def get_detect_model():
 (x, conv_layer, conv_vars) = convolutional_layers()
 W_fc1 = weight_variable([((8 * 32) * 128), 2048])
 W_conv1 = tf.reshape(W_fc1, [8, 32, 128, 2048])
 b_fc1 = bias_variable([2048])
 h_conv1 = tf.nn.relu((conv2d(conv_layer, W_conv1, stride=(1, 1), padding='VALID') + b_fc1))
 W_fc2 = weight_variable([2048, (1 + (7 * len(common.CHARS)))])
 W_conv2 = tf.reshape(W_fc2, [1, 1, 2048, (1 + (7 * len(common.CHARS)))])
 b_fc2 = bias_variable([(1 + (7 * len(common.CHARS)))])
 h_conv2 = (conv2d(h_conv1, W_conv2) + b_fc2)
 return (x, h_conv2, (conv_vars + [W_fc1, b_fc1, W_fc2, b_fc2]))

def get_detect_model ( ) : ( x , conv_layer , conv_vars ) = convolutional_layers ( ) W_fc1 = weight_variable ( [ ( ( 8 * 32 ) * 128 ) , 2048 ] ) W_conv1 = tf . reshape ( W_fc1 , [ 8 , 32 , 128 , 2048 ] ) b_fc1 = bias_variable ( [ 2048 ] ) h_conv1 = tf . nn . relu ( ( conv2d ( conv_layer , W_conv1 , stride = ( 1 , 1 ) , padding = 'VALID' ) + b_fc1 ) ) W_fc2 = weight_variable ( [ 2048 , ( 1 + ( 7 * len ( common . CHARS ) ) ) ] ) W_conv2 = tf . reshape ( W_fc2 , [ 1 , 1 , 2048 , ( 1 + ( 7 * len ( common . CHARS ) ) ) ] ) b_fc2 = bias_variable ( [ ( 1 + ( 7 * len ( common . CHARS ) ) ) ] ) h_conv2 = ( conv2d ( h_conv1 , W_conv2 ) + b_fc2 ) return ( x , h_conv2 , ( conv_vars + [ W_fc1 , b_fc1 , W_fc2 , b_fc2 ] ) )
the same as the training model .