def setUp(self): self.op_type = "cross_entropy" batch_size = 30 class_num = 10 X = randomize_probability(batch_size, class_num, dtype='float64') label = np.random.randint(0, class_num, (batch_size, 1), dtype="int64") cross_entropy = np.asmatrix([[-np.log(X[i][label[i][0]])] for i in range(X.shape[0])], dtype="float64") self.inputs = {"X": X, "Label": label} self.outputs = {"Y": cross_entropy} self.attrs = {"soft_label": False}
def setUp(self): self.op_type = "cross_entropy" batch_size = 5 class_num = 37 X = randomize_probability(batch_size, class_num) label = np.random.uniform(0.1, 1.0, [batch_size, class_num]).astype("float32") label /= label.sum(axis=1, keepdims=True) cross_entropy = (-label * np.log(X)).sum( axis=1, keepdims=True).astype("float32") self.inputs = {"X": X, "Label": label} self.outputs = {"Y": cross_entropy} self.attrs = {"soft_label": True}
def setUp(self): self.op_type = "cross_entropy" batch_size = 30 class_num = 10 X = randomize_probability(batch_size, class_num, dtype='float64') label = np.random.randint(0, class_num, (batch_size, 1), dtype="int64") cross_entropy = np.asmatrix( [[-np.log(X[i][label[i][0]])] for i in range(X.shape[0])], dtype="float64") self.inputs = {"X": X, "Label": label} self.outputs = {"Y": cross_entropy} self.attrs = {"soft_label": False}
def setUp(self): self.op_type = "cross_entropy" batch_size = 5 class_num = 37 X = randomize_probability(batch_size, class_num) label = np.random.uniform(0.1, 1.0, [batch_size, class_num]).astype("float32") label /= label.sum(axis=1, keepdims=True) cross_entropy = (-label * np.log(X)).sum( axis=1, keepdims=True).astype("float32") self.inputs = {"X": X, "Label": label} self.outputs = {"Y": cross_entropy} self.attrs = {"soft_label": True}
def setUp(self): self.op_type = "bpr_loss" batch_size = 40 class_num = 5 X = randomize_probability(batch_size, class_num, dtype='float64') label = np.random.randint(0, class_num, (batch_size, 1), dtype="int64") bpr_loss_result = [] for i in range(batch_size): sum = 0.0 for j in range(class_num): if j == label[i][0]: continue sum += (-np.log(1.0 + np.exp(X[i][j] - X[i][label[i][0]]))) bpr_loss_result.append(-sum / (class_num - 1)) bpr_loss = np.asmatrix([[x] for x in bpr_loss_result], dtype="float64") self.inputs = {"X": X, "Label": label} self.outputs = {"Y": bpr_loss}
def setUp(self): self.op_type = "cross_entropy" shape = [4, 3] ins_num = np.prod(np.array(shape)) class_num = 37 X_2d = randomize_probability(ins_num, class_num) label_2d = np.random.uniform(0.1, 1.0, [ins_num, class_num]).astype("float32") label_2d /= label_2d.sum(axis=1, keepdims=True) cross_entropy_2d = (-label_2d * np.log(X_2d)).sum( axis=1, keepdims=True).astype("float32") X = X_2d.reshape(shape + [class_num]) label = label_2d.reshape(shape + [class_num]) cross_entropy = np.array(cross_entropy_2d).reshape(shape + [1]) self.inputs = {"X": X, "Label": label} self.outputs = {"Y": cross_entropy} self.attrs = {"soft_label": True}
def setUp(self): self.op_type = "cross_entropy" shape = [10, 2, 4] ins_num = np.prod(np.array(shape)) class_num = 10 X_2d = randomize_probability(ins_num, class_num, dtype='float64') label_2d = np.random.randint(0, class_num, (ins_num, 1), dtype="int64") cross_entropy_2d = np.asmatrix([[-np.log(X_2d[i][label_2d[i][0]])] for i in range(X_2d.shape[0])], dtype="float64") X = X_2d.reshape(shape + [class_num]) label = label_2d.reshape(shape + [1]) cross_entropy = np.array(cross_entropy_2d).reshape(shape + [1]) self.inputs = {"X": X, "Label": label} self.outputs = {"Y": cross_entropy} self.attrs = {"soft_label": False}
def setUp(self): self.op_type = "cross_entropy" batch_size = 5 class_num = 17 X = randomize_probability(batch_size, class_num) label_index = np.random.randint( 0, class_num, (batch_size), dtype="int32") label = np.zeros(X.shape) label[np.arange(batch_size), label_index] = 1 cross_entropy = np.asmatrix( [[-np.log(X[i][label_index[i]])] for i in range(X.shape[0])], dtype="float32") cross_entropy2 = (-label * np.log(X)).sum( axis=1, keepdims=True).astype("float32") self.inputs = {"X": X, "Label": label.astype(np.float32)} self.outputs = {"Y": cross_entropy} self.attrs = {"soft_label": True}
def setUp(self): self.op_type = "cross_entropy" shape = [4, 3, 2] ins_num = np.prod(np.array(shape)) class_num = 17 X_2d = randomize_probability(ins_num, class_num) label_index_2d = np.random.randint(0, class_num, (ins_num), dtype="int32") label_2d = np.zeros(X_2d.shape) label_2d[np.arange(ins_num), label_index_2d] = 1 cross_entropy_2d = np.asmatrix([[-np.log(X_2d[i][label_index_2d[i]])] for i in range(X_2d.shape[0])], dtype="float32") X = X_2d.reshape(shape + [class_num]) label = label_2d.reshape(shape + [class_num]) cross_entropy = np.array(cross_entropy_2d).reshape(shape + [1]) self.inputs = {"X": X, "Label": label.astype(np.float32)} self.outputs = {"Y": cross_entropy} self.attrs = {"soft_label": True}
def init_x(self): self.x = randomize_probability(self.batch_size, self.class_num, dtype=self.dtype)
def init_x(self): self.shape = [4, 3, 2] self.ins_num = np.prod(np.array(self.shape)) self.X_2d = randomize_probability(self.ins_num, self.class_num).astype(self.dtype) self.x = self.X_2d.reshape(self.shape + [self.class_num])