model.add(Flatten()) # add fully connected layer with 128 hidden units model.add(Dense(128)) model.add(Activation('relu')) model.add(Dropout(0.5)) # output layer with softmax model.add(Dense(nb_classes)) model.
smodel.add(Conv2D(filters=128, kernel_size=(3, 3), activation=’relu’)) smodel.add(MaxPool2D((2, 2))) smodel.add(Flatten()) smodel.add(Dense(256, activation=’relu’)) smodel.add(Dense(256, activation=’relu’)) smodel.add(Dense(12, activation=’softmax’)) optimizer = Adam...
m2_dense_layer_1 = Dense(32, activation='relu')(m2_input_layer) m2_dense_layer_2 = Dense(16, activation='relu')(m2_input_layer) m2_merged_layer = Concatenate()([m2_dense_layer_1, m2_dense_layer_2]) m2_final_layer = Dense(output_classes, activation='softmax')(m2_merged_layer)...
model = Sequential()# Dense(64) is a fully-connected layer with 64 hidden units.# in the first layer, you must specify the expected input data shape:# here, 20-dimensional vectors.model.add(Dense(64, activation='relu', input_dim=20)) model.add(Dropout(0.5)) model.add(Dense(64, acti...
(x) x = layers.Dropout(0.25)(x) outputs = layers.Dense(10, activation='softmax')...
model = Sequential()# Dense(64) is a fully-connected layer with 64 hidden units.# in the first layer, you must specify the expected input data shape:# here, 20-dimensional vectors.model.add(Dense(64, activation='relu', input_dim=20)) ...
model.add(layers.Dense(46, activation="softmax")) # 46就是最终的分类数目 对比二分类问题,有3个需要注意的点: 网络的第一层输入的𝑠ℎ𝑎𝑝𝑒shape为𝑥𝑡𝑟𝑎𝑖𝑛xtrain的𝑠ℎ𝑎𝑝𝑒shape第二个值 网络的最后一个层是4646的𝐷𝑒𝑛𝑠𝑒Dense层(标签有46个类别);网络输...
model = Sequential([ Dense(32, units=784), Activation('relu'), Dense(10), Activation('softmax'), ]) (2)、也可以通过.add()方法一个个的将layer加入模型中: model = Sequential() model.add(Dense(32, input_shape=(784,))) model.add(Activation('relu')) ...
activation='tanh') ) # add output layer model.add( keras.layers.Dense( units=y_train_onehot.shape[1], input_dim=50, kernel_initializer='glorot_uniform', bias_initializer='zeros', activation='softmax') ) # define SGD optimizer sgd_optimizer = keras.optimizers.SGD( ...
model.add(Dense(1000)) model.add(Activation("relu")) model.add(Dropout(0.5)) model.add(Dense(classes)) model.add(Activation("softmax")) return model # Define model model = Convnet.build(width=28, height=28, depth=1, classes=3755) # Input size and catogries ...