我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用keras.layers.advanced_activations.ThresholdedReLU()。
def test_thresholded_relu(): from keras.layers.advanced_activations import ThresholdedReLU layer_test(ThresholdedReLU, kwargs={'theta': 0.5}, input_shape=(2, 3, 4))
def test_keras_export(self): tests = open(os.path.join(settings.BASE_DIR, 'tests', 'unit', 'keras_app', 'keras_export_test.json'), 'r') response = json.load(tests) tests.close() net = yaml.safe_load(json.dumps(response['net'])) net = {'l0': net['Input'], 'l1': net['ThresholdedReLU']} net['l0']['connection']['output'].append('l1') inp = data(net['l0'], '', 'l0')['l0'] net = activation(net['l1'], [inp], 'l1') model = Model(inp, net['l1']) self.assertEqual(model.layers[1].__class__.__name__, 'ThresholdedReLU')
def activation(layer, layer_in, layerId): out = {} if (layer['info']['type'] == 'ReLU'): if (layer['params']['negative_slope'] != 0): out[layerId] = LeakyReLU(alpha=layer['params']['negative_slope'])(*layer_in) else: out[layerId] = Activation('relu')(*layer_in) elif (layer['info']['type'] == 'PReLU'): out[layerId] = PReLU()(*layer_in) elif (layer['info']['type'] == 'ELU'): out[layerId] = ELU(alpha=layer['params']['alpha'])(*layer_in) elif (layer['info']['type'] == 'ThresholdedReLU'): out[layerId] = ThresholdedReLU(theta=layer['params']['theta'])(*layer_in) elif (layer['info']['type'] == 'Sigmoid'): out[layerId] = Activation('sigmoid')(*layer_in) elif (layer['info']['type'] == 'TanH'): out[layerId] = Activation('tanh')(*layer_in) elif (layer['info']['type'] == 'Softmax'): out[layerId] = Activation('softmax')(*layer_in) elif (layer['info']['type'] == 'SELU'): out[layerId] = Activation('selu')(*layer_in) elif (layer['info']['type'] == 'Softplus'): out[layerId] = Activation('softplus')(*layer_in) elif (layer['info']['type'] == 'Softsign'): out[layerId] = Activation('softsign')(*layer_in) elif (layer['info']['type'] == 'HardSigmoid'): out[layerId] = Activation('hard_sigmoid')(*layer_in) return out
def test_tiny_conv_thresholded_relu_random(self): np.random.seed(1988) # Define a model from keras.layers.advanced_activations import ThresholdedReLU model = Sequential() model.add(Conv2D(input_shape = (10, 10, 3), filters = 3, kernel_size=(5,5), padding = 'same')) model.add(ThresholdedReLU(theta=0.8)) model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) # Get the coreml model self._test_keras_model(model)
def test_tiny_conv_thresholded_relu_random(self): np.random.seed(1988) # Define a model from keras.layers.advanced_activations import ThresholdedReLU model = Sequential() model.add(Convolution2D(input_shape = (10, 10, 3), nb_filter = 3, nb_row = 5, nb_col = 5, border_mode = 'same')) model.add(ThresholdedReLU(theta=0.8)) model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()]) # Get the coreml model self._test_keras_model(model)
def get_activation_layer(activation): if activation == 'LeakyReLU': return LeakyReLU() if activation == 'PReLU': return PReLU() if activation == 'ELU': return ELU() if activation == 'ThresholdedReLU': return ThresholdedReLU() return Activation(activation) # TODO: same for optimizers, including clipnorm
def test_keras_import(self): # softmax model = Sequential() model.add(Activation('softmax', input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'Softmax') # relu model = Sequential() model.add(Activation('relu', input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'ReLU') # tanh model = Sequential() model.add(Activation('tanh', input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'TanH') # sigmoid model = Sequential() model.add(Activation('sigmoid', input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'Sigmoid') # selu model = Sequential() model.add(Activation('selu', input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'SELU') # softplus model = Sequential() model.add(Activation('softplus', input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'Softplus') # softsign model = Sequential() model.add(Activation('softsign', input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'Softsign') # hard_sigmoid model = Sequential() model.add(Activation('hard_sigmoid', input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'HardSigmoid') # LeakyReLU model = Sequential() model.add(LeakyReLU(alpha=1, input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'ReLU') # PReLU model = Sequential() model.add(PReLU(input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'PReLU') # ELU model = Sequential() model.add(ELU(alpha=1, input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'ELU') # ThresholdedReLU model = Sequential() model.add(ThresholdedReLU(theta=1, input_shape=(15,))) model.build() self.keras_type_test(model, 0, 'ThresholdedReLU')
