Ytukyg

First of all sorry I am not prcise for this question but I am studying the tensorflow-serving and how to put in production my cnn. sincerely the documentation is quite confuse to me. I hope you can help to understand better the save model architecture. So please reply to me as teacher, i would like to know more about the whole flow.

I am developping a simple cnn to classify an image to 4 output.
I need tensorflow-serving to put it in production.
The image in input can be watherver size, the CNN should resize it first and predict.
Here the code

import numpy as np

import tensorflow as tf

from tensorflow import keras

from keras.preprocessing.image import ImageDataGenerator

from matplotlib import pyplot as plt

from scipy.misc import toimage

from keras.models import Sequential

from keras.layers import *

from keras.optimizers import *

from tensorflow.python.saved_model import builder as saved_model_builder

from tensorflow.python.saved_model import tag_constants, signature_constants, signature_def_utils_impl

import cv2







#train_path='Garage/train'

#train_datagen = ImageDataGenerator(rescale=1./255)

#train_batch = train_datagen.flow_from_directory(train_path, target_size=(64,64), class_mode='categorical', batch_size=10, color_mode='grayscale')





#validation_datagen = ImageDataGenerator(rescale=1./255)

#validation_batch = validation_datagen.flow_from_directory(

#        './Garage/validation',

#        target_size=(64, 64),

#        batch_size=3,

#        class_mode='categorical', color_mode='grayscale')



model = Sequential()

model.add(InputLayer(input_shape=[64,64,1]))

model.add(Conv2D(filters=32,kernel_size=5,strides=1,padding='same',activation='relu'))

model.add(MaxPool2D(pool_size=5,padding='same'))



model.add(Conv2D(filters=50,kernel_size=5,strides=1,padding='same',activation='relu'))

model.add(MaxPool2D(pool_size=5,padding='same'))



model.add(Conv2D(filters=80,kernel_size=5,strides=1,padding='same',activation='relu'))

model.add(MaxPool2D(pool_size=5,padding='same'))



model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(512,activation='relu'))

model.add(Dropout(rate=0.5))

model.add(Dense(4,activation='softmax'))

optimizer=Adam(lr=1e-3)



model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])

#model.fit_generator(

#        train_batch,

#        epochs=50,

#        steps_per_epoch=6,

#        validation_data=validation_batch,

#        validation_steps=5)



model.load_weights('model.h5')



#score = model.evaluate_generator(validation_batch,steps=3)

#print('Test loss:', score[0])

#print('Test accuracy:', score[1])



#model.save('model.h5')





from PIL import Image

import requests

from io import BytesIO



response = requests.get('http://192.168.3.21:7451/shot.jpg')

image_pil = Image.open(BytesIO(response.content))

image = np.asarray(image_pil)



img2 = cv2.resize(image,(64,64))

img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

img = np.reshape(img2,[1,64,64,1])



classes = model.predict_classes(img)



print(classes)



model_version="1"



sess = tf.Session()



#setting values for the sake of saving the model in the proper format

x = model.input

y = model.output



prediction_signature = tf.saved_model.signature_def_utils.predict_signature_def({"inputs":x}, {"prediction":y})



valid_prediction_signature = tf.saved_model.signature_def_utils.is_valid_signature(prediction_signature)

if(valid_prediction_signature == False):

    raise ValueError("Error: Prediction signature not valid!")



builder = saved_model_builder.SavedModelBuilder('./'+model_version)

legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')



# Add the meta_graph and the variables to the builder

builder.add_meta_graph_and_variables(

      sess, [tag_constants.SERVING],

      signature_def_map={

           signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:prediction_signature, },

      legacy_init_op=legacy_init_op)



# save the graph

builder.save()

the code will take the picture from a cam http://192.168.3.21:7451/shot.jpg
and then it will predict it

When I compile the code it return a lot of errors when it try to save the model. can you please check it and tell me if the save model instructions are right?

I use x = model.input as input from the serving but I would like it take the picture as input from the server.
I am quite confuse actually, sorry.
The scope is when I request by gRPC to predict the image the model can give me the prediction result
Thanks