emotion-python/train_model.py at main · meher0704/emotion-python · GitHub

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import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import keras
from keras.preprocessing import image
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Flatten,Dense,Dropout,BatchNormalization
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import cv2
from tensorflow.keras.applications import VGG16, InceptionResNetV2
from keras import regularizers
from tensorflow.keras.optimizers import Adam,RMSprop,SGD,Adamax

train_dir = "/Users/meherkhurana/PycharmProjects/pythonProject2/fer2013/Expressions/train"
test_dir = "/Users/meherkhurana/PycharmProjects/pythonProject2/fer2013/Expressions/test"

import tensorflow as tf
import keras
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization

model = tf.keras.models.Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=(48, 48, 1)))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(128, (5, 5), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.25))

model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.25))

model.add(Dense(7, activation='softmax'))
img_size=48
train_datagen = ImageDataGenerator(width_shift_range = 0.1,
                                         height_shift_range = 0.1,
                                         horizontal_flip = True,
                                         rescale = 1./255,
                                         validation_split = 0.2
                                        )
validation_datagen = ImageDataGenerator(rescale = 1./255,
                                         validation_split = 0.2)

train_generator = train_datagen.flow_from_directory(directory = train_dir,
                                                    target_size = (img_size,img_size),
                                                    batch_size = 64,
                                                    color_mode = "grayscale",
                                                    class_mode = "categorical",
                                                    subset = "training"
                                                   )
validation_generator = validation_datagen.flow_from_directory( directory = test_dir,
                                                              target_size = (img_size,img_size),
                                                              batch_size = 64,
                                                              color_mode = "grayscale",
                                                              class_mode = "categorical",
                                                              subset = "validation"
                                                             )


model.compile(
    optimizer = Adam(lr=0.001),
    loss='categorical_crossentropy',
    metrics=['accuracy']
  )


epochs = 45
batch_size = 64


history = model.fit(x = train_generator,epochs = epochs,validation_data = validation_generator)


fig , ax = plt.subplots(1,2)
train_acc = history.history['accuracy']
train_loss = history.history['loss']
fig.set_size_inches(12,4)

ax[0].plot(history.history['accuracy'])
ax[0].plot(history.history['val_accuracy'])
ax[0].set_title('Training Accuracy vs Validation Accuracy')
ax[0].set_ylabel('Accuracy')
ax[0].set_xlabel('Epoch')
ax[0].legend(['Train', 'Validation'], loc='upper left')

ax[1].plot(history.history['loss'])
ax[1].plot(history.history['val_loss'])
ax[1].set_title('Training Loss vs Validation Loss')
ax[1].set_ylabel('Loss')
ax[1].set_xlabel('Epoch')
ax[1].legend(['Train', 'Validation'], loc='upper left')

model.save_weights('FER_model.h5')

plt.show()

model.save_weights('FER_model.h5')