當前位置: 首頁>>代碼示例>>Python>>正文

Python keras.regularizers方法代碼示例

本文整理匯總了Python中tensorflow.keras.regularizers方法的典型用法代碼示例。如果您正苦於以下問題：Python keras.regularizers方法的具體用法？Python keras.regularizers怎麽用？Python keras.regularizers使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.keras的用法示例。


在下文中一共展示了keras.regularizers方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: model

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import regularizers [as 別名]
def model(self):
        input_tensor = Input(shape=self._input_shape['input'], name='input')
        cnn_layer_1 = Conv1D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[0],
                             kernel_size=self.filter_len, kernel_regularizer=regularizers.l2(self.l2))(input_tensor)
        activation_1 = Activation(activation=self.activation)(cnn_layer_1)
        cnn_layer_2 = Conv1D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[1],
                             kernel_size=self.filter_len, kernel_regularizer=regularizers.l2(self.l2))(activation_1)
        activation_2 = Activation(activation=self.activation)(cnn_layer_2)
        maxpool_1 = MaxPooling1D(pool_size=self.pool_length)(activation_2)
        flattener = Flatten()(maxpool_1)
        dropout_1 = Dropout(self.dropout_rate)(flattener)
        layer_3 = Dense(units=self.num_hidden[0], kernel_regularizer=regularizers.l2(self.l2),
                        kernel_initializer=self.initializer)(dropout_1)
        activation_3 = Activation(activation=self.activation)(layer_3)
        dropout_2 = Dropout(self.dropout_rate)(activation_3)
        layer_4 = Dense(units=self.num_hidden[1], kernel_regularizer=regularizers.l2(self.l2),
                        kernel_initializer=self.initializer)(dropout_2)
        activation_4 = Activation(activation=self.activation)(layer_4)
        layer_5 = Dense(units=self._labels_shape['output'])(activation_4)
        output = Activation(activation=self._last_layer_activation, name='output')(layer_5)

        model = Model(inputs=input_tensor, outputs=output)

        return model
開發者ID:henrysky，項目名稱:astroNN，代碼行數:26，代碼來源:apogee_models.py

示例2: model

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import regularizers [as 別名]
def model(self):
        input_tensor = Input(shape=self._input_shape, name='input')
        cnn_layer_1 = Conv2D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[0],
                             kernel_size=self.filter_len, kernel_regularizer=regularizers.l2(self.l2))(input_tensor)
        activation_1 = Activation(activation=self.activation)(cnn_layer_1)
        cnn_layer_2 = Conv2D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[1],
                             kernel_size=self.filter_len, kernel_regularizer=regularizers.l2(self.l2))(activation_1)
        activation_2 = Activation(activation=self.activation)(cnn_layer_2)
        maxpool_1 = MaxPooling2D(pool_size=self.pool_length)(activation_2)
        flattener = Flatten()(maxpool_1)
        dropout_1 = MCDropout(0.2, disable=True)(flattener)
        layer_3 = Dense(units=self.num_hidden[0], kernel_regularizer=regularizers.l2(self.l2),
                        kernel_initializer=self.initializer)(dropout_1)
        activation_3 = Activation(activation=self.activation)(layer_3)
        dropout_2 = MCDropout(0.2, disable=True)(activation_3)
        layer_4 = Dense(units=self.num_hidden[1], kernel_regularizer=regularizers.l2(self.l2),
                        kernel_initializer=self.initializer, kernel_constraint=max_norm(2))(dropout_2)
        activation_4 = Activation(activation=self.activation)(layer_4)
        layer_5 = Dense(units=self._labels_shape)(activation_4)
        output = Activation(activation=self._last_layer_activation, name='output')(layer_5)

        model = Model(inputs=input_tensor, outputs=output)

        return model
開發者ID:henrysky，項目名稱:astroNN，代碼行數:26，代碼來源:custom_models.py

示例3: model

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import regularizers [as 別名]
def model(self):
        input_tensor = Input(shape=self._input_shape['input'], name='input')
        cnn_layer_1 = Conv2D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[0],
                             kernel_size=self.filter_len, kernel_regularizer=regularizers.l2(self.l2))(input_tensor)
        activation_1 = Activation(activation=self.activation)(cnn_layer_1)
        cnn_layer_2 = Conv2D(kernel_initializer=self.initializer, padding="same", filters=self.num_filters[1],
                             kernel_size=self.filter_len, kernel_regularizer=regularizers.l2(self.l2))(activation_1)
        activation_2 = Activation(activation=self.activation)(cnn_layer_2)
        maxpool_1 = MaxPooling2D(pool_size=self.pool_length)(activation_2)
        flattener = Flatten()(maxpool_1)
        dropout_1 = Dropout(self.dropout_rate)(flattener)
        layer_3 = Dense(units=self.num_hidden[0], kernel_regularizer=regularizers.l2(self.l2),
                        kernel_initializer=self.initializer)(dropout_1)
        activation_3 = Activation(activation=self.activation)(layer_3)
        dropout_2 = Dropout(self.dropout_rate)(activation_3)
        layer_4 = Dense(units=self.num_hidden[1], kernel_regularizer=regularizers.l2(self.l2),
                        kernel_initializer=self.initializer, kernel_constraint=MaxNorm(2))(dropout_2)
        activation_4 = Activation(activation=self.activation)(layer_4)
        layer_5 = Dense(units=self._labels_shape['output'])(activation_4)
        output = Activation(activation=self._last_layer_activation, name='output')(layer_5)

        model = Model(inputs=input_tensor, outputs=output)

        return model


# noinspection PyCallingNonCallable
開發者ID:henrysky，項目名稱:astroNN，代碼行數:29，代碼來源:misc_models.py


注：本文中的tensorflow.keras.regularizers方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。