Merge branch 'SCIT' of http://gitlab.sliit.lk/2020-046/child-intelligent-assistant-tool into SCIT

cnn.py

-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-@author: GUnasinghe M.D.
-"""
-
-# CNN classifier
-
-# Building architecture of our CNN classifier
-import keras
-from keras.models import Sequential
-from keras.layers import Convolution2D
-from keras.layers import MaxPooling2D
-from keras.layers import Flatten
-from keras.layers import Dense
-
-# Initialising the CNN
-classifier = Sequential()
-
-# Step - 1 Convolution
-classifier.add(Convolution2D(
-    16, 3, 3, input_shape=(28, 28, 3), activation='relu'))
-
-# Step - 2 Pooling
-classifier.add(MaxPooling2D(pool_size=(2, 2)))
-
-classifier.add(Convolution2D(32, 3, 3, activation='relu'))
-classifier.add(MaxPooling2D(pool_size=(2, 2)))
-
-# Step - 3 Flattening
-classifier.add(Flatten())
-
-# Step - 4 Full connection -> First layer input layer then hidden layer
-# and last softmax layer
-classifier.add(Dense(56, activation='relu', kernel_initializer='uniform'))
-classifier.add(Dense(3, activation='softmax', kernel_initializer='uniform'))
-
-# Compiling the CNN
-classifier.compile(
-    optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
-
-
-# Image Preprocessing
-from keras.preprocessing.image import ImageDataGenerator
-
-train_datagen = ImageDataGenerator(
-    rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
-
-test_datagen = ImageDataGenerator(rescale=1. / 255)
-
-training_set = train_datagen.flow_from_directory(
-    'shapes/train', target_size=(28, 28), batch_size=1, class_mode='categorical')
-#X_images, y_labels = training_set.filenames, training_set.classes
-test_set = test_datagen.flow_from_directory(
-    'shapes/test', target_size=(28, 28), batch_size=1, class_mode='categorical')
-
-# Logging the training of models
-from keras.callbacks import CSVLogger, EarlyStopping
-csv_logger = CSVLogger('log.csv', append=True, separator=';')
-early_stopping_monitor = EarlyStopping(patience=5)
-
-steps_per_epoch = len(training_set.filenames)  # 300
-validation_steps = len(test_set.filenames)  # 90
-
-model_info = classifier.fit_generator(training_set, steps_per_epoch=steps_per_epoch, epochs=25, validation_data=test_set,
-                                      validation_steps=validation_steps, callbacks=[csv_logger, early_stopping_monitor])
-
-classifier.save("drawing_classification.h5")
-
-# plot model history after each epoch
-from visulization import plot_model_history
-plot_model_history(model_info)

visulization.py

-import matplotlib.pyplot as plt
-import numpy as np
-
-
-def plot_model_history(model_history):
-    fig, axs = plt.subplots(1, 2, figsize=(15, 5))
-    # summarize history for accuracy
-    axs[0].plot(range(1, len(model_history.history['acc']) + 1),
-                model_history.history['acc'])
-    axs[0].plot(range(1, len(model_history.history['val_acc']) + 1),
-                model_history.history['val_acc'])
-    axs[0].set_title('Model Accuracy')
-    axs[0].set_ylabel('Accuracy')
-    axs[0].set_xlabel('Epoch')
-    axs[0].set_xticks(np.arange(1, len(model_history.history[
-                      'acc']) + 1), len(model_history.history['acc']) / 10)
-    axs[0].legend(['train', 'val'], loc='best')
-    # summarize history for loss
-    axs[1].plot(range(1, len(model_history.history['loss']) + 1),
-                model_history.history['loss'])
-    axs[1].plot(range(1, len(model_history.history['val_loss']) + 1),
-                model_history.history['val_loss'])
-    axs[1].set_title('Model Loss')
-    axs[1].set_ylabel('Loss')
-    axs[1].set_xlabel('Epoch')
-    axs[1].set_xticks(np.arange(1, len(model_history.history[
-                      'loss']) + 1), len(model_history.history['loss']) / 10)
-    axs[1].legend(['train', 'val'], loc='best')
-    plt.show()