这篇教程Python layers.PReLU方法代码示例写得很实用，希望能帮到您。
本文整理汇总了Python中keras.layers.PReLU方法的典型用法代码示例。如果您正苦于以下问题：Python layers.PReLU方法的具体用法？Python layers.PReLU怎么用？Python layers.PReLU使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块keras.layers的用法示例。 在下文中一共展示了layers.PReLU方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。 示例1: CapsuleNet# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def CapsuleNet(n_capsule = 10, n_routings = 5, capsule_dim = 16, n_recurrent=100, dropout_rate=0.2, l2_penalty=0.0001): K.clear_session() inputs = Input(shape=(170,)) x = Embedding(21099, 300, trainable=True)(inputs) x = SpatialDropout1D(dropout_rate)(x) x = Bidirectional( CuDNNGRU(n_recurrent, return_sequences=True, kernel_regularizer=l2(l2_penalty), recurrent_regularizer=l2(l2_penalty)))(x) x = PReLU()(x) x = Capsule( num_capsule=n_capsule, dim_capsule=capsule_dim, routings=n_routings, share_weights=True)(x) x = Flatten(name = 'concatenate')(x) x = Dropout(dropout_rate)(x)# fc = Dense(128, activation='sigmoid')(x) outputs = Dense(6, activation='softmax')(x) model = Model(inputs=inputs, outputs=outputs) model.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy']) return model
开发者ID:WeavingWong，项目名称:DigiX_HuaWei_Population_Age_Attribution_Predict，代码行数:24，代码来源:models.py
示例2: CapsuleNet_v2# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def CapsuleNet_v2(n_capsule = 10, n_routings = 5, capsule_dim = 16, n_recurrent=100, dropout_rate=0.2, l2_penalty=0.0001): K.clear_session() inputs = Input(shape=(200,)) x = Embedding(20000, 300, trainable=True)(inputs) x = SpatialDropout1D(dropout_rate)(x) x = Bidirectional( CuDNNGRU(n_recurrent, return_sequences=True, kernel_regularizer=l2(l2_penalty), recurrent_regularizer=l2(l2_penalty)))(x) x = PReLU()(x) x = Capsule( num_capsule=n_capsule, dim_capsule=capsule_dim, routings=n_routings, share_weights=True)(x) x = Flatten(name = 'concatenate')(x) x = Dropout(dropout_rate)(x)# fc = Dense(128, activation='sigmoid')(x) outputs = Dense(6, activation='softmax')(x) model = Model(inputs=inputs, outputs=outputs) model.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy']) return model
开发者ID:WeavingWong，项目名称:DigiX_HuaWei_Population_Age_Attribution_Predict，代码行数:24，代码来源:models.py
示例3: model_definition# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def model_definition(): """ Keras RNetwork for MTCNN """ input_ = Input(shape=(24, 24, 3)) var_x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(input_) var_x = PReLU(shared_axes=[1, 2], name='prelu1')(var_x) var_x = MaxPool2D(pool_size=3, strides=2, padding='same')(var_x) var_x = Conv2D(48, (3, 3), strides=1, padding='valid', name='conv2')(var_x) var_x = PReLU(shared_axes=[1, 2], name='prelu2')(var_x) var_x = MaxPool2D(pool_size=3, strides=2)(var_x) var_x = Conv2D(64, (2, 2), strides=1, padding='valid', name='conv3')(var_x) var_x = PReLU(shared_axes=[1, 2], name='prelu3')(var_x) var_x = Permute((3, 2, 1))(var_x) var_x = Flatten()(var_x) var_x = Dense(128, name='conv4')(var_x) var_x = PReLU(name='prelu4')(var_x) classifier = Dense(2, activation='softmax', name='conv5-1')(var_x) bbox_regress = Dense(4, name='conv5-2')(var_x) return [input_], [classifier, bbox_regress]
开发者ID:deepfakes，项目名称:faceswap，代码行数:22，代码来源:mtcnn.py
示例4: get_srresnet_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def get_srresnet_model(input_channel_num=3, feature_dim=64, resunit_num=16): def _residual_block(inputs): x = Conv2D(feature_dim, (3, 3), padding="same", kernel_initializer="he_normal")(inputs) x = BatchNormalization()(x) x = PReLU(shared_axes=[1, 2])(x) x = Conv2D(feature_dim, (3, 3), padding="same", kernel_initializer="he_normal")(x) x = BatchNormalization()(x) m = Add()([x, inputs]) return m inputs = Input(shape=(None, None, input_channel_num)) x = Conv2D(feature_dim, (3, 3), padding="same", kernel_initializer="he_normal")(inputs) x = PReLU(shared_axes=[1, 2])(x) x0 = x for i in range(resunit_num): x = _residual_block(x) x = Conv2D(feature_dim, (3, 3), padding="same", kernel_initializer="he_normal")(x) x = BatchNormalization()(x) x = Add()([x, x0]) x = Conv2D(input_channel_num, (3, 3), padding="same", kernel_initializer="he_normal")(x) model = Model(inputs=inputs, outputs=x) return model# UNet: code from https://github.com/pietz/unet-keras
开发者ID:zxq2233，项目名称:n2n-watermark-remove，代码行数:31，代码来源:model.py
示例5: emit_PRelu# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def emit_PRelu(self, IR_node, in_scope=False): if in_scope: raise NotImplementedError else: code = "{:<15} = layers.PReLU(name='{}')({})".format( IR_node.variable_name, IR_node.name, self.parent_variable_name(IR_node) ) return code
示例6: ResCNN# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def ResCNN(self, x): """ repeat of two conv :param x: tensor, input shape :return: tensor, result of two conv of resnet """ # pre-activation # x = PReLU()(x) x = Conv1D(self.filters_num, kernel_size=1, padding='SAME', kernel_regularizer=l2(self.l2), bias_regularizer=l2(self.l2), activation=self.activation_conv, )(x) x = BatchNormalization()(x) #x = PReLU()(x) x = Conv1D(self.filters_num, kernel_size=1, padding='SAME', kernel_regularizer=l2(self.l2), bias_regularizer=l2(self.l2), activation=self.activation_conv, )(x) x = BatchNormalization()(x) # x = Dropout(self.dropout)(x) x = PReLU()(x) return x
开发者ID:yongzhuo，项目名称:Keras-TextClassification，代码行数:30，代码来源:graph.py
示例7: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def __init__(self): super(PReLUNet, self).__init__() self.prelu = nn.PReLU(3)
示例8: test_prelu# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def test_prelu(self): keras_model = Sequential() keras_model.add(PReLU(input_shape=(3, 32, 32), shared_axes=(2, 3), name='prelu')) keras_model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.SGD()) pytorch_model = PReLUNet() self.transfer(keras_model, pytorch_model) self.assertEqualPrediction(keras_model, pytorch_model, self.test_data)
示例9: test_prelu# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def test_prelu(): layer_test(layers.PReLU, kwargs={}, input_shape=(2, 3, 4))
示例10: test_prelu_share# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def test_prelu_share(): layer_test(layers.PReLU, kwargs={'shared_axes': 1}, input_shape=(2, 3, 4))
示例11: activate# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def activate(self, layer): """ activate layer with given activation function :param layer: the input layer :return: the layer after activation """ if self.activ == 'lrelu': return layers.LeakyReLU(0.2)(layer) elif self.activ == 'prelu': return layers.PReLU()(layer) else: return Activation(self.activ)(layer)
示例12: activate# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def activate(self, layer): """ activate layer with given activation function :param layer: the input layer :return: the layer after activation """ if self.activ == 'lrelu': return layers.LeakyReLU()(layer) elif self.activ == 'prelu': return layers.PReLU()(layer) else: return Activation(self.activ)(layer)
开发者ID:CongBao，项目名称:ImageEnhancer，代码行数:13，代码来源:enhancer.py
示例13: RnnVersion1# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def RnnVersion1( n_recurrent=50, n_filters=30, dropout_rate=0.2, l2_penalty=0.0001,n_capsule = 10, n_routings = 5, capsule_dim = 16): K.clear_session() def conv_block(x, n, kernel_size): x = Conv1D(n, kernel_size, activation='relu') (x) x = Conv1D(n_filters, kernel_size, activation='relu') (x) x_att = AttentionWithContext()(x) x_avg = GlobalAveragePooling1D()(x) x_max = GlobalMaxPooling1D()(x) return concatenate([x_att, x_avg, x_max]) def att_max_avg_pooling(x): x_att = AttentionWithContext()(x) x_avg = GlobalAveragePooling1D()(x) x_max = GlobalMaxPooling1D()(x) return concatenate([x_att, x_avg, x_max]) inputs = Input(shape=(170,)) emb = Embedding(21099, 300, trainable=True)(inputs) # model 0 x0 = BatchNormalization()(emb) x0 = SpatialDropout1D(dropout_rate)(x0) x0 = Bidirectional( CuDNNGRU(n_recurrent, return_sequences=True, kernel_regularizer=l2(l2_penalty), recurrent_regularizer=l2(l2_penalty)))(x0) x0 = Conv1D(n_filters, kernel_size=3)(x0) x0 = PReLU()(x0)# x0 = Dropout(dropout_rate)(x0) x0 = att_max_avg_pooling(x0) # model 1 x1 = SpatialDropout1D(dropout_rate)(emb) x1 = Bidirectional( CuDNNGRU(2*n_recurrent, return_sequences=True, kernel_regularizer=l2(l2_penalty), recurrent_regularizer=l2(l2_penalty)))(x1) x1 = Conv1D(2*n_filters, kernel_size=2)(x1) x1 = PReLU()(x1)# x1 = Dropout(dropout_rate)(x1) x1 = att_max_avg_pooling(x1) x = concatenate([x0, x1],name='concatenate') # fc = Dense(128, activation='sigmoid')(x) outputs = Dense(6, activation='softmax')(x)# , kernel_regularizer=l2(l2_penalty), activity_regularizer=l2(l2_penalty) model = Model(inputs=inputs, outputs=outputs) model.compile(loss='categorical_crossentropy', optimizer='Nadam',metrics =['accuracy']) return model
开发者ID:WeavingWong，项目名称:DigiX_HuaWei_Population_Age_Attribution_Predict，代码行数:51，代码来源:models.py
示例14: RnnVersion1# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def RnnVersion1( n_recurrent=50, n_filters=30, dropout_rate=0.2, l2_penalty=0.0001,n_capsule = 10, n_routings = 5, capsule_dim = 16): K.clear_session() def conv_block(x, n, kernel_size): x = Conv1D(n, kernel_size, activation='relu') (x) x = Conv1D(n_filters, kernel_size, activation='relu') (x) x_att = AttentionWithContext()(x) x_avg = GlobalAveragePooling1D()(x) x_max = GlobalMaxPooling1D()(x) return concatenate([x_att, x_avg, x_max]) def att_max_avg_pooling(x): x_att = AttentionWithContext()(x) x_avg = GlobalAveragePooling1D()(x) x_max = GlobalMaxPooling1D()(x) return concatenate([x_att, x_avg, x_max]) inputs = Input(shape=(100,)) emb = Embedding(9399, 300, trainable=True)(inputs) # model 0 x0 = BatchNormalization()(emb) x0 = SpatialDropout1D(dropout_rate)(x0) x0 = Bidirectional( CuDNNGRU(n_recurrent, return_sequences=True, kernel_regularizer=l2(l2_penalty), recurrent_regularizer=l2(l2_penalty)))(x0) x0 = Conv1D(n_filters, kernel_size=3)(x0) x0 = PReLU()(x0)# x0 = Dropout(dropout_rate)(x0) x0 = att_max_avg_pooling(x0) # model 1 x1 = SpatialDropout1D(dropout_rate)(emb) x1 = Bidirectional( CuDNNGRU(2*n_recurrent, return_sequences=True, kernel_regularizer=l2(l2_penalty), recurrent_regularizer=l2(l2_penalty)))(x1) x1 = Conv1D(2*n_filters, kernel_size=2)(x1) x1 = PReLU()(x1)# x1 = Dropout(dropout_rate)(x1) x1 = att_max_avg_pooling(x1) x = concatenate([x0, x1],name='concatenate') fc = Dense(128, activation='relu')(x) outputs = Dense(6, activation='softmax')(fc)# , kernel_regularizer=l2(l2_penalty), activity_regularizer=l2(l2_penalty) model = Model(inputs=inputs, outputs=outputs) model.compile(loss='categorical_crossentropy', optimizer='Nadam',metrics =['accuracy']) return model
开发者ID:WeavingWong，项目名称:DigiX_HuaWei_Population_Age_Attribution_Predict，代码行数:51，代码来源:rnn_feature.py
示例15: header_code# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import PReLU [as 别名]def header_code(self): return """import kerasfrom keras.models import Modelfrom keras import layersimport keras.backend as Kimport numpy as npfrom keras.layers.core import Lambdaimport tensorflow as tfweights_dict = dict()def load_weights_from_file(weight_file): try: weights_dict = np.load(weight_file, allow_pickle=True).item() except: weights_dict = np.load(weight_file, allow_pickle=True, encoding='bytes').item() return weights_dictdef set_layer_weights(model, weights_dict): for layer in model.layers: if layer.name in weights_dict: cur_dict = weights_dict[layer.name] current_layer_parameters = list() if layer.__class__.__name__ == "BatchNormalization": if 'scale' in cur_dict: current_layer_parameters.append(cur_dict['scale']) if 'bias' in cur_dict: current_layer_parameters.append(cur_dict['bias']) current_layer_parameters.extend([cur_dict['mean'], cur_dict['var']]) elif layer.__class__.__name__ == "Scale": if 'scale' in cur_dict: current_layer_parameters.append(cur_dict['scale']) if 'bias' in cur_dict: current_layer_parameters.append(cur_dict['bias']) elif layer.__class__.__name__ == "SeparableConv2D": current_layer_parameters = [cur_dict['depthwise_filter'], cur_dict['pointwise_filter']] if 'bias' in cur_dict: current_layer_parameters.append(cur_dict['bias']) elif layer.__class__.__name__ == "Embedding": current_layer_parameters.append(cur_dict['weights']) elif layer.__class__.__name__ == "PReLU": gamma = np.ones(list(layer.input_shape[1:]))*cur_dict['gamma'] current_layer_parameters.append(gamma) else: # rot if 'weights' in cur_dict: current_layer_parameters = [cur_dict['weights']] if 'bias' in cur_dict: current_layer_parameters.append(cur_dict['bias']) model.get_layer(layer.name).set_weights(current_layer_parameters) return modeldef KitModel(weight_file = None): global weights_dict weights_dict = load_weights_from_file(weight_file) if not weight_file == None else None """
|
