keras根据层名称来初始化网络

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 = h5py.File(weights, 'r')
        # for idx in range(len(model.layers)):
        #     model.set_weights()
    print 'have prepared the model.'

    return model