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facial_expression.py
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# -*- coding: utf-8 -*-
"""
@authors: jaydeep thik , Vasudev Purandare
"""
import tensorflow as tf
from tensorflow import keras
#from tensorflow.keras.models import Sequential
#from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
#---------------------------------------------------------------------------------------------------------------------------------
def generate_dataset():
"""generate dataset from csv"""
df = pd.read_csv("./fer2013/fer2013.csv")
train_samples = df[df['Usage']=="Training"]
validation_samples = df[df["Usage"]=="PublicTest"]
test_samples = df[df["Usage"]=="PrivateTest"]
y_train = train_samples.emotion.astype(np.int32).values
y_valid = validation_samples.emotion.astype(np.int32).values
y_test = test_samples.emotion.astype(np.int32).values
X_train =np.array([ np.fromstring(image, np.uint8, sep=" ").reshape((48,48)) for image in train_samples.pixels])
X_valid =np.array([ np.fromstring(image, np.uint8, sep=" ").reshape((48,48)) for image in validation_samples.pixels])
X_test =np.array([ np.fromstring(image, np.uint8, sep=" ").reshape((48,48)) for image in test_samples.pixels])
return X_train, y_train, X_valid, y_valid, X_test, y_test
#---------------------------------------------------------------------------------------------------------------------------------
def generate_model(lr=0.001):
"""training model"""
with tf.device('/gpu:0'):
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(64,(3,3), input_shape=(48,48, 1), padding="same"))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.MaxPooling2D())
model.add(keras.layers.Dropout(0.20))
model.add(keras.layers.Conv2D(128,(5,5), padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.MaxPooling2D())
model.add(keras.layers.Dropout(0.20))
model.add(keras.layers.Conv2D(512,(3,3), padding="same"))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.MaxPooling2D())
model.add(keras.layers.Dropout(0.20))
model.add(keras.layers.Conv2D(512,(3,3), padding="same"))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.MaxPooling2D())
model.add(keras.layers.Dropout(0.25))
model.add(keras.layers.Conv2D(256,(3,3), activation='relu', padding='same'))
model.add(keras.layers.Conv2D(128,(3,3), padding='same', activation='relu'))
model.add(keras.layers.MaxPooling2D())
model.add(keras.layers.Dropout(0.25))
#model.add(keras.layers.GlobalAveragePooling2D())
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(256))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(512, activation='relu'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(4,activation='softmax'))
model.compile(loss="categorical_crossentropy", optimizer=keras.optimizers.Adam(lr=lr) , metrics=['accuracy'])
return model
#---------------------------------------------------------------------------------------------------------------------------------
if __name__=="__main__":
#df = pd.read_csv("./fer2013/fer2013.csv")
# X_train, y_train, X_valid, y_valid, X_test, y_test = generate_dataset()
# X_train = X_train.reshape((-1,48,48,1)).astype(np.float32)
# X_valid = X_valid.reshape((-1,48,48,1)).astype(np.float32)
#X_test = X_test.reshape((-1,48,48,1)).astype(np.float32)
# X_train_std = X_train/255.
#X_valid_std = X_valid/255.
#X_test_std = X_test/255.
train_datagen = keras.preprocessing.image.ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'hackdataset/train',
target_size=(48, 48),
color_mode='grayscale',
batch_size=128,
class_mode='categorical')
test_generator = test_datagen.flow_from_directory(
'hackdataset/test',
target_size=(48, 48),
color_mode='grayscale',
batch_size=128,
class_mode='categorical')
model = generate_model(0.01)
with tf.device("/gpu:0"):
#history = model.fit(X_train_std, y_train,batch_size=128,epochs=35, validation_data=(X_valid_std, y_valid), shuffle=True)
history = model.fit_generator(train_generator, steps_per_epoch=200,epochs=35, validation_data=test_generator, validation_steps=80)