python - TimeDistributed 一次多个层

Question

我编写了以下脚本，用于读取 CNN-RNN-FCN NN 架构的 yaml 规范并构建相应的 Keras 模型:

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 27 10:22:03 2018

@author: jsevillamol
"""

import yaml, argparse
from contextlib import redirect_stdout

from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Input 
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, Flatten
from tensorflow.python.keras.layers import TimeDistributed, LSTM
from tensorflow.python.keras.layers import Dense
from tensorflow.python.keras.layers import BatchNormalization, Dropout

def build_model(
        input_shape, 
        num_classes, 
        data_type,
        activation_function, 
        dropout_rate,
        use_batchnorm,
        cnn_layers,
        lstm_units,
        concat_lstm_output,
        fcn_layers):
    """
    Builds a CNN-RNN-FCN model according to some specs
    """

    # Build a model with the functional API
    inputs = Input(input_shape)
    x = inputs

    # CNN feature extractor    
    for i, cnn_layer in enumerate(cnn_layers):
        # Extract layer params
        filters = cnn_layer['filters']
        kernel_size = cnn_layer['kernel_size']
        use_maxpool = cnn_layer['use_maxpool']

        # build cnn_layer
        x = TimeDistributed(Conv2D(
                filters, 
                kernel_size, 
                strides=(1, 1), 
                padding='same', 
                data_format=None, 
                dilation_rate=(1, 1), 
                activation=activation_function, 
                use_bias=True, 
                kernel_initializer='glorot_uniform', 
                bias_initializer='zeros', 
                kernel_regularizer=None, 
                bias_regularizer=None, 
                activity_regularizer=None, 
                kernel_constraint=None, 
                bias_constraint=None
            ), name=f'conv2D_{i}')(x)

        if use_batchnorm:
            x = TimeDistributed(BatchNormalization(
                    axis=-1, 
                    momentum=0.99, 
                    epsilon=0.001, 
                    center=True, 
                    scale=True, 
                    beta_initializer='zeros', 
                    gamma_initializer='ones', 
                    moving_mean_initializer='zeros', 
                    moving_variance_initializer='ones', 
                    beta_regularizer=None, 
                    gamma_regularizer=None, 
                    beta_constraint=None, 
                    gamma_constraint=None
                ), name=f'batchnorm_{i}')(x)


        # add maxpool if needed
        if use_maxpool:
            x = TimeDistributed(MaxPooling2D(
                    pool_size=(2, 2), 
                    strides=None, 
                    padding='valid', 
                    data_format=None
                ), name=f'maxpool_{i}')(x)

    x = TimeDistributed(Flatten(), name='flatten')(x)
    x = TimeDistributed(Dropout(dropout_rate), name='dropout')(x)

    # LSTM feature combinator
    x = LSTM(
            lstm_units, 
            activation='tanh', 
            recurrent_activation='hard_sigmoid', 
            use_bias=True, 
            kernel_initializer='glorot_uniform', 
            recurrent_initializer='orthogonal', 
            bias_initializer='zeros', 
            unit_forget_bias=True, 
            kernel_regularizer=None, 
            recurrent_regularizer=None, 
            bias_regularizer=None, 
            activity_regularizer=None, 
            kernel_constraint=None, 
            recurrent_constraint=None, 
            bias_constraint=None, 
            dropout=dropout_rate, 
            recurrent_dropout=0.0, 
            implementation=1, 
            return_sequences=concat_lstm_output, 
            return_state=False, 
            go_backwards=False, 
            stateful=False, 
            unroll=False
        )(x)

    if concat_lstm_output:
        x = Flatten()(x)

    # FCN classifier    
    for fcn_layer in fcn_layers:
        # extract layer params
        units = fcn_layer['units']

        # build layer
        x = Dense(
                units, 
                activation=activation_function, 
                use_bias=True, 
                kernel_initializer='glorot_uniform', 
                bias_initializer='zeros', 
                kernel_regularizer=None, 
                bias_regularizer=None, 
                activity_regularizer=None, 
                kernel_constraint=None, 
                bias_constraint=None
            )(x)

        x = Dropout(dropout_rate)(x)


    prediction = Dense(num_classes, activation='softmax')(x)

    # Build model
    model = Model(inputs=inputs, outputs=prediction)

    return model

if __name__=="__main__":
    # parser options
    parser = argparse.ArgumentParser(
            description=("Build a customized cnn-rnn keras model with ctalearn."))

    parser.add_argument(
            'config_file',
            help="path to YAML file containing a training configuration")

    args = parser.parse_args()

    # load config file
    with open(args.config_file, 'r') as config_file:
        config = yaml.load(config_file)

    model = build_model(**config['model_config'])

    # Show model summary through console and then save it to file
    model.summary()

    with open('model_summary.txt', 'w') as f:
        with redirect_stdout(f):
            model.summary()

    # save model architecture to disk in .h5 format
    model.save('untrained_model.h5', include_optimizer=False)

我想向程序添加一个新功能，该功能将允许构建接受形状为(img_length, img_height, n_channels)作为输入示例的模型，即每个示例一个图像，而不是当前的序列。

为此，如果能够在构建模型的 CNN 部分后立即应用 TimeDistributed 包装器，那就太棒了，这样我就不必在各处添加大量条件。

我怎样才能做到这一点？

Answer 1

单个图像可以被视为长度为一的序列。因此，您可以通过简单的检查并使用 Reshape 层轻松完成此操作:

inputs = Input(input_shape)
x = inputs

# if the input is a single image,
# reshape it to a sequence of length one
if len(input_shape) == 3:
    x = Reshape((1,) + input_shape)(x)

# the rest is the same