keras.layers.core.Dense(units,activation=None,use_bias=True,kernel_initializer='glorot_uniform',bias_initializer='zeros',kernel_regularizer=None,bias_regularizer=None,activity_regularizer=None,kernel_constraint=None,bias_constraint=None) 参数: units:大于0的整数,代表该层的输出维度。 use_bias:布尔值,是...
keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None) # 作为 Sequential 模型的第一层model = Sequential() model....
model.add(Dense(output_dim=1, input_dim=1)) 而这里采用另一种方法,在Sequential()定义的时候通过列表添加神经层。同时需要注意,这里增加了神经网络激励函数并调用RMSprop加速神经网络。 from keras.layers import Dense, Activation from keras.optimizers import RMSprop 该神经网络层为: 第一层为Dense(32, input...
tf.keras.layers.Activation():激活函数层。一般放在Dense层后面,等价于在Dense层中指定activation。 tf.keras.layers.Dropout():随机置零层。训练期间以一定几率将输入置0,一种正则化手段。 tf.keras.layers.BatchNormalization():批标准化层。通过线性变换将输入批次缩放平移到稳定的均值和标准差。可以增强模型对输入...
1.1、Dense层(全连接层) keras.layers.core.Dense(units,activation=None,use_bias=True,kernel_initializer='glorot_uniform',bias_initializer='zeros',kernel_regularizer=None,bias_regularizer=None,activity_regularizer=None,kernel_constraint=None,bias_constraint=None) ...
tf.keras.layers.Dense( units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs ...
1.1 Dense层 代码语言:javascript 复制 keras.layers.core.Dense(output_dim,init='glorot_uniform',activation='linear',weights=None,W_regularizer=None,b_regularizer=None,activity_regularizer=None,W_constraint=None,b_constraint=None,bias=True,input_dim=None) ...
from keras.layers import Dense,Activation,Convolution2D,MaxPooling2D,Flatten 导入可用于处理全连接层,激活函数,二维卷积,最大池化,压平数据包 from keras.optimizers import Adam 导入优化损失方法 构建模型: model = Sequential() 二.构建CNN结构 上图为一个卷积层的示意图,可以知道,卷积层需要突触权值,偏置(可...
feature_layer = tf.keras.layers.DenseFeatures(columns) features = tf.io.parse_example( ..., features=tf.feature_column.make_parse_example_spec(columns)) dense_tensor = feature_layer(features)forunitsin[128,64,32]: dense_tensor = tf.keras.layers.Dense(units, activation='relu')(dense_tensor...
import tensorflow as tfX = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])y = tf.constant([[10.0], [20.0]])# 1、构建线性模型class Linear(tf.keras.Model):def __init__(self):super().__init__()self.dense = tf.keras.layers.Dense(units=1,activation=None,kernel_initializer=tf....