EmoRecog

from __future__ import print_function
import numpy as np

# get the data

filname = '/content/drive/My Drive/fer2013.csv'
label_map = ['Anger', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']


def getData(filname):
    # images are 48x48
    # N = 35887
    Y = []
    X = []
    first = True
    for line in open(filname):
        if first:
            first = False
        else:
            row = line.split(',')
            Y.append(int(row[0]))
            X.append([int(p) for p in row[1].split()])

    X, Y = np.array(X) / 255.0, np.array(Y)
    return X, Y


X, Y = getData(filname)
num_class = len(set(Y))


# To see number of training data point available for each label
def balance_class(Y):
    num_class = set(Y)
    count_class = {}
    for i in range(len(num_class)):
        count_class[i] = sum([1 for y in Y if y == i])
    return count_class


balance = balance_class(Y)

# keras with tensorflow backend
N, D = X.shape
X = X.reshape(N, 48, 48, 1)

# Split in  training set : validation set :  testing set in 80:10:10
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.1, random_state=0)
y_train = (np.arange(num_class) == y_train[:, None]).astype(np.float32)
y_test = (np.arange(num_class) == y_test[:, None]).astype(np.float32)

from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D, ZeroPadding2D
from keras.metrics import categorical_accuracy
import tenserflow as tf
from keras.optimizers import *
from keras.layers.normalization import BatchNormalization

batch_size = 128
epochs = 30

model = Sequential()
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(64, (3, 3), border_mode='same', input_shape=(48, 48, 1)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

# 2nd Convolution layer
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(128, (3, 3), border_mode='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))

# 3rd Convolution layer
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), border_mode='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))

# 4th Convolution layer
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), border_mode='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(1024, (3, 3), border_mode='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))

# Flattening
model.add(Flatten())

# Fully connected layer 1st layer
model.add(Dense(512))
model.add(BatchNormalization())
model.add(Activation('relu'))
# model.add(Dropout(0.2))

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

model.compile(optimizer=tf.train.AdamOptimizer(0.0001), loss='categorical_crossentropy', metrics=[categorical_accuracy])

model.fit(X_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_split=0.1111)

# Model will predict the probability values for 7 labels for a test image
score = model.predict(X_test)
print(model.summary())

new_X = [np.argmax(item) for item in score]
y_test2 = [np.argmax(item) for item in y_test]

# Calculating categorical accuracy taking label having highest probability
accuracy = [(x == y) for x, y in zip(new_X, y_test2)]
print(" Accuracy on Test set : ", np.mean(accuracy))