# -*- coding: utf-8 -*-
"""
Created on Sat Nov 28 22:22:28 2020

@author: <NAME>
"""

# Import Necessary Modules: Tensorflow, Matplotlib, Numpy, Pathlib
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
import pathlib

# Download the flower dataset from the tensorflow tutorial  
dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)

# Set the basic parameters
batch_size = 32
img_height = 180
img_width = 180

# Divide training and validation sets
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.95,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

# Print class
class_names = train_ds.class_names
print(class_names)

# Plot images of flowers
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
  for i in range(9):
    ax = plt.subplot(3, 3, i + 1)
    plt.imshow(images[i].numpy().astype("uint8"))
    plt.title(class_names[labels[i]])
    plt.axis("off")

# Set the training parameters
AUTOTUNE = tf.data.experimental.AUTOTUNE

train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

num_classes = 5

# Define the augmentation layer
data_augmentation = keras.Sequential([
    layers.experimental.preprocessing.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
    layers.experimental.preprocessing.RandomRotation(0.5),
    layers.experimental.preprocessing.RandomZoom(0.5),
])

# Define the network layers
model = Sequential([
    data_augmentation,
    layers.experimental.preprocessing.Rescaling(1./255),
    layers.Conv2D(30, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    #layers.BatchNormalization(),
    #layers.Dropout(0.4),
    layers.Conv2D(60, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    #layers.BatchNormalization(),
    #layers.Dropout(0.4),
    #layers.Conv2D(120, 3, padding='same', activation='relu'),
    #layers.MaxPooling2D(),
    #layers.BatchNormalization(),
    #layers.Dropout(0.2),
    layers.Flatten(),
    layers.Dense(120, activation='relu'),
    layers.Dense(num_classes)
])

model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])

model.summary()

# Training
epochs = 100
history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=epochs
)

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(epochs)

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()