Sequential意为顺序的,即序贯模型。 network.add(layers.Dense(512, activation='relu', input_shape=(28*28,)))# 第一层需要加一个input_shape关键字参数 network.add(layers.Dense(10, activation='softmax'))# ①输出层的units=10,意味着存在10个类别,实际意义为输出的结果是从0~10这是个数字。②我们想...
(48, 48, 3)) for layer in model_vgg.layers: layer.trainable = False model = Dense(4096, activation='relu', name='fc1')(model_vgg.output) model = Dense(4096, activation='relu', name='fc2')(model) model = Dropout(0.5)(model) model = Dense(10, activation='softmax')(model) ...
相反,如果您调用tf.keras.layers.Dense(),tensorflow基本上会创建一个新的密集层并将其返回给您,然后您可以使用它来处理数据。实际上,您可以将其分为两行,以使其更加清晰:dense_layer = layers.Dense(128, activation='relu') # We define a new dense layer dense_layer_output = dense_layer(pretrained_mod...
Sequential:顾名思义就是个顺序模型,Dense定义一个一个隐藏层。Flatten 其实就是把二位变成一维输入 model = keras.models.Sequential() model.add(keras.layers.Flatten(input_shape=[28, 28])) model.add(keras.layers.Dense(300, activation="relu")) ...
Dense(units=1, activation=OutputLayerActMethod)]) # 最后一层就是输出层 Model.compile(loss=LossMethod, # 指定每个批次训练误差的减小方法 optimizer=tf.keras.optimizers.Adam(learning_rate=LearnRate,decay=LearnDecay)) # 运用学习率下降的优化方法 return Model # Draw error image. def LossPlot(History...
inputs = Input(shape=(128,))layer1 = Dense(64, activation='relu')(inputs)layer2 = Dense(64, activation='relu')(layer1)predictions = Dense(10, activation='softmax')(layer2)model = Model(inputs=inputs, outputs=predictions)# Define custom loss ...
dense = layers.Dense(64, activation="relu") x = dense(inputs) 1. 2. “图层调用”操作就像从“输入”到创建的该图层绘制箭头。 您将输入“传递”到密集层(dense ),然后得到x。 让我们在层图中添加更多层: x = layers.Dense(64, activation="relu")(x) ...
self.dense1 = tf.keras.layers.Dense(units=1024, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(units=10) def call(self, inputs, training=False): # call中也可以增加一个training参数,对不同过程(训练、测试)进行特殊操作
class IrisClassifier(Model):def __ini0, activation='relu')self.layer2 = Dense(10, activation='relu')self.outputLayer = Dense(3, activation='softmax')def call(self, x):x = self.layer1(x)x = self.layer2(x)returnmodel.compile(optimizer=tf.keras.optimizers.Adam(),loss='categorical_cros...
decode = Bidirectional(LSTM(55, activation = 'tanh', return_sequences = True, kernel_regularizer=l2(0.01)))(decode) decode = TimeDistributed(Dense(6, activation="softmax"), name="dec1")(decode) new_model = Model(inputs=inp1, outputs = decode) new_model.compile(loss= 'categorical_cross...