本文整理汇总了Python中tensorflow.keras.regularizers方法的典型用法代码示例。如果您正苦于以下问题：Python keras.regularizers方法的具体用法？Python keras.regularizers怎么用？Python keras.regularizers使用的例子？那么, 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类tensorflow.keras的用法示例。
在下文中一共展示了keras.regularizers方法的3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，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: model
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import regularizers [as 别名]
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 示例2: model
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import regularizers [as 别名]
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 示例3: model
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import regularizers [as 别名]
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