nyoki-mtl / keras-facenet

Facenet implementation by Keras2

keras-facenet

Facenet implementation by Keras2

Pretrained model

You can quickly start facenet with pretrained Keras model (trained by MS-Celeb-1M dataset).

• Download model from here and save it in model/keras/

You can also create Keras model from pretrained tensorflow model.

• Download model from here and save it in model/tf/
• Convert model for Keras in tf_to_keras.ipynb

Demo

• Face vector calculation
• Classification with SVM
• Web camera demo

Environments

Ubuntu16.04 or Windows10
python3.6.2
tensorflow: 1.3.0
keras: 2.1.2

keras-facenet's Issues

Question regarding inceptionnet

Why did you manually add the inceptionnetv2 instead of importing it from the tensorflow API?

Did the keras model you provide here implement l2 norm?

Hi, I see in your three demo a function: l2_normalize, so the keras facenet model you provided has no l2_norm step inside, right?

Thanks.

Different probability predictions everytime

Hi folks,
I added a predict_proba function to the clf SVC object to estimate the probability of prediction and it goes something like this:

`

                    pred = le.inverse_transform(clf.predict(embs))
                    pred1 = clf.predict_proba(embs)
                    pred1 = max(max(pred1.tolist()))
                    
                    if pred1>0.71:
                        print(pred[0],' detected with probability ', pred1)

`

The problem I'm facing is that it gives me different probabilities for the same trained face with the same lighting conditions etc everytime for my built-in DELL laptop webcam feed. Also when an untrained face is made to stand infront of it, the probability of a trained face is always greater than that untrained face. Do you guys think the problem is arising because I'm using an inferior 0.3 MP 640x480 feed as compared to training on higher-res higher-quality dataset of images? Does the dataset and the feed from the webcam have to be of same resolution? I really need help as this is a major cause of headache.

Error in demo-images notebook

Getting the following error while loading the model (on tensorflow-gpu (1.13.1))

Any guess?

model_path = '../model/keras/model/facenet_keras.h5'
model = load_model(model_path)

---

WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.

IndexError Traceback (most recent call last)
in
1 model_path = '../model/keras/model/facenet_keras.h5'
----> 2 model = load_model(model_path)

/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py in load_model(filepath, custom_objects, compile)
417 f = h5dict(filepath, 'r')
418 try:
--> 419 model = _deserialize_model(f, custom_objects, compile)
420 finally:
421 if opened_new_file:

/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py in _deserialize_model(f, custom_objects, compile)
223 raise ValueError('No model found in config.')
224 model_config = json.loads(model_config.decode('utf-8'))
--> 225 model = model_from_config(model_config, custom_objects=custom_objects)
226 model_weights_group = f['model_weights']
227

/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py in model_from_config(config, custom_objects)
456 'Sequential.from_config(config)?')
457 from ..layers import deserialize
--> 458 return deserialize(config, custom_objects=custom_objects)
459
460

/usr/local/lib/python3.5/dist-packages/keras/layers/init.py in deserialize(config, custom_objects)
53 module_objects=globs,
54 custom_objects=custom_objects,
---> 55 printable_module_name='layer')

/usr/local/lib/python3.5/dist-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
143 config['config'],
144 custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 145 list(custom_objects.items())))
146 with CustomObjectScope(custom_objects):
147 return cls.from_config(config['config'])

/usr/local/lib/python3.5/dist-packages/keras/engine/network.py in from_config(cls, config, custom_objects)
1030 if layer in unprocessed_nodes:
1031 for node_data in unprocessed_nodes.pop(layer):
-> 1032 process_node(layer, node_data)
1033
1034 name = config.get('name')

/usr/local/lib/python3.5/dist-packages/keras/engine/network.py in process_node(layer, node_data)
989 # and building the layer if needed.
990 if input_tensors:
--> 991 layer(unpack_singleton(input_tensors), **kwargs)
992
993 def process_layer(layer_data):

/usr/local/lib/python3.5/dist-packages/keras/engine/base_layer.py in call(self, inputs, **kwargs)
455 # Actually call the layer,
456 # collecting output(s), mask(s), and shape(s).
--> 457 output = self.call(inputs, **kwargs)
458 output_mask = self.compute_mask(inputs, previous_mask)
459

/usr/local/lib/python3.5/dist-packages/keras/layers/core.py in call(self, inputs, mask)
685 if has_arg(self.function, 'mask'):
686 arguments['mask'] = mask
--> 687 return self.function(inputs, **arguments)
688
689 def compute_mask(self, inputs, mask=None):

/usr/local/lib/python3.5/dist-packages/keras/layers/core.py in (inputs, scale)
88 rate: float between 0 and 1. Fraction of the input units to drop.
89 noise_shape: 1D integer tensor representing the shape of the
---> 90 binary dropout mask that will be multiplied with the input.
91 For instance, if your inputs have shape
92 (batch_size, timesteps, features) and

IndexError: tuple index out of range

EOFError

Unable to load the pre-trained model shared using Google Drive.
Got the following error.

EOFError: EOF read where object expected

tensorflow model on MS Celeb 1M dataset

can you please provide the .pb file of the trained keras model on MS Celeb 1M dataset?

Error in demo-images notebook

Hi, I'm getting the following error when I run the 9th cell, I tried changing image_dirpath and rechecking for loops, but none of them worked:

FileNotFoundError Traceback (most recent call last)
in
2 for name in names:
3 image_dirpath = image_dir_basepath + name
----> 4 image_filepaths = [os.path.join(image_dirpath, f) for f in os.listdir(image_dirpath)]
5 embs = calc_embs(image_filepaths)
6 for i in range(len(image_filepaths)):

FileNotFoundError: [WinError 3] The system cannot find the path specified: '../data/images/LarryPage'

Cell 9th:
data = {}
for name in names:
image_dirpath = image_dir_basepath + name
image_filepaths = [os.path.join(image_dirpath, f) for f in os.listdir(image_dirpath)]
embs = calc_embs(image_filepaths)
for i in range(len(image_filepaths)):
data['{}{}'.format(name, i)] = {'image_filepath' : image_filepaths[i],
'emb' : embs[i]}

How can we retrain the model on custom dataset (Indian Faces)?

I would like to re-train the model on custom data-set. How can we achieve that?

h5 model and weight file

I followed the notebook files.
But, there is some problem for creating keras model file from tf ckpt.
Svm and image results are weird although It successfully created the h5 file.
I think that has problem in converting process for my case.
Can you upload you keras model file?

my env : tf1.3, keras2.1.2, python2.7 or 3.6

Model Architecture

Can you please post the code for model architecture or put the model as a json file, this would help the load the weights in different python versions?
Thank you

get the exceptional answer in windows

I use your notebook example to run, but get the exception answer

I only replace skimage resize to cv2.resive
https://gist.github.com/blackredscarf/5c827eb5afa484b39e7c456a8edfd39f

windows 10
keras 2.1.3
python 3.5
tensorflow 1.4

Bug in inception_resnet_v1.py

As loading the provided file does not work with a different Python version, I tried to convert the weights from David Sandberg's pre-trained model via the provided code. The resulting embeddings were arbitrary, as already mentioned in one of the other comments. I tracked the issue down to a bug in the inception_resnet_v1.py code, line 102:

x = Lambda(scaling,
output_shape=K.int_shape(up)[1:],
arguments={'scale': scale})(up)

should actually say

up = Lambda(scaling,
output_shape=K.int_shape(up)[1:],
arguments={'scale': scale})(up)

as x is the input to the block that needs to go into the final addition with the scaled result computed in the block. However, with the current state of the code, x will be overwritten and the output of the block will be a scaled version of the result computed in the block.

Adding this change enabled me to export a model that gives me the same embeddings as David Sandberg's model.

Best,
Kira

Training in demo-webcam

After executing the line

f.train()

The following error is returned

ValueError                                Traceback (most recent call last)
<ipython-input-10-770cb541f283> in <module>()
----> 1 f.train()

<ipython-input-7-720c27c39f20> in train(self)
     73             embs.append(embs_)
     74 
---> 75         embs = np.concatenate(embs)
     76         le = LabelEncoder().fit(labels)
     77         y = le.transform(labels)

<__array_function__ internals> in concatenate(*args, **kwargs)

ValueError: need at least one array to concatenate

What is the problem? And how to solve it?

License

Could you please let me know the license type of this repository

what is the pretrained model accuracy?

I need a baseline thanks.

image normalization

i was able to create the embedding using the converted CoreML keras model but the input image was not normalized thus the output was incorrect.

I used the tensorflow method tf.image.per_image_standardization(image) to train the data thus would like to use the similar method. Do I need to implement it myself in Swift? or Should I add the layer in the keras model ?

Error message running tf_to_keras notebook

When running the last cell of tf_to_keras notebook (changing model folder and checkpoint to the latest model available), I get the following error:

Loading numpy weights from ../model/keras/npy_weights/
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-14-903bce1a4b25> in <module>
      8             weight_arr = np.load(os.path.join(npy_weights_dir, weight_file))
      9             weights.append(weight_arr)
---> 10         layer.set_weights(weights)
     11 
     12 print('Saving weights...')

~/anaconda3/envs/venv/lib/python3.6/site-packages/keras/engine/base_layer.py in set_weights(self, weights)
   1055                                  str(pv.shape) +
   1056                                  ' not compatible with '
-> 1057                                  'provided weight shape ' + str(w.shape))
   1058             weight_value_tuples.append((p, w))
   1059         K.batch_set_value(weight_value_tuples)

ValueError: Layer weight shape (1792, 128) not compatible with provided weight shape (1792, 512)

Any insight would be really appreciated...

How can I finetune keras-facenet?

I want to finetune keras-facenet as was described with VGG16 model Fine-tuning the top layers of a a pre-trained network.

How can I do so? How should I define the classifier on top of this model?

hoe to know the prediction accuracy in

Face Alignment

Unable to load pre-trained model

Unable to load the pre-trained model shared using Google Drive.
Got the following error.

ValueError: bad marshal data (unknown type code)

Core ML model output

Core ML model output seems not scaled, how much scale should I apply while converting?
Keras output:
array([[-2.6317966e-01, -3.3678617e-02, 2.2661042e+00, -6.7546511e-01,
2.6711123e+00, 2.1380582e+00, 3.1932313e+00, -1.0266680e+00,
-1.2064759e+00, -3.7772853e+00, 1.4777870e+00, -4.5663638e+00,
5.3783691e-01, 6.9581866e-01, -3.2163723e+00, -2.2745862e+00,
1.9304838e+00, -4.4520035e+00, -1.1737602e+00, -4.1146436e+00,
2.8376224e+00, -6.1216235e+00, 8.1349170e-01, 4.7025423e+00,
2.4575691e+00, 4.7649293e+00, 3.1503294e+00, 1.7728921e-02,
-3.4083718e-01, -5.7512856e+00, -1.0213958e+00, 5.9897904e+00,
-2.0491607e+00, 2.1902418e+00, -2.5166994e-01, 1.1277127e+00,
4.1162009e+00, -2.1709535e+00, 4.1017589e-01, 7.4210954e-01,
2.5226521e+00, -2.1406457e-01, 1.8106477e+00, 7.8078407e-01,
-5.4581828e+00, 3.3939710e+00, 3.6321692e+00, 5.1154441e-01,
-5.9473997e-01, -6.4269938e+00, 4.8341846e+00, 1.7983266e+00,
9.3833292e-01, 1.2895137e+00, 1.9546670e+00, 6.5272875e+00,
9.4690269e-01, -5.3456831e+00, 2.5641103e+00, 4.0326028e+00,
-2.4558032e+00, -6.3662320e-01, 1.0621898e+00, 1.5926079e+00,
5.3224262e-02, 1.2270166e+00, 2.2601867e+00, 7.6212996e-01,
4.0221577e+00, 3.7582583e+00, 1.6832346e+00, 3.1141477e+00,
-2.5827744e+00, 3.7479327e+00, -2.5769188e+00, 3.5341086e+00,
-1.0932776e+00, 2.3388093e+00, -3.6909742e+00, -3.2998741e+00,
-1.4890214e-02, 2.6787040e+00, 1.6687028e+00, -1.0944015e+00,
6.9286877e-01, 1.8361024e+00, 4.1195717e+00, -6.4409890e+00,
9.2169154e-01, 2.6727970e+00, 2.2134098e-01, -3.7779233e+00,
8.3170211e-01, -2.0311317e+00, 6.9612050e-01, 7.6587849e+00,
3.3811717e+00, -3.0122232e+00, 2.9068935e+00, 2.9820104e+00,
-3.9757938e+00, -3.4195602e+00, -1.9991946e-01, 2.5219235e-01,
-3.4377544e+00, -7.1006083e-01, -5.6879859e+00, 3.5768299e+00,
1.8099457e+00, -1.5660530e+00, -5.2569504e+00, -1.1321362e+00,
2.7814114e+00, -3.8275607e+00, 3.7700075e-01, 2.9798200e+00,
-1.3275119e+00, 8.1083018e-01, -8.8596046e-01, -6.0629940e+00,
1.9946741e+00, -4.0994754e+00, 3.5792630e+00, 1.3484008e+00,
-1.8742681e-04, 1.3709370e+00, 3.6508691e+00, -3.8959811e+00]],
dtype=float32)
Converted model output:
[855.0784912109375,-4063.724365234375,737.659912109375,-7393.7275390625,-2245.569580078125,-1595.0166015625,-2120.451904296875,3890.713623046875,509.8944702148438,-3713.39208984375,-166.2485809326172,2475.9208984375,12017.4716796875,1453.450561523438,-4773.72509765625,-3715.33837890625,1487.673828125,-2976.453369140625,2839.177734375,987.212646484375,-139.4358978271484,-1500.54736328125,-1800.843627929688,2315.100830078125,2692.251220703125,1220.254272460938,8099.76123046875,-2944.90283203125,-1743.339721679688,-7470.42041015625,-2737.231689453125,4109.24609375,8797.529296875,-2345.7978515625,-7959.0234375,332.7884826660156,2513.694091796875,550.3179321289062,-1115.5908203125,1945.220092773438,3336.918212890625,1632.697387695312,435.2661743164062,1182.497314453125,-3632.15478515625,-2488.587890625,-566.3248901367188,-66.20777130126953,6390.865234375,-10544.40625,3047.216552734375,2446.52197265625,-3668.0166015625,-3492.718017578125,2651.781494140625,-1474.755859375,-407.1371154785156,-1065.56591796875,83.17457580566406,890.1207885742188,871.6825561523438,-945.396728515625,-3681.1923828125,2485.04736328125,3726.015380859375,-1097.35205078125,1911.19677734375,-2232.069580078125,1297.970458984375,-1134.264770507812,-4204.21142578125,3786.914794921875,-2623.253173828125,-1265.18505859375,1580.590454101562,-5637.24072265625,-2561.71240234375,314.7735595703125,2296.7470703125,-5458.6435546875,-388.1426696777344,3363.203857421875,2543.2294921875,2422.521240234375,-1095.759887695312,7738.43359375,-63.55666732788086,-5111.3408203125,-4203.83837890625,3556.224609375,2779.212890625,118.7055282592773,924.0149536132812,-5947.62548828125,1709.426879882812,6934.91650390625,4084.002197265625,-1921.875854492188,1726.5087890625,453.2798767089844,-857.8190307617188,-566.110595703125,5079.982421875,351.6022644042969,-6870.47802734375,2958.294921875,-4496.14794921875,1221.89111328125,-1598.419189453125,-2361.905517578125,-6226.74462890625,-1889.393798828125,-171.6868286132812,-2751.922607421875,2748.287841796875,3023.27392578125,-5400.4267578125,1489.955200195312,6292.98828125,-3763.5556640625,-2693.62060546875,-763.9926147460938,-3135.6083984375,4158.576171875,2564.194580078125,-1977.328979492188,352.8983154296875,-95.43745422363281]
converted model output with 1/255 scale:
[22.72692,-111.8219,21.57746,-205.979,-62.09193,-43.63466,-59.90541,105.5358,13.35077,-102.3288,-4.489635,66.16525,330.7473,41.02428,-131.7471,-101.4517,41.12999,-79.95216,79.33298,25.1811,-3.271009,-39.42453,-51.64153,63.79717,73.8793,35.71499,223.8051,-79.38886,-47.4924,-205.7483,-75.49256,114.1817,242.6053,-62.9214,-216.9569,9.832993,68.27296,13.29188,-28.98055,55.34957,91.76531,43.88745,13.52798,32.19314,-102.5722,-70.43962,-14.59233,-4.072866,176.7958,-292.1834,82.75787,68.06502,-100.6124,-96.46475,73.84764,-40.1497,-11.05663,-29.77695,2.676188,21.43342,22.9453,-27.11697,-101.4999,71.70054,102.3595,-28.63747,53.31374,-59.51252,37.61403,-32.10843,-116.7825,103.3929,-68.22962,-37.54702,42.9712,-152.4216,-69.04482,8.215605,63.2452,-151.812,-11.31077,91.50224,68.38104,70.26328,-26.96819,211.9508,-1.72123,-140.1563,-116.7873,101.6846,77.99618,4.845797,24.48302,-165.1091,46.5722,191.0615,111.0762,-53.38795,47.65784,10.37083,-23.10145,-12.50677,140.9233,8.339808,-187.8266,82.70412,-125.4531,34.69426,-45.9505,-68.5683,-168.2019,-50.66414,-6.257753,-73.38413,78.46208,84.6222,-147.3123,42.28506,176.3857,-100.7463,-75.18451,-20.3528,-85.68301,115.6824,72.76437,-57.86979,12.71073,-1.227953]

Got errors when run webcam demo

My code:

#coding:utf-8
import numpy as np
import cv2
import matplotlib.pyplot as plt
import signal
from IPython import display
from sklearn.svm import SVC
from sklearn.preprocessing import LabelEncoder
from skimage.transform import resize
from keras.models import load_model
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

cascade_path = './model/cv2/haarcascade_frontalface_alt2.xml'

model_path = './model/keras/model/facenet_keras.h5'
model = load_model(model_path)

def prewhiten(x):
    if x.ndim == 4:
        axis = (1, 2, 3)
        size = x[0].size
    elif x.ndim == 3:
        axis = (0, 1, 2)
        size = x.size
    else:
        raise ValueError('Dimension should be 3 or 4')

    mean = np.mean(x, axis=axis, keepdims=True)
    std = np.std(x, axis=axis, keepdims=True)
    std_adj = np.maximum(std, 1.0/np.sqrt(size))
    y = (x - mean) / std_adj
    return y

def l2_normalize(x, axis=-1, epsilon=1e-10):
    output = x / np.sqrt(np.maximum(np.sum(np.square(x), axis=axis, keepdims=True), epsilon))
    return output

def calc_embs(imgs, margin, batch_size):
    aligned_images = prewhiten(imgs)
    pd = []
    for start in range(0, len(aligned_images), batch_size):
        pd.append(model.predict_on_batch(aligned_images[start:start+batch_size]))
    embs = l2_normalize(np.concatenate(pd))

    return embs

class FaceDemo(object):
    def __init__(self, cascade_path):
        self.vc = None
        self.cascade = cv2.CascadeClassifier(cascade_path)
        self.margin = 10
        self.batch_size = 1
        self.n_img_per_person = 10
        self.is_interrupted = False
        self.data = {}
        self.le = None
        self.clf = None
        
    def _signal_handler(self, signal, frame):
        self.is_interrupted = True
        
    def capture_images(self, name='Unknown'):
        vc = cv2.VideoCapture(0)
        self.vc = vc
        if vc.isOpened():
            is_capturing, _ = vc.read()
        else:
            is_capturing = False

        imgs = []
        signal.signal(signal.SIGINT, self._signal_handler)
        self.is_interrupted = False
        while is_capturing:
            is_capturing, frame = vc.read()
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            faces = self.cascade.detectMultiScale(frame,
                                         scaleFactor=1.1,
                                         minNeighbors=3,
                                         minSize=(100, 100))
            if len(faces) != 0:
                face = faces[0]
                (x, y, w, h) = face
                left = x - self.margin // 2
                right = x + w + self.margin // 2
                bottom = y - self.margin // 2
                top = y + h + self.margin // 2
                img = resize(frame[bottom:top, left:right, :],
                             (160, 160), mode='reflect')
                imgs.append(img)
                cv2.rectangle(frame,
                              (left-1, bottom-1),
                              (right+1, top+1),
                              (255, 0, 0), thickness=2)

            plt.imshow(frame)
            plt.title('{}/{}'.format(len(imgs), self.n_img_per_person))
            plt.xticks([])
            plt.yticks([])
            display.clear_output(wait=True)
            if len(imgs) == self.n_img_per_person:
                vc.release()
                self.data[name] = np.array(imgs)
                break
            try:
                plt.pause(0.1)
            except Exception:
                pass
            if self.is_interrupted:
                vc.release()
                break
                
    def train(self):
        labels = []
        embs = []
        names = self.data.keys()
        for name, imgs in self.data.items():
            embs_ = calc_embs(imgs, self.margin, self.batch_size)    
            labels.extend([name] * len(embs_))
            embs.append(embs_)

        embs = np.concatenate(embs)
        le = LabelEncoder().fit(labels)
        y = le.transform(labels)
        print(embs.shape)
        print(y.shape)
        clf = SVC(kernel='linear', probability=True).fit(embs, y)
        
        self.le = le
        self.clf = clf
        
    def infer(self):
        vc = cv2.VideoCapture(0)
        self.vc = vc
        if vc.isOpened():
            is_capturing, _ = vc.read()
        else:
            is_capturing = False

        signal.signal(signal.SIGINT, self._signal_handler)
        self.is_interrupted = False
        while is_capturing:
            is_capturing, frame = vc.read()
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            faces = self.cascade.detectMultiScale(frame,
                                         scaleFactor=1.1,
                                         minNeighbors=3,
                                         minSize=(100, 100))
            pred = None
            if len(faces) != 0:
                face = faces[0]
                (x, y, w, h) = face
                left = x - self.margin // 2
                right = x + w + self.margin // 2
                bottom = y - self.margin // 2
                top = y + h + self.margin // 2
                img = resize(frame[bottom:top, left:right, :],
                             (160, 160), mode='reflect')
                embs = calc_embs(img[np.newaxis], self.margin, 1)
                pred = self.le.inverse_transform(self.clf.predict(embs))
                cv2.rectangle(frame,
                              (left-1, bottom-1),
                              (right+1, top+1),
                              (255, 0, 0), thickness=2)
            plt.imshow(frame)
            plt.title(pred)
            plt.xticks([])
            plt.yticks([])
            display.clear_output(wait=True)
            try:
                plt.pause(0.1)
            except Exception:
                pass
            if self.is_interrupted:
                vc.release()
                break

f = FaceDemo(cascade_path)
f.capture_images('XinyuDu')
f.train()
f.infer()

Error info as follows:

File "F:\Baiduyun\TensorFlowProjects\keras-facenet-master\webcam.py", line 128, in train
    clf = SVC(kernel='linear', probability=True).fit(embs, y)
  File "C:\Users\Administrator\Anaconda3\envs\ktf\lib\site-packages\sklearn\svm\base.py", line 150, in fit
    y = self._validate_targets(y)
  File "C:\Users\Administrator\Anaconda3\envs\ktf\lib\site-packages\sklearn\svm\base.py", line 506, in _validate_targets
    % len(cls))
ValueError: The number of classes has to be greater than one; got 1

Get error with webcam demo

Sir,

when I run the f.train(). it shows below
How can I solve it?

---

ValueError Traceback (most recent call last)
in
----> 1 f.train()

in train(self)
73 embs.append(embs_)
74
---> 75 embs = np.concatenate(embs)
76 le = LabelEncoder().fit(labels)
77 y = le.transform(labels)

ValueError: need at least one array to concatenate

using coreml model

i got this error while trying to test the corml model :

"[coreml] A Core ML custom neural network layer requires an implementation named 'scaling' which was not found in the global namespace."
and
"[coreml] Error creating Core ML custom layer implementation from factory for layer "scaling"."

How to use Gray scale image?

I am using Histogram equalisation for sharpening the image, which works well for me.

I would like to change the input layer size from 1601603 to 1601601. Is their any way to achieve it with tweaking the model itself.

Error Converting Tensorflow model into keras

There are 4 files, .pb, .meta, .index, ckpt-250000.data-00000-of-00001. How to convert it using your notebook??
also I'm running Anaconda on Windows 10