我在现有模型的最后一层有一个双层双向循环堆栈，它被训练用于从常量大小的图像中提取文本。我在重复堆栈之前向现有架构添加了一个额外的卷积层，以便在较大的图像上启用文本检测。该模型如下所示：
Layer (type) Output Shape Param #
=================================================================
the_input (InputLayer) (None, 64, 128, 3) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 64, 128, 48) 3648
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 32, 64, 48) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 32, 64, 64) 76864
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 16, 64, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 16, 64, 128) 204928
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 8, 32, 128) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 1, 1, 2048) 67110912
_________________________________________________________________
lambda_1 (Lambda) (None, 1, 1, 1) 0
=================================================================
conv2d_4是新的卷积层，用于启用大小不是128 x 64的图像.lambda_1是一个存在指示符变量，每个滑动窗口位置有一个值。创建以下模型的代码如下：
input_data = Input(name='the_input', shape=input_shape)
def add_conv(prev_component, kernel_dims, num_filters, max_pool_dims=None, padding='same'):
cnv = Conv2D(num_filters, kernel_dims, activation='relu',
padding=padding)(prev_component)
if max_pool_dims is not None:
cnv = MaxPooling2D(pool_size=max_pool_dims)(cnv)
return cnv
prev = input_data
prev = add_conv(prev, (5, 5), 48, max_pool_dims=(2, 2))
prev = add_conv(prev, (5, 5), 64, max_pool_dims=(2, 1))
prev = add_conv(prev, (5, 5), 128, max_pool_dims=(2, 2))
#[kernel_height, kernel_width, prev_filter_count, new_filter_count]
prev = add_conv(prev, (8, 32), time_dense_size, padding='valid')
def is_plate_func(windowed_data):
is_plate_w = K.variable(K.truncated_normal(stddev=0.1, shape=(1, 1, 2048, 1)))
is_plate_b = K.variable(K.constant(.1, shape=[1]))
is_plate_out = K.bias_add(K.conv2d(windowed_data, is_plate_w), is_plate_b)
return is_plate_out
is_plate_lambda = Lambda(is_plate_func)(prev)
Model(inputs=input_data, outputs=[is_plate_lambda]).summary()
我希望以与lambda_1相同的方式附加到模型的循环堆栈的代码如下：
bdrnn_model = Sequential()
bdrnn_model.add(Reshape((2048, 1), input_shape=(1, 1, 2048)))
for idx in range(num_backward_layers):
if idx == num_backward_layers - 1:
merge_mode = 'concat'
else:
merge_mode = 'sum'
bdrnn_model.add(Bidirectional(GRU(rnn_size, return_sequences=True),
merge_mode=merge_mode))
bdrnn_model.add(Dense(num_output_characters + 2, activation='relu'))
有没有办法让Keras使用'bdrnn_model'作为卷积滤波器？