如何使用keras根据层名称来初始化网络-创新互联

这篇文章主要为大家展示了如何使用keras根据层名称来初始化网络，内容简而易懂，希望大家可以学习一下，学习完之后肯定会有收获的，下面让小编带大家一起来看看吧。

keras根据层名称来初始化网络

def get_model(input_shape1=[75, 75, 3], input_shape2=[1], weights=None):
 bn_model = 0
 trainable = True
 # kernel_regularizer = regularizers.l2(1e-4)
 kernel_regularizer = None
 activation = 'relu'

 img_input = Input(shape=input_shape1)
 angle_input = Input(shape=input_shape2)

 # Block 1
 x = Conv2D(64, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block1_conv1')(img_input)
 x = Conv2D(64, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block1_conv2')(x)
 x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

 # Block 2
 x = Conv2D(128, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block2_conv1')(x)
 x = Conv2D(128, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block2_conv2')(x)
 x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

 # Block 3
 x = Conv2D(256, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block3_conv1')(x)
 x = Conv2D(256, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block3_conv2')(x)
 x = Conv2D(256, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block3_conv3')(x)
 x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

 # Block 4
 x = Conv2D(512, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block4_conv1')(x)
 x = Conv2D(512, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block4_conv2')(x)
 x = Conv2D(512, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block4_conv3')(x)
 x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

 # Block 5
 x = Conv2D(512, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block5_conv1')(x)
 x = Conv2D(512, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block5_conv2')(x)
 x = Conv2D(512, (3, 3), activation=activation, padding='same',
    trainable=trainable, kernel_regularizer=kernel_regularizer,
    name='block5_conv3')(x)
 x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

 branch_1 = GlobalMaxPooling2D()(x)
 # branch_1 = BatchNormalization(momentum=bn_model)(branch_1)

 branch_2 = GlobalAveragePooling2D()(x)
 # branch_2 = BatchNormalization(momentum=bn_model)(branch_2)

 branch_3 = BatchNormalization(momentum=bn_model)(angle_input)

 x = (Concatenate()([branch_1, branch_2, branch_3]))
 x = Dense(1024, activation=activation, kernel_regularizer=kernel_regularizer)(x)
 # x = Dropout(0.5)(x)
 x = Dense(1024, activation=activation, kernel_regularizer=kernel_regularizer)(x)
 x = Dropout(0.6)(x)
 output = Dense(1, activation='sigmoid')(x)

 model = Model([img_input, angle_input], output)
 optimizer = Adam(lr=1e-5, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=0.0)
 model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

 if weights is not None:
  # 将by_name设置成True
  model.load_weights(weights, by_name=True)
  # layer_weights = h6py.File(weights, 'r')
  # for idx in range(len(model.layers)):
  #  model.set_weights()
 print 'have prepared the model.'

 return model