1. Import All Required Packages # Import libraries# Impor
import os ,cv2
import numpy as np
import matplotlib .pyplot as plt
import matplotlib .image as mpimg
from pylab import rcParams
rcParams ['figure.figsize' ] = 20 , 10
from sklearn .utils import shuffle
from sklearn .cross_validation import train_test_split
import keras
from keras .utils import np_utils
from keras import backend as K
from keras .models import Sequential
from keras .layers .core import Dense , Dropout , Activation , Flatten
from keras .layers .convolutional import Convolution2D , MaxPooling2D
from keras .optimizers import SGD ,RMSprop ,adam
from keras .preprocessing .image import ImageDataGenerator 2.Read Images from the data path #Define Datapath
data_path = './jaffe/'
data_dir_list = os .listdir (data_path )
img_rows = 256
img_cols = 256
num_channel = 1
num_epoch = 10
img_data_list = []
for dataset in data_dir_list :
img_list = os .listdir (data_path + '/' + dataset )
print ('Loaded the images of dataset-' + '{}\n ' .format (dataset ))
for img in img_list :
input_img = cv2 .imread (data_path + '/' + dataset + '/' + img )
#input_img=cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)
input_img_resize = cv2 .resize (input_img ,(128 ,128 ))
img_data_list .append (input_img_resize )
img_data = np .array (img_data_list )
img_data = img_data .astype ('float32' )
img_data = img_data / 255
img_data .shape Loaded the images of dataset-.ipynb_checkpoints
Loaded the images of dataset-ANGRY
Loaded the images of dataset-DISGUST
Loaded the images of dataset-FEAR
Loaded the images of dataset-HAPPY
Loaded the images of dataset-NEUTRAL
Loaded the images of dataset-SAD
Loaded the images of dataset-SURPRISE
(213, 128, 128, 3)
3.Define the number of classes num_classes = 7
num_of_samples = img_data .shape [0 ]
labels = np .ones ((num_of_samples ,),dtype = 'int64' )
labels [0 :29 ]= 0 #30
labels [30 :59 ]= 1 #29
labels [60 :92 ]= 2 #32
labels [93 :124 ]= 3 #31
labels [125 :155 ]= 4 #30
labels [156 :187 ]= 5 #31
labels [188 :]= 6 #30
names = ['ANGRY' ,'DISGUST' ,'FEAR' ,'HAPPY' ,'NEUTRAL' ,'SAD' ,'SURPRISE' ]
def getLabel (id ):
return ['ANGRY' ,'DISGUST' ,'FEAR' ,'HAPPY' ,'NEUTRAL' ,'SAD' ,'SURPRISE' ][id ]4.Convert the class label to one-hot encoding # convert class labels to on-hot encoding# conve
Y = np_utils .to_categorical (labels , num_classes )
#Shuffle the dataset
x ,y = shuffle (img_data ,Y , random_state = 2 )
# Split the dataset
X_train , X_test , y_train , y_test = train_test_split (x , y , test_size = 0.15 , random_state = 2 )# Defining the model
input_shape = img_data [0 ].shape
model = Sequential ()
# Feature Extraction
# model.add(Convolution2D(32,3,3, border_mode='same',input_shape=input_shape))
# model.add(Activation('relu'))
# model.add(Convolution2D(32, 3, 3))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.5))
# model.add(Convolution2D(64, 3, 3))
# model.add(Activation('relu'))
# # model.add(Convolution2D(64, 3, 3))
# # model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.5))
# model.add(Convolution2D(128, 3, 3))
# model.add(Activation('relu'))
# # model.add(Convolution2D(128, 3, 3))
# # model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.5))
#new Code
model .add (Convolution2D (6 , 5 , 5 , input_shape = input_shape , border_mode = 'same' ))
model .add (Activation ('relu' ))
model .add (MaxPooling2D (pool_size = (2 , 2 )))
model .add (Convolution2D (16 , 5 , 5 , border_mode = 'same' ))
model .add (Activation ('relu' ))
model .add (MaxPooling2D (pool_size = (2 , 2 )))
model .add (Convolution2D (120 , 5 , 5 ))
model .add (Activation ('relu' ))
model .add (Dropout (0.25 ))
model .add (Flatten ())
model .add (Dense (84 ))
model .add (Activation ('relu' ))
model .add (Dropout (0.5 ))
model .add (Dense (num_classes ))
model .add (Activation ('softmax' ))
# Classification
# model.add(Flatten())
# model.add(Dense(64))
# model.add(Activation('relu'))
# model.add(Dropout(0.5))
# model.add(Dense(num_classes))
# model.add(Activation('softmax'))
#Compile Model
model .compile (loss = 'categorical_crossentropy' , optimizer = 'adam' ,metrics = ["accuracy" ])C:\Users\ashish.patel\Anaconda3\lib\site-packages\ipykernel_launcher.py:30: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(6, (5, 5), input_shape=(128, 128,..., padding="same")`
C:\Users\ashish.patel\Anaconda3\lib\site-packages\ipykernel_launcher.py:34: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(16, (5, 5), padding="same")`
C:\Users\ashish.patel\Anaconda3\lib\site-packages\ipykernel_launcher.py:38: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(120, (5, 5))`
6.View Model Configuration model .summary ()
model .get_config ()
model .layers [0 ].get_config ()
model .layers [0 ].input_shape
model .layers [0 ].output_shape
model .layers [0 ].get_weights ()
np .shape (model .layers [0 ].get_weights ()[0 ])
model .layers [0 ].trainable _________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_13 (Conv2D) (None, 128, 128, 6) 456
_________________________________________________________________
activation_21 (Activation) (None, 128, 128, 6) 0
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 64, 64, 6) 0
_________________________________________________________________
conv2d_14 (Conv2D) (None, 64, 64, 16) 2416
_________________________________________________________________
activation_22 (Activation) (None, 64, 64, 16) 0
_________________________________________________________________
max_pooling2d_10 (MaxPooling (None, 32, 32, 16) 0
_________________________________________________________________
conv2d_15 (Conv2D) (None, 28, 28, 120) 48120
_________________________________________________________________
activation_23 (Activation) (None, 28, 28, 120) 0
_________________________________________________________________
dropout_10 (Dropout) (None, 28, 28, 120) 0
_________________________________________________________________
flatten_5 (Flatten) (None, 94080) 0
_________________________________________________________________
dense_9 (Dense) (None, 84) 7902804
_________________________________________________________________
activation_24 (Activation) (None, 84) 0
_________________________________________________________________
dropout_11 (Dropout) (None, 84) 0
_________________________________________________________________
dense_10 (Dense) (None, 7) 595
_________________________________________________________________
activation_25 (Activation) (None, 7) 0
=================================================================
Total params: 7,954,391
Trainable params: 7,954,391
Non-trainable params: 0
_________________________________________________________________
True
from keras import callbacks
filename = 'model_train_new.csv'
filepath = "Best-weights-my_model-{epoch:03d}-{loss:.4f}-{acc:.4f}.hdf5"
csv_log = callbacks .CSVLogger (filename , separator = ',' , append = False )
checkpoint = callbacks .ModelCheckpoint (filepath , monitor = 'val_loss' , verbose = 1 , save_best_only = True , mode = 'min' )
callbacks_list = [csv_log ,checkpoint ]
callbacks_list = [csv_log ]# Training
hist = model .fit (X_train , y_train , batch_size = 7 , nb_epoch = 30 , verbose = 1 , validation_data = (X_test , y_test ),callbacks = callbacks_list )C:\Users\ashish.patel\Anaconda3\lib\site-packages\keras\models.py:942: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.
warnings.warn('The `nb_epoch` argument in `fit` '
Train on 181 samples, validate on 32 samples
Epoch 1/30
181/181 [==============================] - 6s 35ms/step - loss: 0.2098 - acc: 0.9171 - val_loss: 0.4530 - val_acc: 0.9063
Epoch 2/30
181/181 [==============================] - 7s 36ms/step - loss: 0.2269 - acc: 0.9282 - val_loss: 0.3455 - val_acc: 0.8750
Epoch 3/30
181/181 [==============================] - 6s 35ms/step - loss: 0.2118 - acc: 0.9006 - val_loss: 0.4345 - val_acc: 0.9063
Epoch 4/30
181/181 [==============================] - 6s 35ms/step - loss: 0.1585 - acc: 0.9558 - val_loss: 0.4712 - val_acc: 0.8438
Epoch 5/30
181/181 [==============================] - 6s 35ms/step - loss: 0.1346 - acc: 0.9392 - val_loss: 0.4494 - val_acc: 0.9063
Epoch 6/30
181/181 [==============================] - 6s 34ms/step - loss: 0.1372 - acc: 0.9282 - val_loss: 0.5018 - val_acc: 0.8750
Epoch 7/30
181/181 [==============================] - 6s 35ms/step - loss: 0.1456 - acc: 0.9392 - val_loss: 0.4257 - val_acc: 0.8750
Epoch 8/30
181/181 [==============================] - 6s 35ms/step - loss: 0.1435 - acc: 0.9282 - val_loss: 0.3619 - val_acc: 0.9375
Epoch 9/30
181/181 [==============================] - 6s 35ms/step - loss: 0.1538 - acc: 0.9392 - val_loss: 0.3632 - val_acc: 0.9063
Epoch 10/30
181/181 [==============================] - 6s 35ms/step - loss: 0.0964 - acc: 0.9779 - val_loss: 0.4315 - val_acc: 0.9375
Epoch 11/30
181/181 [==============================] - 6s 34ms/step - loss: 0.1538 - acc: 0.9337 - val_loss: 0.5474 - val_acc: 0.8438
Epoch 12/30
181/181 [==============================] - 6s 35ms/step - loss: 0.1100 - acc: 0.9448 - val_loss: 0.5291 - val_acc: 0.9375
Epoch 13/30
181/181 [==============================] - 6s 34ms/step - loss: 0.1660 - acc: 0.9282 - val_loss: 0.3775 - val_acc: 0.9063
Epoch 14/30
181/181 [==============================] - 6s 35ms/step - loss: 0.0673 - acc: 0.9779 - val_loss: 0.5139 - val_acc: 0.8750
Epoch 15/30
181/181 [==============================] - 6s 35ms/step - loss: 0.0870 - acc: 0.9724 - val_loss: 0.6909 - val_acc: 0.8750
Epoch 16/30
181/181 [==============================] - 6s 35ms/step - loss: 0.1067 - acc: 0.9613 - val_loss: 0.6124 - val_acc: 0.8750
Epoch 17/30
181/181 [==============================] - 6s 35ms/step - loss: 0.1258 - acc: 0.9558 - val_loss: 0.4615 - val_acc: 0.8438
Epoch 18/30
181/181 [==============================] - 6s 34ms/step - loss: 0.1450 - acc: 0.9613 - val_loss: 0.4444 - val_acc: 0.8750
Epoch 19/30
181/181 [==============================] - 6s 35ms/step - loss: 0.1225 - acc: 0.9558 - val_loss: 0.4193 - val_acc: 0.9063
Epoch 20/30
181/181 [==============================] - 6s 35ms/step - loss: 0.0756 - acc: 0.9834 - val_loss: 0.4697 - val_acc: 0.9375
Epoch 21/30
181/181 [==============================] - 6s 34ms/step - loss: 0.1110 - acc: 0.9503 - val_loss: 0.5044 - val_acc: 0.8750
Epoch 22/30
181/181 [==============================] - 6s 35ms/step - loss: 0.1175 - acc: 0.9558 - val_loss: 0.5838 - val_acc: 0.8750
Epoch 23/30
181/181 [==============================] - 6s 34ms/step - loss: 0.0677 - acc: 0.9669 - val_loss: 0.7173 - val_acc: 0.9063
Epoch 24/30
181/181 [==============================] - 6s 34ms/step - loss: 0.0889 - acc: 0.9558 - val_loss: 0.6095 - val_acc: 0.8750
Epoch 25/30
181/181 [==============================] - 6s 35ms/step - loss: 0.1079 - acc: 0.9503 - val_loss: 0.5208 - val_acc: 0.8438
Epoch 26/30
181/181 [==============================] - 6s 34ms/step - loss: 0.1025 - acc: 0.9503 - val_loss: 0.4764 - val_acc: 0.9063
Epoch 27/30
181/181 [==============================] - 6s 36ms/step - loss: 0.0592 - acc: 0.9779 - val_loss: 0.4826 - val_acc: 0.9063
Epoch 28/30
181/181 [==============================] - 6s 34ms/step - loss: 0.1115 - acc: 0.9448 - val_loss: 0.3785 - val_acc: 0.9375
Epoch 29/30
181/181 [==============================] - 6s 34ms/step - loss: 0.1058 - acc: 0.9337 - val_loss: 0.5347 - val_acc: 0.9063
Epoch 30/30
181/181 [==============================] - 6s 36ms/step - loss: 0.1807 - acc: 0.9227 - val_loss: 0.4655 - val_acc: 0.9063
#Model Save
model .save_weights ('model_weights.h5' )
model .save ('model_keras.h5' )# visualizing losses and accuracy
% matplotlib inline
train_loss = hist .history ['loss' ]
val_loss = hist .history ['val_loss' ]
train_acc = hist .history ['acc' ]
val_acc = hist .history ['val_acc' ]
epochs = range (len (train_acc ))
plt .plot (epochs ,train_loss ,'r' , label = 'train_loss' )
plt .plot (epochs ,val_loss ,'b' , label = 'val_loss' )
plt .title ('train_loss vs val_loss' )
plt .legend ()
plt .figure ()
plt .plot (epochs ,train_acc ,'r' , label = 'train_acc' )
plt .plot (epochs ,val_acc ,'b' , label = 'val_acc' )
plt .title ('train_acc vs val_acc' )
plt .legend ()
plt .figure ()<matplotlib.figure.Figure at 0x18e18b8eb70>
<matplotlib.figure.Figure at 0x18e18b8eb70>
# Evaluating the model
score = model .evaluate (X_test , y_test , verbose = 0 )
print ('Test Loss:' , score [0 ])
print ('Test accuracy:' , score [1 ])
test_image = X_test [0 :1 ]
print (test_image .shape )
print (model .predict (test_image ))
print (model .predict_classes (test_image ))
print (y_test [0 :1 ])
res = model .predict_classes (X_test [:9 ])
plt .figure (figsize = (10 , 10 ))
for i in range (0 , 9 ):
plt .subplot (330 + 1 + i )
plt .imshow (X_test [i ],cmap = plt .get_cmap ('gray' ))
plt .gca ().get_xaxis ().set_ticks ([])
plt .gca ().get_yaxis ().set_ticks ([])
plt .ylabel ('prediction = %s' % getLabel (res [i ]), fontsize = 14 )
# show the plot
plt .show ()Test Loss: 0.4654931426048279
Test accuracy: 0.90625
(1, 128, 128, 3)
[[2.1821804e-09 8.9050531e-08 3.2704984e-06 1.3671537e-04 9.9984014e-01
1.5250735e-05 4.4816770e-06]]
[4]
[[0. 0. 0. 0. 1. 0. 0.]]
from sklearn .metrics import confusion_matrix
results = model .predict_classes (X_test )
cm = confusion_matrix (np .where (y_test == 1 )[1 ], results )
plt .matshow (cm )
plt .title ('Confusion Matrix' )
plt .colorbar ()
plt .ylabel ('True Label' )
plt .xlabel ('Predicted Label' )
plt .show ()
testimg_data_list = []
test_img = cv2 .imread ('men.jpg' , True )
test_img_resize = cv2 .resize (test_img ,(128 ,128 ))
testimg_data_list .append (test_img_resize )
testimg_data = np .array (testimg_data_list )
testimg_data = testimg_data .astype ('float32' )
testimg_data = testimg_data / 255
testimg_data .shape
print ("test image original shaape" ,testimg_data [0 ].shape )
print ("image original shaape" ,img_data [0 ].shape )
results = model .predict_classes (testimg_data )
plt .imshow (test_img ,cmap = plt .get_cmap ('Set2' ))
plt .gca ().get_xaxis ().set_ticks ([])
plt .gca ().get_yaxis ().set_ticks ([])
plt .xlabel ('prediction = %s' % getLabel (results [0 ]), fontsize = 25 )test image original shaape (128, 128, 3)
image original shaape (128, 128, 3)
Text(0.5,0,'prediction = SAD')
