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_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_initializer='glorot_uniform')) model.add(Dense(X_tr...
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_regul...
model = Sequential() model.add(Dense(50, input_shape=(8, ), kernel_initializer='uniform', activation='relu')) model.add(Dropout(0.05)) model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid')) # 编译模型 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=...
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01)) pool4=tf.layers.max_pooling2d(inputs=conv4, pool_size=[2, 2], strides=2) re1 = tf.reshape(pool4, [-1, 6 * 6 * 128]) #全连接层 dense1 = tf.layers.dense(inputs=re1, ...
Dense(units=6, activation="relu", kernel_initializer="uniform", input_dim=11) ) # 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") ...
tensor shape, the initializer will raise a `ValueError`. Args: value: A Python scalar, list of values, or a N-dimensional numpy array. All elements of the initialized variable will be set to the corresponding value in the `value` argument. ...
kernel_initializer=‘glorot_uniform’, bias_initializer=‘zeros’, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs) filters:过滤器数量,对应输出的通道数 kernel_size:过滤器的尺寸,好像网上大多数教程说2维卷积其尺寸是二维的...
(input_dim=13))# 输入层 批标准化model.add(Dense(k,kernel_initializer='random_uniform',# 均匀初始化activation='relu',# relu激活函数kernel_regularizer=regularizers.l1_l2(l1=0.01,l2=0.01),# L1及L2 正则项use_bias=True))# 隐藏层model.add(Dropout(0.1))# dropout法model.add(Dense(1,use_...
1 Pool([numprocess [,initializer [, initargs]]]):创建进程池 (2):参数介绍 1 numprocess:要创建的进程数,如果省略,将默认使用cpu_count()的值 2 initializer:是每个工作进程启动时要执行的可调用对象,默认为None 3 initargs:是要传给initializer的参数组 ...