ValueError: `decode_predictions` expects a batch of predictions (i.e. a 2D array of shape (samples, 1000)). Found array with shape: (1, 105)

Question

I am working on Face recognition of movie stars, where total of 105 stars are featured. Have used MobileNetV2 for image classification and dump model as 'model.h5'. Below is code ofgenerating APi for same model but came through this error. I have used the same code as of Imagenet Classification.

from __future__ import division, print_function
# coding=utf-8
import sys
import os
import glob
import re
import numpy as np

# Keras
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from keras.models import load_model
from tensorflow.keras.preprocessing import image

#import Pillow

# Flask utils
from flask import Flask, redirect, url_for, request, render_template
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer

# Define a flask app
app = Flask(__name__)

# Model saved with Keras model.save()
MODEL_PATH = 'model.h5'

# Load your trained model
model = load_model(MODEL_PATH)
model.make_predict_function()          # Necessary



def model_predict(img_path, model):
    img = image.load_img(img_path, target_size=(224, 224))

    # Preprocessing the image
    x = image.img_to_array(img)
    # x = np.true_divide(x, 255)
    x = np.expand_dims(x, axis=0)

    # Be careful how your trained model deals with the input
    # otherwise, it won't make correct prediction!
    x = preprocess_input(x)

    preds = model.predict(x)
    return preds


@app.route('/', methods=['GET'])
def index():
    # Main page
    return render_template('index.html')


@app.route('/predict', methods=['GET', 'POST'])
def upload():
    if request.method == 'POST':
        # Get the file from post request
        f = request.files['file']

        # Save the file to ./uploads
        basepath = os.path.dirname(__file__)
        file_path = os.path.join(
            basepath, 'uploads', secure_filename(f.filename))
        f.save(file_path)

        # Make prediction
        preds = model_predict(file_path, model)

        # Process your result for human
        # pred_class = preds.argmax(axis=-1)            # Simple argmax
        pred_class = decode_predictions(preds, top=1)   # ImageNet Decode
        result = str(pred_class[0][0][1])               # Convert to string
        return result
    return None


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

Below is the error i came across.

ValueError: `decode_predictions` expects a batch of predictions (i.e. a 2D array of shape (samples, 1000)). Found array with shape: (1, 105)

Can anyone help me how can i change 1000 classes to 105 classes.Thanks in Advance.

Answer 1

So the parse_expr from rlang worked for me.

For example, I had to go:

test_variable_construction %>%
mutate(Total_Utilisation_Sum_3m = !!parse_expr(paste(iterates), collapse = " + "))