#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 30 17:27:15 2019

Train your first neural network: basic classification

@author: eirikespe
"""

from __future__ import absolute_import, division, print_function, unicode_literals

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

print(tf.__version__)


# Import and load the Fashion MNIST data directly from TensorFlow:
fashion_mnist = keras.datasets.fashion_mnist

# The train_images and train_labels arrays are the training set
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

# Labels for the images
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

# Let's explore the format of the dataset before training the model. 
# The following shows there are 60,000 images in the training set, 
#  with each image represented as 28 x 28 pixels:
train_images.shape

# Likewise, there are 60,000 labels in the training set:
len(train_labels)

# Each label is an integer between 0 and 9:
train_labels


# There are 10,000 images in the test set. Again, each image is represented as 28 x 28 pixels:
test_images.shape

# And the test set contains 10,000 images labels:
len(test_labels)


#--- Pre-processing ----

# The data must be preprocessed before training the network. 
#  If you inspect the first image in the training set, you will see that 
#  the pixel values fall in the range of 0 to 255:
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()



#  We scale the values to a range of 0 to 1 before feeding them to the neural 
#   network model. To do so, we divide the values by 255. It's important that 
#   the training set and the testing set be pre-processed in the same way:

train_images = train_images / 255.0

test_images = test_images / 255.0


# To verify that the data is in the correct format and that we're ready to 
#  build and train the network, let's display the first 25 images from the 
#  training set and display the class name below each image.
plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()



#--- Build the model ----

# Building the neural network requires configuring the layers of the model, 
#  then compiling the model.

#--- Set up the layers ---#
# The basic building block of a neural network is the layer. 
# Layers extract representations from the data fed into them. 
# Hopefully, these representations are meaningful for the problem at hand.

# Most of deep learning consists of chaining together simple layers. 
#  Most layers, such as tf.keras.layers.Dense, have parameters that are 
#  learned during training.

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])


#--- Compile the model ---#
# Before the model is ready for training, it needs a few more settings. 
#  These are added during the model's compile step:

#Loss function — This measures how accurate the model is during training. 
#   We want to minimize this function to "steer" the model in the right direction.
#Optimizer —This is how the model is updated based on the data it sees and 
#    its loss function.
#Metrics — Used to monitor the training and testing steps. 

#   The following example uses accuracy, the fraction of the images that 
#    are correctly classified.
    
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

#--- Train the model ----

# Training the neural network model requires the following steps:

# 1. Feed the training data to the model. In this example, the training data is 
#     in the train_images and train_labels arrays.

# 2. The model learns to associate images and labels.

# 3. We ask the model to make predictions about a test set—in this example, 
#     the test_images array. We verify that the predictions match the labels 
#     from the test_labels array.

# To start training, call the model.fit method—so called because it "fits" the 
#  model to the training data:

model.fit(train_images, train_labels, epochs=10)



#--- Evaluate accuracy ----

# Next, compare how the model performs on the test dataset:
test_loss, test_acc = model.evaluate(test_images, test_labels)

print('\nTest accuracy:', test_acc)


#--- Make predictions ----

# With the model trained, we can use it to make predictions about some images.
predictions = model.predict(test_images)

# Here, the model has predicted the label for each image in the testing set. 
# Let's take a look at the first prediction:
predictions[0]

# A prediction is an array of 10 numbers. They represent the 
#  model's confidence" that the image corresponds to each of the 10 different 
#   articles of clothing. We can see which label has the highest confidence value:
np.argmax(predictions[0])

# Check what type of clothing this is
class_names[9]

# Examining the test label shows that this classification is correct:
test_labels[0]


# We can graph this to look at the full set of 10 class predictions.
def plot_image(i, predictions_array, true_label, img):
  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])

  plt.imshow(img, cmap=plt.cm.binary)

  predicted_label = np.argmax(predictions_array)
  if predicted_label == true_label:
    color = 'blue'
  else:
    color = 'red'

  plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
                                100*np.max(predictions_array),
                                class_names[true_label]),
                                color=color)

def plot_value_array(i, predictions_array, true_label):
  predictions_array, true_label = predictions_array[i], true_label[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])
  thisplot = plt.bar(range(10), predictions_array, color="#777777")
  plt.ylim([0, 1])
  predicted_label = np.argmax(predictions_array)

  thisplot[predicted_label].set_color('red')
  thisplot[true_label].set_color('blue')



# Let's look at the 0th image, predictions, and prediction array.
  i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions,  test_labels)
plt.show()


# And the 12th image
i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions,  test_labels)
plt.show()


# Let's plot several images with their predictions. Correct prediction labels 
#  are blue and incorrect prediction labels are red. The number gives the 
#   percentage (out of 100) for the predicted label. Note that the model can be 
#    wrong even when very confident.


# Plot the first X test images, their predicted labels, and the true labels.
# Color correct predictions in blue and incorrect predictions in red.
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
  plt.subplot(num_rows, 2*num_cols, 2*i+1)
  plot_image(i, predictions, test_labels, test_images)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_value_array(i, predictions, test_labels)
plt.show()


# Finally, use the trained model to make a prediction about a single image.


# Grab an image from the test dataset.
img = test_images[0]

print(img.shape)

# tf.keras models are optimized to make predictions on a batch, or collection, 
#   of examples at once. Accordingly, even though we're using a single image, 
#    we need to add it to a list:

# Add the image to a batch where it's the only member.
img = (np.expand_dims(img,0))

print(img.shape)

# Now predict the correct label for this image:
predictions_single = model.predict(img)

print(predictions_single)

# Make a plot
plot_value_array(0, predictions_single, test_labels)
_ = plt.xticks(range(10), class_names, rotation=45)


# model.predict returns a list of lists — one list for each image in the 
#   batch of data. Grab the predictions for our (only) image in the batch:

np.argmax(predictions_single[0])

#   And, as before, the model predicts a label of 9.

