Python 一次对多个层进行时间分配
我编写了以下脚本，读取CNN-RNN-FCN NN体系结构的Python 一次对多个层进行时间分配,python,machine-learning,keras,neural-network,conv-neural-network,Python,Machine Learning,Keras,Neural Network,Conv Neural Network,我编写了以下脚本，读取CNN-RNN-FCN NN体系结构的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
yaml#!/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)
（img\u长度、img\u高度、n\u通道）TimeDistributed我怎样才能做到这一点呢？单个图像可以被视为长度为1的序列。因此，通过简单的检查和使用
重塑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
单个图像可以被视为长度为1的序列。因此，通过简单的检查和使用
重塑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
因此，当前的
输入形状（n\u帧，w，h，c）（w，h，c）输入形状（n\u frames，w，h，c）（w，h，c）输入的\u形状（n\u frames，w，h，c）（w，h，c）输入_形（n_帧，w，h，c）（w，h，c）