CNN for shapes identification

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)