python-3.x - Tensorflowjs を使用してブラウザに保存されたモデルを読み込めません

Question

エッジ デバイスで転移学習を使用して米の分類器を構築しようとしています。https://github.com/ADLsourceCode/TensorflowJSのチュートリアルを参考にしました。

サンプル データはhttps://www.dropbox.com/s/esirpr6q1lsdsms/ricetransfer1.zip?dl=0にあります。

以下のコードを使用してモデルをローカルに保存し、TensorflowJS/Mobilenet_VGG16_Keras_To_TensorflowJS/static/ フォルダに vgg および mobilenet とともに保存しましたが、ブラウザで tensorflowjs に米モデルをロードできません。

ローカル システムに vgg モデルを保存し、tensoflowjs (ブラウザ内) にモデルをロードしようとすると、うまくいきます。

# Base variables
import os
base_dir = 'ricetransfer1/'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')
train_cats_dir = os.path.join(train_dir, 'KN')
train_dogs_dir = os.path.join(train_dir, 'DM')

train_size, validation_size, test_size = 90, 28, 26
#train_size, validation_size, test_size = 20, 23, 14

img_width, img_height = 224, 224  # Default input size for VGG16

# Instantiate convolutional base
from keras.applications import VGG16
import tensorflowjs as tfjs
import tensorflow as tf
tf.compat.v1.disable_eager_execution()

img_width, img_height = 224, 224  # Default input size for VGG16

conv_base = VGG16(weights='imagenet', 
              include_top=False,
              input_shape=(img_width, img_height, 3))  
# 3 = number of channels in RGB pictures

 #saving the vgg model to run it locally
 tfjs.converters.save_keras_model(conv_base, '/TensorflowJS/Mobilenet_VGG16_Keras_To_TensorflowJS/static/vgg')

# Check architecture
conv_base.summary()


# Extract features
import os, shutil
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
train_size, validation_size, test_size = 90, 28, 25

datagen = ImageDataGenerator(rescale=1./255)
batch_size = 1
#train_dir = "ricetransfer1/train"
#validation_dir = "ricetransfer1/validation"
#test_dir="ricetransfer1/test"
#indices = np.random.choice(range(len(X_train)))

def extract_features(directory, sample_count):
#sample_count= X_train.ravel()

features = np.zeros(shape=(sample_count, 7, 7, 512))  # Must be equal to the output of the convolutional base
labels = np.zeros(shape=(sample_count))
# Preprocess data
generator = datagen.flow_from_directory(directory,
                                        target_size=(img_width,img_height),
                                        batch_size = batch_size,
                                        class_mode='binary')
# Pass data through convolutional base
i = 0
for inputs_batch, labels_batch in generator:
    features_batch = conv_base.predict(inputs_batch)
    features[i * batch_size: (i + 1) * batch_size] = features_batch
    labels[i * batch_size: (i + 1) * batch_size] = labels_batch
    i += 1
    if i * batch_size >= sample_count:
        break
return features, labels

train_features, train_labels = extract_features(train_dir, train_size)  # Agree with our small dataset size
validation_features, validation_labels = extract_features(validation_dir, validation_size)
 test_features, test_labels = extract_features(test_dir, test_size)



# Define model
from keras import models
from keras import layers
from keras import optimizers

epochs = 2

ricemodel = models.Sequential()
ricemodel.add(layers.Flatten(input_shape=(7,7,512)))
ricemodel.add(layers.Dense(256, activation='relu', input_dim=(7*7*512)))
ricemodel.add(layers.Dropout(0.5))
ricemodel.add(layers.Dense(1, activation='sigmoid'))
ricemodel.summary()

 # Compile model
 ricemodel.compile(optimizer=optimizers.Adam(),
          loss='binary_crossentropy',
          metrics=['acc'])


# Train model
import os
history = ricemodel.fit(train_features, train_labels,
                epochs=epochs,
                batch_size=batch_size, 
                validation_data=(validation_features, validation_labels))


##saving the rice classification model to run it locally
 tfjs.converters.save_keras_model(ricemodel, '/TensorflowJS/Mobilenet_VGG16_Keras_To_TensorflowJS/static/rice/')

米のモデルに間違いがあると思いますが、どうすれば問題を解決できますか?

予想される出力は、tensorflowjs を使用してブラウザーで米の分類を実行することです。