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

Question

**

code editor: vscode

cmd: anaconda prompt

I followed the tutorial but why this error? **

first error was ModuleNotFoundError: No module named 'tensorflow' but i make env and install it second error was ModuleNotFoundError: No module named 'flask' but i make env and install it i fix them and they work on python How can I solve this?

# T81-558: Applications of Deep Neural Networks
# Module 13: Advanced/Other Topics
# Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)
# For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/).
# Deploy simple Keras tabular model with Flask only.
from flask import Flask, request, jsonify
import uuid
import os
from tensorflow.keras.models import load_model
import numpy as np

app = Flask(__name__)

# Used for validation
EXPECTED = {
  "cylinders":{"min":3,"max":8},
  "displacement":{"min":68.0,"max":455.0},
  "horsepower":{"min":46.0,"max":230.0},
  "weight":{"min":1613,"max":5140},
  "acceleration":{"min":8.0,"max":24.8},
  "year":{"min":70,"max":82},
  "origin":{"min":1,"max":3}
}

# Load neural network when Flask boots up
model = load_model(os.path.join("../dnn/","mpg_model.h5"))

@app.route('/api/mpg', methods=['POST'])
def calc_mpg():
    content = request.json
    errors = []

    # Check for valid input fields 
    for name in content:
      if name in EXPECTED:
        expected_min = EXPECTED[name]['min']
        expected_max = EXPECTED[name]['max']
        value = content[name]
        if value < expected_min or value > expected_max:
          errors.append(f"Out of bounds: {name}, has value of: {value}, but should be between {expected_min} and {expected_max}.")
      else:
        errors.append(f"Unexpected field: {name}.")

    # Check for missing input fields
    for name in EXPECTED:
      if name not in content:
        errors.append(f"Missing value: {name}.")

    if len(errors) <1:
      # Predict
      x = np.zeros( (1,7) )

      x[0,0] = content['cylinders']
      x[0,1] = content['displacement'] 
      x[0,2] = content['horsepower']
      x[0,3] = content['weight']
      x[0,4] = content['acceleration'] 
      x[0,5] = content['year']
      x[0,6] = content['origin']

      pred = model.predict(x)
      mpg = float(pred[0])
      response = {"id":str(uuid.uuid4()),"mpg":mpg,"errors":errors}
    else:
      # Return errors
      response = {"id":str(uuid.uuid4()),"errors":errors}


    print(content['displacement'])

    return jsonify(response)

if __name__ == '__main__':
    app.run(host= '0.0.0.0',debug=True)

#conda
(tf-gpu) (HelloWold) C:\Users\ASUS\t81_558_deep_learning\py>python mpg_server_1.py
2020-05-09 17:25:38.498181: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
Traceback (most recent call last):
  File "mpg_server_1.py", line 26, in <module>
    model = load_model(os.path.join("../dnn/","mpg_model.h5"))
  File "C:\Users\ASUS\Envs\HelloWold\lib\site-packages\tensorflow\python\keras\saving\save.py", line 189, in load_model
    loader_impl.parse_saved_model(filepath)
  File "C:\Users\ASUS\Envs\HelloWold\lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 113, in parse_saved_model
    constants.SAVED_MODEL_FILENAME_PB))
OSError: SavedModel file does not exist at: ../dnn/mpg_model.h5/{saved_model.pbtxt|saved_model.pb}

from https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_13_01_flask.ipynb https://www.youtube.com/watch?v=H73m9XvKHug&t=1056s

Answer 1

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"))

Answer 2

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

Answer 3

If on windows, the path to the model can cause the error.

For a sanity check, try placing the model in the same folder as the file that you are calling. Then fix your path to call the model from the same folder. This fixed my error.

If this works, then you can figure out how to fix the path issue (perhaps try providing an absolute path).

Answer 4

I got the same error, and I solved it by running the model again and save it with .pb extension instead with the .h5 or .hdf5 extension

Then use the tf.keras.models.load_model('D:\\model_name.pb') using double backslash I had that error in windows and could solve it