keras mobilenet

mobilenet

import numpy as np
import keras
from keras.models import load_model
from keras.applications import InceptionV3, mobilenet
import tensorflow as tf
import os
import os.path as osp
from keras import backend as K
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import graph_io
from tensorflow.python.keras._impl.keras.applications import mobilenet
from tensorflow.python.keras._impl.keras.models import load_model



input_fld = './'
weight_file = 'model.17–1.23.hdf5'
num_output = 1
write_graph_def_ascii_flag = True
prefix_output_node_names_of_final_network = 'output_node'
output_graph_name = 'keras_mobilenetv1.pb'

output_fld = input_fld + 'tensorflow_model/'
if not os.path.isdir(output_fld):
    os.mkdir(output_fld)
weight_file_path = osp.join(input_fld, weight_file)
K.set_learning_phase(0)


net_model=keras.applications.mobilenet.MobileNet(input_shape=(192, 192, 3), alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=False, weights='imagenet', input_tensor=None, pooling=None, classes=1000)

pred = [None] * num_output
pred_node_names = [None] * num_output
for i in range(num_output):
    pred_node_names[i] = prefix_output_node_names_of_final_network + str(i)
    pred[i] = tf.identity(net_model.output[i], name=pred_node_names[i])

sess = K.get_session()
constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), pred_node_names)
graph_io.write_graph(constant_graph, output_fld, output_graph_name, as_text=False)