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colab.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 7 21:07:51 2019
@author: saireddy
"""
__author__ = 'Sai Reddy'
from keras.layers import Conv2D, MaxPool2D, Dense, Flatten
from keras.layers import BatchNormalization
from keras.layers import Dropout, ZeroPadding2D
from keras.optimizers import Adam, SGD, RMSprop
from keras.preprocessing import image
from keras.models import Sequential
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
import numpy as np
def data_prep():
train_gen = image.ImageDataGenerator(rescale = 1./255,
featurewise_std_normalization=True,
samplewise_std_normalization=True,
zca_whitening=False, zca_epsilon=1e-06,
rotation_range=10, width_shift_range=0.0,
height_shift_range=0.0, brightness_range=(0.2, 0.2),
shear_range=0.2, zoom_range=0.2,
channel_shift_range=0.0, fill_mode='nearest',
cval=0.0, horizontal_flip=True, vertical_flip=True,
data_format=None, validation_split=0.0, dtype=None)
test_gen = image.ImageDataGenerator(rescale = 1./255)
val_gen = image.ImageDataGenerator(rescale = 1./255)
train_data = train_gen.flow_from_directory("/content/images/Training/",
target_size = (48, 48),
classes = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral'],
class_mode = 'categorical',
batch_size = 32,seed = 1,
color_mode = "grayscale")
test_data = test_gen.flow_from_directory("/content/images/PublicTest/",
target_size = (48, 48),
classes = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral'],
class_mode = 'categorical',
batch_size = 32,seed = 1,
color_mode = "grayscale")
val_data = val_gen.flow_from_directory("/content/images/PrivateTest/",
target_size = (48, 48),
classes = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral'],
class_mode = 'categorical',
batch_size = 32,seed = 1,
color_mode = "grayscale")
return train_data, test_data, val_data
def model_creation():
model = Sequential()
model.add(Conv2D(8, kernel_size=(3, 3), strides=(1, 1), padding='SAME',
input_shape= (48, 48, 1),activation = 'relu')) ##Input Layers
model.add(Conv2D(8, kernel_size=(3, 3), strides=(1, 1), padding='SAME',activation = 'relu'))
model.add(BatchNormalization(axis = -1))
model.add(MaxPool2D(pool_size=(2, 2), strides=(1, 1), padding = 'SAME')) #Max Pool1
model.add(Dropout(0.5))
model.add(ZeroPadding2D(padding = (2, 2)))
model.add(Conv2D(16, kernel_size=(3, 3), strides=(1, 1), padding='SAME',activation = 'relu'))
model.add(BatchNormalization(axis = -1))
model.add(Conv2D(16, kernel_size=(3, 3), strides=(1, 1), padding='SAME',activation = 'relu'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(1, 1), padding = 'SAME')) #MaxPool2
model.add(Dropout(0.5))
model.add(BatchNormalization(axis = -1))
model.add(ZeroPadding2D(padding = (2, 2)))
model.add(Conv2D(32, kernel_size=(3, 3), strides=(1, 1), padding='SAME',activation = 'relu'))
model.add(BatchNormalization(axis = -1))
model.add(Conv2D(32, kernel_size=(3, 3), strides=(1, 1), padding='SAME',activation = 'relu'))
model.add(BatchNormalization(axis = -1))
model.add(MaxPool2D(pool_size=(2, 2), strides=(1, 1), padding = 'SAME')) #MaxPool3
model.add(Dropout(0.5))
model.add(BatchNormalization(axis = -1))
model.add(ZeroPadding2D(padding = (2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(1, 1), padding='SAME',activation = 'relu'))
model.add(BatchNormalization(axis = -1))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(1, 1), padding='SAME',activation = 'relu'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(1, 1), padding = 'SAME')) #MaxPool4
model.add(Dropout(0.5))
model.add(BatchNormalization(axis = -1))
model.add(ZeroPadding2D(padding = (2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), strides=(1, 1), padding='SAME',activation = 'relu'))
model.add(Conv2D(128, kernel_size=(3, 3), strides=(1, 1), padding='SAME',activation = 'relu'))
model.add(BatchNormalization(axis = -1))
model.add(MaxPool2D(pool_size=(2, 2), strides=(1, 1), padding = 'SAME')) #MaxPool5
model.add(Dropout(0.5))
model.add(Flatten())
#Dense1
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization(axis = -1))
model.add(Dropout(0.5))
#Dense2
model.add(Dense(256, activation='relu'))
model.add(BatchNormalization(axis = -1))
model.add(Dropout(0.5))
#Dense3
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization(axis = -1))
model.add(Dropout(0.5))
#Dense4
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))
model.summary()
return model
def Training_data(model, train_data, test_data, val_data):
#Optimizers
Ad = RMSprop(lr = 0.001)
#Compile Model
model.compile(optimizer = Ad,
loss = 'categorical_crossentropy',metrics=['accuracy'])
#Fitting Model
history = model.fit_generator(train_data, steps_per_epoch=35887//32,
epochs=100, validation_data=val_data,
validation_steps= 500)
return history
def test_model(model, test_data):
score = model.evaluate_generator(test_data,
steps = 500)
#printAccuracy
print("Accuracyloss:-", score[0])
print("AccuracyScore",score[1] )
model.save("finalweights1.h5")
return score[0], score[1]
def plotting(history):
import matplotlib.pyplot as plt
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
if __name__ == "__main__":
data = data_prep()
train_data, test_data, val_data = data
model = model_creation()
history = Training_data(model, train_data, test_data, val_data)
plotting(history)
score = test_model(model, test_data)