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