Question

我编写了以下脚本，该脚本读取了CNN-RNN-FCN NN体系结构的yaml规范并构建了相应的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)

我想在程序中添加一个新功能，该功能允许构建模型，这些模型接受形状为(img_length, img_height, n_channels)的输入示例，即每个示例一个图像，而不是当前的顺序。

为此，在构建模型的CNN部分之后能够一次全部应用TimeDistributed包装器真是太棒了，因此我不必到处添加很多条件。

我该怎么做？

Answer 1

单个图像可以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层轻松地做到这一点：

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

TimeDistributed-一次多层

1 个答案: