Я работаю над классификацией изображений, и мне нужен совет.Я строю классификатор больших изображений, используя keras с внутренним тензорным потоком, потому что я хочу использовать эту модель для приложения Android, поэтому я обучил ее и преобразовал эти контрольные точки в файл protobuf (.pb).Во многих примерах я видел 2 файла, когда они экспортировали в папку ресурсов Android, это файл protobuf и файл label.txt, так что можно использовать только файл protobuf на Android?Спасибо!
и вот исходный код
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import keras
from keras.models import Sequential
from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout,Activation
from keras.optimizers import Adam
from keras.callbacks import TensorBoard
from keras.layers.core import Lambda
from keras.optimizers import Adam
import keras
import keras.backend as k
import tensorflow as tf
from tensorflow.python.framework import graph_util
print(keras.__version__)
print(tf.__version__)
import os
train_df = pd.read_csv('fashionmnist/fashion-mnist_train.csv',sep=',')
test_df = pd.read_csv('fashionmnist/fashion-mnist_test.csv',sep=',')
train_data =np.array(train_df,dtype = 'float32')
test_data = np.array(test_df,dtype = 'float32')
x_train = train_data[:,1:]/255
y_train = train_data[:,0]
x_test = train_data[:,1:]/255
y_test = train_data[:,0]
x_train,x_validate,y_train,y_validate=train_test_split(x_train,y_train,test_size = 0.2,random_state = 12345)
image = x_train[50,:].reshape((28,28))
plt.imshow(image)
plt.show()
image_rows =28
image_cols= 28
batch_size =100
image_shape =(image_rows,image_cols,1)
x_train = x_train.reshape(x_train.shape[0],*image_shape)
x_test = x_test.reshape(x_test.shape[0],*image_shape)
x_validate = x_validate.reshape(x_validate.shape[0],*image_shape)
def build_network(is_training=True):
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=image_shape, padding='same',name="1_conv"))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3), padding='same',name="2_conv"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),name="1_pool"))
model.add(Conv2D(64, (3, 3), padding='same',name="3_conv"))
model.add(Activation('relu'))
model.add(Conv2D(64,(3, 3), padding='same',name="4_conv"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),name="2_pool"))
model.add(Conv2D(128,(3, 3),padding='same',name="5_conv"))
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3),padding='same',name="6_conv"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),name="3_pool"))
model.add(Conv2D(256,(3, 3), padding='same',name="7_conv"))
model.add(Activation('relu'))
model.add(Conv2D(256, (3, 3), padding='same',name="8_conv"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),name="4_pool"))
model.add(Flatten())
model.add(Dense(512,name="fc_1"))
model.add(Activation('relu'))
if (is_training):
#model.add(Dense(512, activation='relu'))
#model.add(Dropout(0.5, name="drop_1"))
model.add(Lambda(lambda x:k.dropout(x,level=0.5),name="drop_1"))
model.add(Dense(10,name="fc_2"))
model.add(Activation('softmax',name="class_result"))
#model.summary()
return model
tf.reset_default_graph()
sess = tf.Session()
k.set_session(sess)
model=build_network()
history_dict = {}
model.compile(loss='sparse_categorical_crossentropy',optimizer = Adam(),metrics=['accuracy'])
class TFCheckpointCallback(keras.callbacks.Callback):
def __init__(self,saver,sess):
self.saver=saver
self.sess=sess
def on_epoch_end(self,epoch,log=None):
self.saver.save(self.sess,'fMnist/ckpt',global_step=epoch)
tf_saver= tf.train.Saver(max_to_keep=2)
checkpoint_callback= TFCheckpointCallback(tf_saver,sess)
%time
tf_graph=sess.graph
tf.train.write_graph(tf_graph.as_graph_def(),'freeze','fm_graph.pdtxt',as_text=True)
%time
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=50,
callbacks=[checkpoint_callback],
shuffle=True,
verbose=1,
validation_data=(x_validate,y_validate)
)
sess.close()
model_folder='fMnist/'
def prepare_graph_for_freezing(model_folder):
model=build_network(is_training=False)
checkpoint=tf.train.get_checkpoint_state(model_folder)
input_checkpoint=checkpoint.model_checkpoint_path
saver=tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
k.set_session(sess)
saver.restore(sess,input_checkpoint)
tf.gfile.MakeDirs(model_folder+'freeze')
saver.save(sess,model_folder + 'freeze/ckpt',global_step=0)
def freeze_graph(model_folder):
checkpoint =tf.train.get_checkpoint_state(model_folder)
print(model_folder+'freeze/')
input_checkpoint = checkpoint.model_checkpoint_path
absolut_model_folder="/".join(input_checkpoint.split('/')[:-1])
output_graph=absolut_model_folder + "/fm_freazen_model.pb"
print(output_graph)
output_node_name = "class_result/Softmax"
clear_devices = True
new_saver= tf.train.import_meta_graph(input_checkpoint + '.meta',clear_devices=clear_devices)
graph = tf.get_default_graph()
input_graph_def = graph.as_graph_def()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess2:
print(input_checkpoint)
new_saver.restore(sess2,input_checkpoint)
output_graph_def=graph_util.convert_variables_to_constants(
sess2,
input_graph_def,
output_node_name.split(","))
with tf.gfile.GFile(output_graph,"wb") as f:
f.write(output_graph_def.SerializeToString())
print("%d ops in the final graph."% len(output_graph_def.node))
tf.reset_default_graph()
prepare_graph_for_freezing("freeze/")
freeze_graph("freeze/")