emotion-detection/main.py at main · devatraj/emotion-detection · GitHub

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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import os

from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dense,Input,Dropout,GlobalAveragePooling2D,Flatten,Conv2D,BatchNormalization,Activation,MaxPooling2D
from keras.models import Model,Sequential
from tensorflow.keras.optimizers import Adam,SGD,RMSprop
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau

folder_path=r"F:/Datasets/images/images/"
picture_size=48

batch_size=64
train=ImageDataGenerator()
val=ImageDataGenerator()

train_set = train.flow_from_directory(folder_path+"train",
                                              target_size = (picture_size,picture_size),
                                              color_mode = "grayscale",
                                              batch_size=batch_size,
                                              class_mode='categorical',
                                              shuffle=True)


test_set = val.flow_from_directory(folder_path+"validation",
                                              target_size = (picture_size,picture_size),
                                              color_mode = "grayscale",
                                              batch_size=batch_size,
                                              class_mode='categorical',
                                              shuffle=False)
classes=7
model = Sequential()

#Layer 1
model.add(Conv2D(64,(3,3),padding = 'same',input_shape = (48,48,1)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.25))

#Layer 2
model.add(Conv2D(128,(5,5),padding = 'same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout (0.25))

#Layer 3
model.add(Conv2D(512,(3,3),padding = 'same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout (0.25))

#Layer 4
model.add(Conv2D(512,(3,3), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())

#Fully connected 1st layer
model.add(Dense(256))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.25))


# Fully connected layer 2nd layer
model.add(Dense(512))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.25))

model.add(Dense(classes, activation='softmax'))


# opt = Adam(learning_rate = 0.0001)
# model.compile(optimizer=opt,loss='categorical_crossentropy', metrics=['accuracy'])

checkpoint=ModelCheckpoint("./model.h5", verbose=1,monitor='val_accuracy', save_best_only=True,mode='max')
early_stop=EarlyStopping(monitor='val_loss',
                        min_delta=0,
                        patience=3,
                        verbose=1,
                        restore_best_weights=True)
reduce_lr=ReduceLROnPlateau(monitor='val_loss',
                           factor=0.2,
                           patience=3,
                           verbose=1,
                           min_delta=0.001)
callbacks=[early_stop,checkpoint,reduce_lr]
epochs=10
model.compile(loss='categorical_crossentropy',
              optimizer = Adam(learning_rate=0.001),
              metrics=['accuracy'])

history= model.fit(generator=train_set,
                                steps_per_epoch=train_set.n//train_set.batch_size,
                                epochs=epochs,
                                validation_data = test_set,
                                validation_steps = test_set.n//test_set.batch_size,
                                callbacks=callbacks
                                )

plt.style.use('dark_background')

plt.figure(figsize=(20,10))
plt.subplot(1, 2, 1)
plt.suptitle('Optimizer : Adam', fontsize=10)
plt.ylabel('Loss', fontsize=16)
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.legend(loc='upper right')

plt.subplot(1, 2, 2)
plt.ylabel('Accuracy', fontsize=16)
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.legend(loc='lower right')
plt.show()