self.in_layer1.forward(contexts[:, 1]) h = (h0 + h1) * 0.5 score = self.out_layer.forward(h) loss = self.loss_layer.forward(score, target) return loss def backward(self, dout=1): ds = self.loss_layer.backward(dout) da = self.out_layer.backward(ds) da *= 0.5 self.in_layer1.backward(da) self.in_layer0.backward(da) return None
def __init__(self, input_size, hidden_size_list, output_size, activation="relu", weight_init_std="relu", weight_decay_lambda=0, use_dropout=False, dropout_ratio=0.5, use_batchnorm=False): """ :param input_size: 输入的大小 :param hidden_size_list: 隐藏层的神经元数量列表 :param output_size: 输出的大小 :param activation: "relu" or "sigmoid" :param weight_init_std: 指定权重的标准差, 指定"relu" 或者 "he" 是定为"He"的初始值 指定"sigmoid" 或者 "xavier" 是定为"Xauver"的初始值 :param weight_decay_lambda: Weight Decay(L2范数)的强度 :param use_dropout: 是否使用Dropout :param dropout_ratio: Dropout比例 :param use_batchnorm: 是否只用Batch Normalization """ self.input_size = input_size self.output_size = output_size self.hidden_size_list = hidden_size_list self.hidden_layer_num = len(hidden_size_list) self.use_dropout = use_dropout self.weight_decay_lambda = weight_decay_lambda self.use_batchnorm = use_batchnorm self.params = {} # 初始化权值 self.__init_weight(weight_init_std) # 生成层 activation_layer = {"sigmoid": Sigmoid, "relu": ReLU} self.layers = OrderedDict() for idx in range(1, self.hidden_layer_num + 1): self.layers["Affine" + str(idx)] = Affine( self.params["W" + str(idx)], self.params["b" + str(idx)]) if self.use_batchnorm: self.params["gamma" + str(idx)] = np.ones( hidden_size_list[idx - 1]) self.params["beta" + str(idx)] = np.zeros( hidden_size_list[idx - 1]) self.layers['BatchNorm' + str(idx)] = BatchNormalization( self.params['gamma' + str(idx)], self.params['beta' + str(idx)]) self.layers["Activation_function" + str(idx)] = activation_layer[activation]() if self.use_dropout: self.layers["Dropout" + str(idx)] = Dropout(dropout_ratio) idx = self.hidden_layer_num + 1 self.layers["Affine" + str(idx)] = Affine(self.params["W" + str(idx)], self.params["b" + str(idx)]) self.last_layer = SoftmaxWithLoss()
class TwoLayerNet: "Affineを二層つなげたネットワーク" def __init__(self, input_size, hidden_size, output_size): """ class initializer Parameters -------- input_size : int the number of input neurons hidden_size : int the number of hidden neurons output_size : int the number of output neurons """ I, H, O = input_size, hidden_size, output_size # Initialize Weight & Bias W1 = np.random.randn(I, H) b1 = np.random.randn(H) W2 = np.random.randn(H, O) b2 = np.random.randn(O) # Generate Layers self.layers = [Affine(W1, b1), Sigmoid(), Affine(W2, b2)] self.loss_layer = SoftmaxWithLoss() # Store all layers' parameters (オリジナルとは異なる) self.params_list, self.grads_list = [], [] for layer in self.layers: self.params_list.append(layer.params) self.grads_list.append(layer.grads) def predict(self, x): for layer in self.layers: x = layer.forward(x) return x def forward(self, x, t): """ Parameters -------- x : ndarray input of the highest layer t : ndarray teacher data Returns -------- loss: ndarray loss of prediction result """ score = self.predict(x) loss = self.loss_layer.forward(score, t) return loss def backward(self, dout=1): dout = self.loss_layer.backward(dout) for layer in reversed(self.layers): dout = layer.backward(dout) return dout
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()
class SimpleCBOW: """ Simple continuous bag-of-words. """ def __init__(self, vocabulary_size, hidden_size): V, H = vocabulary_size, hidden_size # initialize weights W_in = 0.01 * np.random.randn(V, H).astype('f') W_out = 0.01 * np.random.randn(H, V).astype('f') # generate layers self.in_layer0 = MatMul(W_in) self.in_layer1 = MatMul(W_in) self.out_layer = MatMul(W_out) self.loss_layer = SoftmaxWithLoss() # list all weights and gradient layers layers = [self.in_layer0, self.in_layer1, self.out_layer] self.params, self.grads = [], [] for layer in layers: self.params += layer.params self.grads += layer.grads # set distributed representation of words to variable self.word_vecs = W_in def forward(self, contexts, target): """ :param contexts: dim 3 of numpy array :param target: dim2 of numpy array """ h0 = self.in_layer0.forward(contexts[:, 0]) h1 = self.in_layer1.forward(contexts[:, 1]) h = (h0 + h1) * 0.5 score = self.out_layer.forward(h) loss = self.loss_layer.forward(score, target) return loss def backward(self, dout=1): """ Continuous bag-of-words (CBOW) 0.5*da MatMul <-+ vector ----+ W_in | v | 0.5*da Softmax +-- [+] <- [x] <-- MatMul <-- With <-- Loss | ^ da W_out ds Loss 1 | 0.5 ----+ MatMul <-+ W_in 0.5*da """ ds = self.loss_layer.backward(dout) da = self.out_layer.backward(ds) da *= 0.5 self.in_layer1.backward(da) self.in_layer0.backward(da) return None
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 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 forward(self, xs, ts): _, T, _ = xs.shape layers = [] loss = 0 for t in range(T): layer = SoftmaxWithLoss() loss += layer.forward(xs[:, t, :], ts[:, t]) layers.append(layer) loss /= T self.cache = (layers, xs) return loss
def __init__(self, input_size, hidden_size, output_size, weight_init_std = 0.01): # 重みの初期化 self.params = {} self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size) self.params['b1'] = np.zeros(hidden_size) self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size) self.params['b2'] = np.zeros(output_size) # レイヤの生成 self.layers = OrderedDict() # 順番付きdict形式. self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1']) self.layers['Relu1'] = ReLU() self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2']) self.lastLayer = SoftmaxWithLoss() # 出力層
class SimpleCBOW: def __init__(self, vocab_size, hidden_size): V, H = vocab_size, hidden_size # 가중치 초기화 W_in = 0.01 * np.random.randn(V, H).astype('f') W_out = 0.01 * np.random.randn(H, V).astype('f') # 계층 생성 # layer0, layer1은 weight-sharing self.in_layer0 = MatMul(W_in) ## 입력층은 윈도우 크기만큼 만들어야함, 인스턴스 생성. self.in_layer1 = MatMul(W_in) self.out_layer = MatMul(W_out) self.loss_layer = SoftmaxWithLoss() # 모든 가중치와 기울기를 리스트에 모음 layers = [ self.in_layer0, self.in_layer1, self.out_layer, self.loss_layer ] self.params, self.grads = [], [] for layer in layers: self.params += layer.params self.grads += layer.grads # 인스턴스 변수에 단어의 분산 표현 저장 self.word_vecs = W_in def forward(self, contexts, target): # 양옆 단어에 대한 x*Win을 batch만큼 수행. -> 해당단어가 중심단어에 관해 어느정도의 의미가 있는지를 나타내(분산표현) # -> one_hot으로 표현되어 matmul이 수행되므로 weight에서 해당 행이 분산표현 벡터(값)이 됨. h0 = self.in_layer0.forward( contexts[:, 0]) # (batch, 7) * (vocab_size(7), hidden) h1 = self.in_layer1.forward( contexts[:, 1]) # (bathc, 7) * (vocab_size, hidden) h = (h0 + h1) * 0.5 # 양 옆의 분산표현의 합. score = self.out_layer.forward( h) # (batch,hidden) * ( hidden, vocab_size ) # print(score) # print(target) loss = self.loss_layer.forward(score, target) return loss def backward(self, dout=1): ds = self.loss_layer.backward(dout) da = self.out_layer.backward(ds) da *= 0.5 self.in_layer1.backward(da) self.in_layer0.backward(da) return None
def __init__(self, input_size, hidden_size, output_size): I, H, O = input_size, hidden_size, output_size W1 = 0.01 * np.random.randn(I, H) b1 = np.zeros(H) W2 = 0.01 * np.random.randn(H, O) b2 = np.zeros(O) self.layers = [Affine(W1, b1), Sigmoid(), Affine(W2, b2)] self.loss_layer = SoftmaxWithLoss() self.params, self.grads = [], [] for layer in self.layers: self.params += layer.params self.grads += layer.grads
class SimpleCBOW: def __init__(self, vocab_size, hidden_size): V, H = vocab_size, hidden_size # 重みの初期化 W_in = 0.01 * np.random.randn(V, H).astype('f') W_out = 0.01 * np.random.randn(H, V).astype('f') W_in = np.array( [[-1.0655735, 1.3231287, -1.1051644, -1.1049938, -1.0685176], [1.1559865, 0.08719956, 1.1672966, 1.1607609, 1.1567391], [-0.7532327, 0.6444376, -0.76896185, -0.71775854, -0.7918966], [0.9111972, 1.9940354, 0.6837302, 0.89859486, 0.87255], [-0.78328615, 0.6444221, -0.7729693, -0.7400077, -0.80646306], [-1.058986, 1.3268483, -1.1123687, -1.1059289, -1.0616288], [1.1203294, -1.6394324, 1.2104743, 1.1509397, 1.1612827]]).astype('f') # レイヤの生成 self.in_layer0 = MatMul(W_in) self.in_layer1 = MatMul(W_in) self.out_layer = MatMul(W_out) self.loss_layer = SoftmaxWithLoss() # 全ての重みと勾配をリストにまとめる layers = [self.in_layer0, self.in_layer1, self.out_layer] self.params, self.grads = [], [] for layer in layers: self.params += layer.params self.grads += layer.grads # メンバ変数に単語の分散表現を設定 self.word_vecs = W_in def forward(self, contexts, target): h0 = self.in_layer0.forward(contexts[:, 0]) h1 = self.in_layer1.forward(contexts[:, 1]) h = (h0 + h1) * 0.5 score = self.out_layer.forward(h) loss = self.loss_layer.forward(score, target) return loss def backward(self, dout=1): ds = self.loss_layer.backward(dout) da = self.out_layer.backward(ds) da *= 0.5 self.in_layer1.backward(da) self.in_layer0.backward(da) return None
class SimpleSkipGram: def __init__(self, vocab_size, hidden_size): V, H = vocab_size, hidden_size # 가중치 초기화 W_in = 0.01 * np.random.randn(V, H).astype('f') W_out = 0.01 * np.random.randn(H, V).astype('f') # 계층 생성 # 입력층 1개 self.in_layer = MatMul(W_in) # 출력층 1개 self.out_layer = MatMul(W_out) # 맥락의 수만큼 손실 계층을 구한다 self.loss_layer1 = SoftmaxWithLoss() self.loss_layer2 = SoftmaxWithLoss() # 모든 가중치와 기울기를 리스트에 모은다 layers = [self.in_layer, self.out_layer] self.params, self.grads = [], [] for layer in layers: self.params += layer.params self.grads += layer.grads # 인스턴스 변수에 단어의 분산 표현을 저장한다 self.word_vecs = W_in def forward(self, contexts, target): h = self.in_layer.forward(target) s = self.out_layer.forward(h) l1 = self.loss_layer1.forward(s, contexts[:, 0]) l2 = self.loss_layer2.forward(s, contexts[:, 1]) loss = l1 + l2 return loss def backward(self, dout=1): dl1 = self.loss_layer1.backward(dout) dl2 = self.loss_layer2.backward(dout) ds = dl1 + dl2 dh = self.out_layer.backward(ds) self.in_layer.backward(dh) return None
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] # 畳み込み層の出力サイズの計算 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)) # 重みパラメータの初期化 (1: 畳み込み層、2: 全結合、3: 全結合) self.params = {} 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) # 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']) 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_size, hidden_size, output_size, weight_init_std=0.01): # 初始化权重 # self.params = {"W1": np.random.randn(input_size, hidden_size) / np.sqrt(input_size), # "b1": np.zeros(hidden_size), # "W2": np.random.randn(hidden_size, output_size) / np.sqrt(hidden_size), # "b2": np.zeros(output_size)} self.params = {"W1": weight_init_std * np.random.randn(input_size, hidden_size), "b1": np.zeros(hidden_size), "W2": weight_init_std * np.random.randn(hidden_size, output_size), "b2": np.zeros(output_size)} # 生成层 self.layers = OrderedDict() self.layers["Affine1"] = Affine(self.params["W1"], self.params["b1"]) self.layers["ReLU1"] = ReLU() self.layers["Affine2"] = Affine(self.params["W2"], self.params["b2"]) self.lastLayer = SoftmaxWithLoss()
class SimpleCBOW: def __init__(self, vocab_size, hidden_size): V, H = vocab_size, hidden_size # 重みの初期化 W_in = 0.01 * np.random.randn(V, H).astype('f') W_out = 0.01 * np.random.randn(H, V).astype('f') # レイヤの作成 self.in_layer0 = MatMul(W_in) self.in_layer1 = MatMul(W_in) self.out_layer = MatMul(W_out) self.loss_layer = SoftmaxWithLoss() # すべての重みと勾配をリストにまとめる layers = [self.in_layer0, self.in_layer1, self.out_layer] self.params, self.grads = [], [] for layer in layers: self.params += layer.params self.grads += layer.grads # メンバ変数に単語の分散表現を設定 self.word_vecs = W_in def forward(self, contexts, target): print(contexts[:, 0]) h0 = self.in_layer0.forward(contexts[:, 0]) h1 = self.in_layer1.forward(contexts[:, 1]) h = (h0 + h1) * 0.5 score = self.out_layer.forward(h) loss = self.loss_layer.forward(score, target) return loss