OSError: SavedModel file does not exist at: ../dnn/mpg_model.h5/{saved_model.pbtxt|saved_model.pb}

The error occurs because your code is trying to load a model that does not exist. From the Notebook file you linked, you will most likely have to run the following:

from werkzeug.wrappers import Request, Response
from flask import Flask

app = Flask(__name__)

@app.route("/")
def hello():
    return "Hello World!"

if __name__ == '__main__':
    from werkzeug.serving import run_simple
    run_simple('localhost', 9000, app)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping
import pandas as pd
import io
import os
import requests
import numpy as np
from sklearn import metrics

df = pd.read_csv(
    "https://data.heatonresearch.com/data/t81-558/auto-mpg.csv", 
    na_values=['NA', '?'])

cars = df['name']

# Handle missing value
df['horsepower'] = df['horsepower'].fillna(df['horsepower'].median())

# Pandas to Numpy
x = df[['cylinders', 'displacement', 'horsepower', 'weight',
       'acceleration', 'year', 'origin']].values
y = df['mpg'].values # regression

# Split into validation and training sets
x_train, x_test, y_train, y_test = train_test_split(    
    x, y, test_size=0.25, random_state=42)

# Build the neural network
model = Sequential()
model.add(Dense(25, input_dim=x.shape[1], activation='relu')) # Hidden 1
model.add(Dense(10, activation='relu')) # Hidden 2
model.add(Dense(1)) # Output
model.compile(loss='mean_squared_error', optimizer='adam')

monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=1, mode='auto',
        restore_best_weights=True)
model.fit(x_train,y_train,validation_data=(x_test,y_test),callbacks=[monitor],verbose=2,epochs=1000)

pred = model.predict(x_test)
# Measure RMSE error.  RMSE is common for regression.
score = np.sqrt(metrics.mean_squared_error(pred,y_test))
print(f"After load score (RMSE): {score}")

model.save(os.path.join("./dnn/","mpg_model.h5"))

This will train and save the model that your code is loading.

It also looks like you have a small typo on the line: model = load_model(os.path.join("../dnn/","mpg_model.h5")) which should be changed to model = load_model(os.path.join("./dnn/","mpg_model.h5"))

I was getting the same error trying to load a .h5 model on a raspberry pi.

OSError: SavedModel file does not exist at: ... {saved_model.pbtxt|saved_model.pb}

sudo apt install python3-h5py

Seemed to have solved the issue.

reference