.tflite model (converted from keras .h5 model) always predicts the same class with same probability

I’ve trained a model (using keras) to count number of fingers held up. The model works really well (~99% accuracy on test images).
Now, I’m trying to deploy this model on the edge by converting the saved model (.h5 file) to a .tflite file.

Using tf.lite.TFLiteConverter.from_keras_model_file(), it converts and gives me a .tflite file with this error:

tensorflow/core/grappler/grappler_item_builder.cc:637] Init node conv2d/kernel/Assign doesn't exist in graph

When I load this tflite file and try to make predictions on the same input images, it always predicts ‘ZERO’ which is the first class and with probability = 0.003922. The rest of the classes are always 0.00
I get the same results when loading my tflite model in the Android Image classification example app from Tensorflow repo’s.

Why does this tflite model not work as expected? Am I missing something during the conversion or using operations that aren’t supported in TFlite? Please help!

So far, I’ve tried doing the conversions on different versions of Tensorflow;
– 1.12
– 1.14
– tf-nightly-gpu 1.14

All give the same results.

My .h5 model and a test image, if you would like to try it yourself!

This is the code I used to convert the model:

import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_keras_model_file("fingers_latest.h5")
tflite_model = converter.convert()
open("fingers_latest.tflite", "wb").write(tflite_model)

My keras model:

nbatch = 64
IMG_SIZE = 256


def load_data():
    print("Batch size = ", nbatch, "n")

    train_datagen = ImageDataGenerator(rescale=1. / 255, rotation_range=12., width_shift_range=0.2,
                                       height_shift_range=0.2,
                                       zoom_range=0.15, shear_range=0.2, horizontal_flip=False)

    test_datagen = ImageDataGenerator(rescale=1. / 255)

    train_gen = train_datagen.flow_from_directory('./datasets/fingers_white/train/', target_size=(IMG_SIZE, IMG_SIZE),
                                                  color_mode='rgb',
                                                  batch_size=nbatch, shuffle=True,
                                                  classes=['ZERO', 'ONE', 'TWO', 'THREE', 'FOUR', 'FIVE'],
                                                  class_mode='categorical')

    test_gen = test_datagen.flow_from_directory('./datasets/fingers_white/test/', target_size=(IMG_SIZE, IMG_SIZE),
                                                color_mode='rgb',
                                                batch_size=nbatch,
                                                classes=['ZERO', 'ONE', 'TWO', 'THREE', 'FOUR', 'FIVE'],
                                                class_mode='categorical')

    return train_gen, test_gen


def train_model(train_gen, test_gen):
    model = Sequential()
    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 3)))
    model.add(Dropout(0.2))
    model.add(MaxPooling2D((3, 3)))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(Dropout(0.4))
    model.add(MaxPooling2D((3, 3)))
    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(Dropout(0.6))
    model.add(MaxPooling2D((3, 3)))
    model.add(Conv2D(32, (3, 3), activation='relu'))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(6, activation='softmax'))

    model.summary()

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

    STEP_SIZE_TRAIN = train_gen.n // train_gen.batch_size
    STEP_SIZE_TEST = test_gen.n // test_gen.batch_size
    print(STEP_SIZE_TEST, STEP_SIZE_TRAIN)

    model.fit_generator(train_gen, steps_per_epoch=STEP_SIZE_TRAIN, epochs=5, validation_data=test_gen,
                        validation_steps=STEP_SIZE_TEST, use_multiprocessing=True, workers=6)

The code I use to load and run the .tflite model:

import tensorflow as tf
import tkinter as tk
from tkinter import filedialog
import PIL
from PIL import Image
import numpy as np
import time

# DEF. PARAMETERS
img_row, img_column = 224, 224
num_channel = 3
num_batch = 1
input_mean = 127.5
input_std = 127.5
floating_model = False

path_1 = r"./models/mobilenet_v2_1.0_224.tflite"
labels_path = "./models/labels_mobilenet.txt"

def load_labels(filename):
    my_labels = []
    input_file = open(filename, 'r')
    for l in input_file:
        my_labels.append(l.strip())
    return my_labels

interpreter = tf.lite.Interpreter(path_1)
interpreter.allocate_tensors()

# obtaining the input-output shapes and types
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print(input_details, 'n', output_details)

# file selection window for input selection
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilename()
input_img = Image.open(file_path)
input_img = input_img.resize((img_row, img_column))
input_img = np.expand_dims(input_img, axis=0)

input_img = (np.float32(input_img) - input_mean) / input_std

interpreter.set_tensor(input_details[0]['index'], input_img)

# running inference
interpreter.invoke()

output_data = interpreter.get_tensor(output_details[0]['index'])
results = np.squeeze(output_data)

top_k = results.argsort()[-5:][::-1]
labels = load_labels(labels_path)
for i in top_k:
    print('{0:08.6f}'.format(float(results[i] / 255.0)) + ":", labels[i])