trainingNew.py

import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import applications
import cv2
import csv
import numpy as np
import matplotlib.pyplot as plt
import random
import pandas as pd


with open("E:\Music-App-using-Emotion\label.csv", "r") as handler:
    df = csv.reader(handler, delimiter=',')
    x = list(df)
    data = np.array(x).astype("str")

## In this section we are loading the data and normalizing it

Datadirectory = "trainNew/"
os.chdir('E:\Music-App-using-Emotion')

Classes = ["0","1","2","3","4","5","6","7","8","9"]

img_size = 224

training_Data = []

def create_training_Data():
    for category in Classes:
        path = os.path.join(Datadirectory,category)
        class_num = Classes.index(category)
        for img in os.listdir(path):
            try:
                img_array = cv2.imread(os.path.join(path, img))
                new_array = cv2.resize(img_array, (img_size,img_size))
                training_Data.append([new_array,class_num])
            except Exception as e:
                pass
        
create_training_Data()  

## Test print to check if all data are loaded
print(len(training_Data))  

random.shuffle(training_Data)

X = []
Y = []


y = np.delete(data,0, axis=1)
y.shape

for features,label in training_Data:
    X.append(features)
    Y.append(label)

    
X = np.array(X).reshape(-1,img_size,img_size,3)
    
X = X/255.0

## In this section we are deep learning model for training using Transfer Training

model = tf.keras.applications.MobileNetV2() ## Pre-trained Model

model.summary()

base_input = model.layers[0].input ##input

base_output = model.layers[-2].output

base_output ##checking

final_output = layers.Dense(128)(base_output) ##adding new layer, after the output of global pooling layer
final_output = layers.Activation('relu')(final_output) ## Activation function
final_output = layers.Dense(64)(final_output) 
final_output = layers.Activation('relu')(final_output)
final_output = layers.Dense(1,activation='sigmoid')(final_output) ## my classes are 07, classification layer

final_output ##checking

new_model = keras.Model(inputs = base_input, outputs = final_output)

new_model.summary()

new_model.compile(loss="binary_crossentropy", optimizer = "adam", metrics = ["accuracy"])

Y = np.array(Y)

new_model.fit(X,Y, epochs = 30) ##Change to 25 if accuracy low

new_model.save("CSE499A_Model_new.h5")