Export a Keras model to a tensorflow .pb file with embedded weights to use on Android.

keras2android.py

from keras.models import Sequential
from keras.models import model_from_json
from keras import backend as K
import tensorflow as tf
from tensorflow.python.tools import freeze_graph
import os

# Load existing model.
with open("model.json",'r') as f:
    modelJSON = f.read()

model = model_from_json(modelJSON)
model.load_weights("model_weights.hdf5")

# All new operations will be in test mode from now on.
K.set_learning_phase(0)

# Serialize the model and get its weights, for quick re-building.
config = model.get_config()
weights = model.get_weights()

# Re-build a model where the learning phase is now hard-coded to 0.
new_model = Sequential.from_config(config)
new_model.set_weights(weights)

temp_dir = "graph"
checkpoint_prefix = os.path.join(temp_dir, "saved_checkpoint")
checkpoint_state_name = "checkpoint_state"
input_graph_name = "input_graph.pb"
output_graph_name = "output_graph.pb"

# Temporary save graph to disk without weights included.
saver = tf.train.Saver()
checkpoint_path = saver.save(K.get_session(), checkpoint_prefix, global_step=0, latest_filename=checkpoint_state_name)
tf.train.write_graph(K.get_session().graph, temp_dir, input_graph_name)

input_graph_path = os.path.join(temp_dir, input_graph_name)
input_saver_def_path = ""
input_binary = False
output_node_names = "Softmax" # model dependent
restore_op_name = "save/restore_all"
filename_tensor_name = "save/Const:0"
output_graph_path = os.path.join(temp_dir, output_graph_name)
clear_devices = False

# Embed weights inside the graph and save to disk.
freeze_graph.freeze_graph(input_graph_path, input_saver_def_path,
                          input_binary, checkpoint_path,
                          output_node_names, restore_op_name,
                          filename_tensor_name, output_graph_path,
                          clear_devices, "")

@amadlover in some way i managed to get it to work using predictions/Softmax, btw the model changes as I add more classes (i pass the lenght of the array classes (where i have all the classes name stored) as final number of neurons in the output layer) Imma copy paste the code here so u can get a better general overview
``

-- coding: utf-8 --

"""
Created on Sat Apr 13 18:06:05 2019

@author: chris
"""

Import libraries

import numpy as np
import matplotlib.pyplot as plt
import keras
import itertools
import convert
import os

from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import tensorflow as tf
from keras import backend as K
K.set_image_dim_ordering('th')
from sklearn.metrics import classification_report,confusion_matrix
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.optimizers import SGD,RMSprop,Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import *
import pickle

#%%

sess = tf.Session()

K.set_session(sess)

#%%
train_path = 'model/train'
valid_path = 'model/valid'
test_path = 'model/test'

wkdir = ''

pb_filename = 'pesi_pb.pb'
pbtxt_filename = 'pesi_pb.pbtxt'

#%%

with open ('model/config/config.cfg', 'rb') as fp:
classi = pickle.load(fp)
print("Classi caricate correttamente")

#%%
for i in classi:
questa = 'model/config/' + i + '.cfg'
with open(questa, 'rb') as fp:
batchSizes = pickle.load(fp)

#%%

#trainSize = int(batchSizes[0]/4)
#testSize = int(batchSizes[1]/4)
#validSize = int(batchSizes[2]/4)

#ATTENZIONE!!! LEGGERE ATTENTAMENTE
#Generally batch size of 32 or 25 is good, with epochs = 100 unless you have large dataset.
#in case of large dataset you can go with batch size of 10 with epochs b/w 50 to 100.
#DATASET MOLTO GRANDE, USIAMO UNA BATCH SIZE PICCOLA
trainSize = 10
testSize = 10
validSize = 10

print("train size totale: " + str(trainSize))
print("test size totale: " + str(testSize))
print("valid size totale: " + str(validSize))

train_batches = ImageDataGenerator().flow_from_directory(train_path, target_size=(224,224), classes=classi, batch_size=trainSize)
test_batches = ImageDataGenerator().flow_from_directory(test_path, target_size=(224,224), classes=classi, batch_size=testSize)
valid_batches = ImageDataGenerator().flow_from_directory(valid_path, target_size=(224,224), classes=classi, batch_size=validSize)

#%%
stepsEpoche = len(train_batches)/10
stepsValid = len(valid_batches)/10
numEpoche = 3
#%%
#stampa immagini con le varie label
def plots(ims, figsize=(12,6), rows=1, interp=False, titles=None):
if type(ims[0]) is np.ndarray:
ims = np.array(ims).astype(np.uint8)
if (ims.shape[-1] != 3):
ims = ims.transpose((0,2,3,1))
f = plt.figure(figsize=figsize)
cols = len(ims)//rows if len(ims) % 2 == 0 else len(ims)//rows +1
for i in range(len(ims)):
sp = f.add_subplot(rows, cols, i+1)
sp.axis('Off')
if titles is not None:
sp.set_title(titles[i], fontsize=16)
plt.imshow(ims[i], interpolation=None if interp else 'none')

#%%

imgs, labels = next(train_batches)

#%%
plots(imgs, titles=labels)

#%%
test_imgs, test_labels = next(test_batches)
plots(test_imgs, titles=test_labels)

#%%
test_labels = test_labels[:,0]
test_labels

#%%
#VGG19 IMPORT

vgg19_model = keras.applications.vgg19.VGG19()
vgg19_model.summary()

#ELIMINA L'ULTIMO LAYER CON I 1000 OUTPUT
vgg19_model.layers.pop()

model = Sequential()

for layer in vgg19_model.layers:
model.add(layer)

for layer in model.layers:
layer.trainable = False

model.add(Dense(len(classi), activation='softmax', name='predictions'))

model.compile(Adam(lr=.0001), loss='categorical_crossentropy', metrics=['accuracy'])

hist = model.fit_generator(train_batches, steps_per_epoch=stepsEpoche, validation_data=valid_batches, validation_steps=stepsValid, epochs=numEpoche, verbose=2)

test_imgs, test_labels = next(test_batches)
plots(test_imgs, titles=test_labels)
test_labels= test_labels[:,0]
test_labels
predictions = model.predict_generator(test_batches, steps=1, verbose=0)

print("Un po di statistiche: ")
acc = hist.history['acc']
#print("accuracy " + str(acc))
val_acc = hist.history['val_acc']
#print("val accuracy " + str(val_acc))
loss = hist.history['loss']
#print("loss " + str(loss))
val_loss = hist.history['val_loss']
#print("val loss " + str(val_loss))

stats = []
stats.append(acc)
stats.append(val_acc)
stats.append(loss)
stats.append(val_loss)
#salvo statistiche su un file
with open('model/stats.txt', 'wb') as fp:
pickle.dump(stats, fp)
print("Statistiche per questa sessione di learning salvate sul file stats.txt")
epochs = range(1, len(acc) + 1)

"bo" is for "blue dot"

plt.plot(epochs, loss, 'bo', label='Training loss')

b is for "solid blue line"

plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()

plt.plot(epochs, acc, 'bo', label='Accuracy')

b is for "solid blue line"

plt.plot(epochs, val_acc, 'b', label='Validation cccuracy')
plt.title('Accuracy and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.show()

#%%
#SERIALIZZA MODELLO IN UN FILE JSON

Save JSON config to disk

json_config = model.to_json()
with open('model_config.json', 'w') as json_file:
json_file.write(json_config)
json_file.close()

Save weights to disk

model.save_weights('weights.h5')
model.save('model.h5')

print("Pesi del modello salvati sul disco come 'weights.h5' ")
print("Modello salvati sul disco come 'model.h5' ")
print("JSON del modello salvati sul disco come 'model_config.json' ")

#%%
import freeze_graph

All new operations will be in test mode from now on.

K.set_learning_phase(0)

Serialize the model and get its weights, for quick re-building.

config = model.get_config()
weights = model.get_weights()

Re-build a model where the learning phase is now hard-coded to 0.

new_model = Sequential.from_config(config)
new_model.set_weights(weights)

temp_dir = "graph"
checkpoint_prefix = os.path.join(temp_dir, "saved_checkpoint")
checkpoint_state_name = "checkpoint_state"
input_graph_name = "input_graph.pb"
output_graph_name = "output_graph.pb"

Temporary save graph to disk without weights included.

saver = tf.train.Saver()
checkpoint_path = saver.save(K.get_session(), checkpoint_prefix, global_step=0, latest_filename=checkpoint_state_name)
tf.train.write_graph(K.get_session().graph, temp_dir, input_graph_name)

input_graph_path = os.path.join(temp_dir, input_graph_name)
input_saver_def_path = ""
input_binary = False
output_node_names = "predictions/Softmax" # model dependent
restore_op_name = "save/restore_all"
filename_tensor_name = "save/Const:0"
output_graph_path = os.path.join(temp_dir, output_graph_name)
clear_devices = False
#%%
sess = K.get_session()
graph_def = sess.graph.as_graph_def()
for node in graph_def.node:
print(node)
#%%

Embed weights inside the graph and save to disk.

freeze_graph.freeze_graph(input_graph_path, input_saver_def_path,
input_binary, checkpoint_path,
output_node_names, restore_op_name,
filename_tensor_name, output_graph_path,
clear_devices, "")``

I then use optimize_for_inference.py to optimize the graph but when i finally try to load the net into opencv using this code:
cvNet = cv.dnn.readNetFromTensorflow('opt_model.pb')

I get this error:
Traceback (most recent call last):
File "C:/Users/chris/PycharmProjects/flask/opencv.py", line 9, in
cvOut = cvNet.forward()
cv2.error: OpenCV(4.0.0) C:\projects\opencv-python\opencv\modules\dnn\src\dnn.cpp:413: error: (-2:Unspecified error) Can't create layer "flatten/Shape" of type "Shape" in function 'cv::dnn::dnn4_v20180917::LayerData::getLayerInstance'

Process finished with exit code 1

I then use optimize_for_inference.py to optimize the graph but when i finally try to load the net into opencv using this code:
cvNet = cv.dnn.readNetFromTensorflow('opt_model.pb')

I get this error:
Traceback (most recent call last):
File "C:/Users/chris/PycharmProjects/flask/opencv.py", line 9, in
cvOut = cvNet.forward()
cv2.error: OpenCV(4.0.0) C:\projects\opencv-python\opencv\modules\dnn\src\dnn.cpp:413: error: (-2:Unspecified error) Can't create layer "flatten/Shape" of type "Shape" in function 'cv::dnn::dnn4_v20180917::LayerData::getLayerInstance'

Process finished with exit code 1

Hello, I have exhausted the extent my help on this matter. :|

@amadlover , thanks the same, I hope someone maybe will read this and give me an opinion/solution