kernel_regularizer=regularizers.l2(0.0), input_shape=(X_train.shape[1],) ) ) model.add(Dense(2,activation=act_func, kernel_initializer='glorot_uniform')) model.add(Dense(10,activation=act_func, kernel_initializer='glorot_uniform')) model.add(Dense(X_train.shape[1], kernel_initializer='g...
kernel_initializer='glorot_uniform', kernel_regularizer=regularizers.l2(0.0), input_shape=(X_train.shape[1],) ) ) model.add(Dense(2,activation=act_func, kernel_initializer='glorot_uniform')) model.add(Dense(10,activation=act_func, ...
kernel_regularizer=regularizers.l2(0.0), input_shape=(X_train.shape[1],) ) ) model.add(Dense(2,activation=act_func, kernel_initializer='glorot_uniform')) model.add(Dense(10,activation=act_func, kernel_initializer='glorot_uniform')) model.add(Dense(X_train.shape[1], kernel_initializer='g...
model = Sequential() model.add(Dense(50, input_shape=(8, ), kernel_initializer='uniform', activation=activation)) model.add(Dropout(0.2)) model.add(Dense(1, kernel_initializer='uniform', activation=activation)) # 编译模型 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=...
) # units: 隐藏层个数, activation: 激活函数 kernel_initializer: 初始化权重 input_dim: 输入层神经元个数 # 添加第二个隐藏层 classifier.add( Dense(units=6, activation="relu", kernel_initializer="uniform") ) # units: 隐藏层个数, activation: 激活函数 kernel_initializer: 初始化权重 input_dim...
kernel_initializer是一个用于初始化权重矩阵的函数,默认情况下,它可能是'glorot_uniform'或其他随机初始化方法。 bias_initializer (函数) bias_initializer是一个用于初始化偏置的函数,默认通常是'zeros',意味着所有偏置开始时都是0。 高级选项 除了基本参数外,还可以通过以下选项进一步定制Dense层的行为: ...
kernel_initializer='he_normal', use_bias=False, bias_initializer='zeros', name = "se_block_two_"+str(name))(se_feature) se_feature = Activation('sigmoid')(se_feature) se_feature = multiply([input_feature, se_feature]) return se_feature ...
kernel_initializer: kernel权值矩阵(Conv1D)的初始化器。 use_batch_norm: 是否在使用批处理规范化。 kwargs: 用于配置父类层的任何其他参数。 在完成了TCN层的设置之后,后面接一层一个输出的全连接层进行输出最后的预测结果,其中用到的激活函数是relu。最后对模型进行编译以及训练,训练的loss是mae,优化器是adam...
dtype=dtype, shape=shape)classOnes(Initializer):"""Initializer that generates tensors initialized to 1."""def__init__(self, dtype=dtypes.float32): self.dtype = dtypedef__call__(self, shape, dtype=None, partition_info=None):ifdtypeisNone: ...
(num_pixels,), kernel_initializer='normal', activation='sigmoid'),ka.layers.Dense(784, kernel_initializer='normal', activation='sigmoid'),ka.layers.Dense(num_classes, kernel_initializer='normal', activation='sigmoid')])model.summary()#model.compile(loss='mse', optimizer='sgd', metrics=['...