# -*- coding: utf-8 -*-
"""
'cnn_demo.py'

Convolutional Neural Network implementation on Keras.
TensorFlow is an open-sourced end-to-end platform, a library for multiple 
machine learning tasks, while Keras is a high-level neural network library 
that runs on top of TensorFlow. 
David Pan, UAH
"""

# Setup
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers

# Prepare the data

# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)

# MNIST a dataset of 60,000 28x28 grayscale images of the 10 digits, 
# along with a test set of 10,000 images.

# Load the data and split it between train and test sets
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()


# Make sure images have shape (28, 28, 1)
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
print("x_train shape:", x_train.shape)
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")


# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

# Build the model

model = keras.Sequential(
    [
        keras.Input(shape=input_shape),
        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dense(num_classes, activation="softmax"),
    ]
)

model.summary()

# 18496 parameters = 64*(32*3*3+1) 

# Train the model
batch_size = 128
epochs = 3

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)

# Evaluate the trained model
score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])