我们从Python开源项目中，提取了以下25个代码示例，用于说明如何使用chainer.functions.clipped_relu()。
def to_function(self): if self.nonlinearity.lower() == "clipped_relu": return clipped_relu() if self.nonlinearity.lower() == "crelu": return crelu() if self.nonlinearity.lower() == "elu": return elu() if self.nonlinearity.lower() == "hard_sigmoid": return hard_sigmoid() if self.nonlinearity.lower() == "leaky_relu": return leaky_relu() if self.nonlinearity.lower() == "relu": return relu() if self.nonlinearity.lower() == "sigmoid": return sigmoid() if self.nonlinearity.lower() == "softmax": return softmax() if self.nonlinearity.lower() == "softplus": return softplus() if self.nonlinearity.lower() == "tanh": return tanh() if self.nonlinearity.lower() == "bst": return bst() raise NotImplementedError()
def to_function(self): if self.nonlinearity.lower() == "clipped_relu": return clipped_relu() if self.nonlinearity.lower() == "crelu": return crelu() if self.nonlinearity.lower() == "elu": return elu() if self.nonlinearity.lower() == "hard_sigmoid": return hard_sigmoid() if self.nonlinearity.lower() == "leaky_relu": return leaky_relu() if self.nonlinearity.lower() == "relu": return relu() if self.nonlinearity.lower() == "sigmoid": return sigmoid() if self.nonlinearity.lower() == "softmax": return softmax() if self.nonlinearity.lower() == "softplus": return softplus() if self.nonlinearity.lower() == "tanh": return tanh() raise NotImplementedError()
def __call__(self, x, t=None): self.clear() #x = Variable(x_data) # x_data.astype(np.float32) h = F.leaky_relu(self.conv1(x), slope=0.1) h = F.leaky_relu(self.conv2(h), slope=0.1) h = F.leaky_relu(self.conv3(h), slope=0.1) h = F.leaky_relu(self.conv4(h), slope=0.1) h = F.leaky_relu(self.conv5(h), slope=0.1) h = F.leaky_relu(self.conv6(h), slope=0.1) h = F.clipped_relu(self.conv7(h), z=1.0) if self.train: self.loss = F.mean_squared_error(h, t) return self.loss else: return h
def __call__(self, x): return functions.clipped_relu(x, self.z)
def check_forward(self, x_data): x = chainer.Variable(x_data) y = functions.clipped_relu(x, self.z) self.assertEqual(y.data.dtype, self.dtype) y_expect = self.x.copy() for i in numpy.ndindex(self.x.shape): if self.x[i] < 0: y_expect[i] = 0 elif self.x[i] > self.z: y_expect[i] = self.z gradient_check.assert_allclose(y_expect, y.data)
def _propagate(self, Y, dropout=0.): blstm = self.blstm_layer(Y, dropout=dropout) relu_1 = F.clipped_relu(self.relu_1(blstm, dropout=dropout)) relu_2 = F.clipped_relu(self.relu_2(relu_1, dropout=dropout)) N_mask = F.sigmoid(self.noise_mask_estimate(relu_2)) X_mask = F.sigmoid(self.speech_mask_estimate(relu_2)) return N_mask, X_mask
def _propagate(self, Y, dropout=0.): relu_1 = F.clipped_relu(self.relu_1(Y, dropout=dropout)) relu_2 = F.clipped_relu(self.relu_2(relu_1, dropout=dropout)) relu_3 = F.clipped_relu(self.relu_3(relu_2, dropout=dropout)) N_mask = F.sigmoid(self.noise_mask_estimate(relu_3)) X_mask = F.sigmoid(self.speech_mask_estimate(relu_3)) return N_mask, X_mask
def __init__(self, z=20.0): self._function = "clipped_relu" self.z = z
def __call__(self, x): return F.clipped_relu(x, self.z)
def _propagate(self, Y, dropout=0.): relu_1 = F.clipped_relu(self.relu_1(Y, dropout=dropout)) N_mask = F.sigmoid(self.noise_mask_estimate(relu_1)) X_mask = F.sigmoid(self.speech_mask_estimate(relu_1)) return N_mask, X_mask
def __call__(self, x, t=None): self.clear() h1 = F.leaky_relu(self.conv1(x), slope=0.1) h1 = F.leaky_relu(self.conv2(h1), slope=0.1) h1 = F.leaky_relu(self.conv3(h1), slope=0.1) h2 = self.seranet_v1_crbm(x) # Fusion h12 = F.concat((h1, h2), axis=1) lu = F.leaky_relu(self.convlu6(h12), slope=0.1) lu = F.leaky_relu(self.convlu7(lu), slope=0.1) lu = F.leaky_relu(self.convlu8(lu), slope=0.1) ru = F.leaky_relu(self.convru6(h12), slope=0.1) ru = F.leaky_relu(self.convru7(ru), slope=0.1) ru = F.leaky_relu(self.convru8(ru), slope=0.1) ld = F.leaky_relu(self.convld6(h12), slope=0.1) ld = F.leaky_relu(self.convld7(ld), slope=0.1) ld = F.leaky_relu(self.convld8(ld), slope=0.1) rd = F.leaky_relu(self.convrd6(h12), slope=0.1) rd = F.leaky_relu(self.convrd7(rd), slope=0.1) rd = F.leaky_relu(self.convrd8(rd), slope=0.1) # Splice h = CF.splice(lu, ru, ld, rd) h = F.leaky_relu(self.conv9(h), slope=0.1) h = F.leaky_relu(self.conv10(h), slope=0.1) h = F.leaky_relu(self.conv11(h), slope=0.1) h = F.clipped_relu(self.conv12(h), z=1.0) if self.train: self.loss = F.mean_squared_error(h, t) return self.loss else: return h
def __call__(self, x, t=None): self.clear() h = F.leaky_relu(self.conv1(x), slope=0.1) h = F.leaky_relu(self.conv2(h), slope=0.1) h = F.leaky_relu(self.conv3(h), slope=0.1) h = F.leaky_relu(self.conv4(h), slope=0.1) h = F.leaky_relu(self.conv5(h), slope=0.1) h = F.leaky_relu(self.conv6(h), slope=0.1) h = F.clipped_relu(self.conv7(h), z=1.0) if self.train: self.loss = F.mean_squared_error(h, t) return self.loss else: return h
def __call__(self, x, t=None): self.clear() h = F.leaky_relu(self.conv1(x), slope=0.1) h = F.leaky_relu(self.conv2(h), slope=0.1) #h = F.leaky_relu(self.conv3(h), slope=0.1) #h = F.leaky_relu(self.conv4(h), slope=0.1) h = F.clipped_relu(self.conv3(h), z=1.0) if self.train: self.loss = F.mean_squared_error(h, t) return self.loss else: return h
def __call__(self, x, train=False): """ calculate output of VoxResNet given input x Parameters ---------- x : (batch_size, in_channels, xlen, ylen, zlen) ndarray image to perform semantic segmentation Returns ------- proba: (batch_size, n_classes, xlen, ylen, zlen) ndarray probability of each voxel belonging each class elif train=True, returns list of logits """ h = self.conv1a(x) h = F.relu(self.bnorm1a(h, test=not train)) h = self.conv1b(h) c1 = F.clipped_relu(self.c1deconv(h)) c1 = self.c1conv(c1) h = F.relu(self.bnorm1b(h, test=not train)) h = self.conv1c(h) h = self.voxres2(h, train) h = self.voxres3(h, train) c2 = F.clipped_relu(self.c2deconv(h)) c2 = self.c2conv(c2) h = F.relu(self.bnorm3(h, test=not train)) h = self.conv4(h) h = self.voxres5(h, train) h = self.voxres6(h, train) c3 = F.clipped_relu(self.c3deconv(h)) c3 = self.c3conv(c3) h = F.relu(self.bnorm6(h, test=not train)) h = self.conv7(h) h = self.voxres8(h, train) h = self.voxres9(h, train) c4 = F.clipped_relu(self.c4deconv(h)) c4 = self.c4conv(c4) c = c1 + c2 + c3 + c4 if train: return [c1, c2, c3, c4, c] else: return F.softmax(c)
