# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle

"""
Explanation: Deep Learning
Assignment 3
Previously in 2_fullyconnected.ipynb, you trained a logistic regression and a neural network model.
The goal of this assignment is to explore regularization techniques.
End of explanation
"""


pickle_file = 'notMNIST.pickle'

with open(pickle_file, 'rb') as f:
  save = pickle.load(f)
  train_dataset = save['train_dataset']
  train_labels = save['train_labels']
  valid_dataset = save['valid_dataset']
  valid_labels = save['valid_labels']
  test_dataset = save['test_dataset']
  test_labels = save['test_labels']
  del save  # hint to help gc free up memory
  print('Training set', train_dataset.shape, train_labels.shape)
  print('Validation set', valid_dataset.shape, valid_labels.shape)
  print('Test set', test_dataset.shape, test_labels.shape)

"""
Explanation: First reload the data we generated in notmist.ipynb.
End of explanation
"""


image_size = 28
num_labels = 10

def reformat(dataset, labels):
  dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)
  # Map 2 to [0.0, 1.0, 0.0 ...], 3 to [0.0, 0.0, 1.0 ...]
  labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)
  return dataset, labels
train_dataset, train_labels = reformat(train_dataset, train_labels)
valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)
test_dataset, test_labels = reformat(test_dataset, test_labels)
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)

def accuracy(predictions, labels):
  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
          / predictions.shape[0])

"""
Explanation: Reformat into a shape that's more adapted to the models we're going to train:
- data as a flat matrix,
- labels as float 1-hot encodings.
End of explanation
"""


batch_size = 128
hidden_layer_length = 1024
regularization_factor1 = 0.01
regularization_factor2 = 0.01

graph = tf.Graph()
with graph.as_default():

  # Input data. For the training data, we use a placeholder that will be fed
  # at run time with a training minibatch.
  tf_train_dataset = tf.placeholder(tf.float32,
                                    shape=(batch_size, image_size * image_size))
  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
  tf_valid_dataset = tf.constant(valid_dataset)
  tf_test_dataset = tf.constant(test_dataset)
  tf_regularization_factor1 = tf.constant(regularization_factor1)
  tf_regularization_factor2 = tf.constant(regularization_factor2)
  
  # Variables.
  weights1 = tf.Variable(
    tf.truncated_normal([image_size * image_size, hidden_layer_length]))
  biases1 = tf.Variable(tf.zeros([hidden_layer_length]))
  
  weights2 = tf.Variable(
    tf.truncated_normal([hidden_layer_length, num_labels]))
  biases2 = tf.Variable(tf.zeros([num_labels]))
  
  # Training computation.
  train_logits1 = tf.matmul(tf_train_dataset, weights1) + biases1
  train_activations1 = tf.nn.relu(train_logits1)
  train_logits2 = tf.matmul(train_activations1, weights2) + biases2
  
  loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(train_logits2, tf_train_labels)) + \
    tf_regularization_factor1*tf.nn.l2_loss(weights1) + \
    tf_regularization_factor2*tf.nn.l2_loss(weights2)
  
  # Optimizer.
  optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)
  
  # Predictions for the training, validation, and test data.
  train_prediction = tf.nn.softmax(train_logits2)
  
  valid_logits1 = tf.matmul(tf_valid_dataset, weights1) + biases1
  valid_activations1 = tf.nn.relu(valid_logits1)
  valid_logits2 = tf.matmul(valid_activations1, weights2) + biases2
  valid_prediction = tf.nn.softmax(valid_logits2)
  
  test_logits1 = tf.matmul(tf_test_dataset, weights1) + biases1
  test_activations1 = tf.nn.relu(test_logits1)
  test_logits2 = tf.matmul(test_activations1, weights2) + biases2
  test_prediction = tf.nn.softmax(test_logits2)

num_steps = 3000

with tf.Session(graph=graph) as session:
  tf.initialize_all_variables().run()
  print("Initialized")
  for step in range(num_steps+1):
    # Pick an offset within the training data, which has been randomized.
    # Note: we could use better randomization across epochs.
    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
    # Generate a minibatch.
    batch_data = train_dataset[offset:(offset + batch_size), :]
    batch_labels = train_labels[offset:(offset + batch_size), :]
    # Prepare a dictionary telling the session where to feed the minibatch.
    # The key of the dictionary is the placeholder node of the graph to be fed,
    # and the value is the numpy array to feed to it.
    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
    _, l, predictions = session.run(
      [optimizer, loss, train_prediction], feed_dict=feed_dict)
    if (step % 500 == 0):
      print("Minibatch loss at step %d: %f" % (step, l))
      print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))
      print("Validation accuracy: %.1f%%" % accuracy(
        valid_prediction.eval(), valid_labels))
  print("Train accuracy: %.1f%%" % accuracy(train_prediction.eval(), train_labels))
  print("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))

"""
Explanation: Problem 1
Introduce and tune L2 regularization for both logistic and neural network models. Remember that L2 amounts to adding a penalty on the norm of the weights to the loss. In TensorFlow, you can compute the L2 loss for a tensor t using nn.l2_loss(t). The right amount of regularization should improve your validation / test accuracy.
End of explanation
"""


num_steps = 3000
furthest_training_example_idx = 1000

with tf.Session(graph=graph) as session:
  tf.initialize_all_variables().run()
  print("Initialized")
  for step in range(num_steps+1):
    # Pick an offset within the training data, which has been randomized.
    # Note: we could use better randomization across epochs.
    offset = (step * batch_size) % (furthest_training_example_idx - batch_size)
    # Generate a minibatch.
    batch_data = train_dataset[offset:(offset + batch_size), :]
    batch_labels = train_labels[offset:(offset + batch_size), :]
    # Prepare a dictionary telling the session where to feed the minibatch.
    # The key of the dictionary is the placeholder node of the graph to be fed,
    # and the value is the numpy array to feed to it.
    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
    _, l, predictions = session.run(
      [optimizer, loss, train_prediction], feed_dict=feed_dict)
    if (step % 500 == 0):
      print("Minibatch loss at step %d: %f" % (step, l))
      print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))
      print("Validation accuracy: %.1f%%" % accuracy(
        valid_prediction.eval(), valid_labels))
  print("Train accuracy: %.1f%%" % accuracy(train_prediction.eval(), train_labels))
  print("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))

"""
Explanation: Problem 2
Let's demonstrate an extreme case of overfitting. Restrict your training data to just a few batches. What happens?
End of explanation
"""


batch_size = 128
hidden_layer_length = 1024
regularization_factor1 = 0.01
regularization_factor2 = 0.01
dropout_keep_prob = 0.2

graph = tf.Graph()
with graph.as_default():

  # Input data. For the training data, we use a placeholder that will be fed
  # at run time with a training minibatch.
  tf_train_dataset = tf.placeholder(tf.float32,
                                    shape=(batch_size, image_size * image_size))
  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
  tf_valid_dataset = tf.constant(valid_dataset)
  tf_test_dataset = tf.constant(test_dataset)
  tf_regularization_factor1 = tf.constant(regularization_factor1)
  tf_regularization_factor2 = tf.constant(regularization_factor2)
  tf_dropout_keep_prob = tf.constant(dropout_keep_prob)
  
  # Variables.
  weights1 = tf.Variable(
    tf.truncated_normal([image_size * image_size, hidden_layer_length]))
  biases1 = tf.Variable(tf.zeros([hidden_layer_length]))
  
  weights2 = tf.Variable(
    tf.truncated_normal([hidden_layer_length, num_labels]))
  biases2 = tf.Variable(tf.zeros([num_labels]))
  
  # Training computation.
  train_logits1 = tf.matmul(tf_train_dataset, weights1) + biases1
  train_activations1 = tf.nn.relu(train_logits1)
  dropped_train_activations1 = tf.nn.dropout(train_activations1, tf_dropout_keep_prob)
  dropped_train_logits2 = tf.matmul(dropped_train_activations1, weights2) + biases2
  
  loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(dropped_train_logits2, tf_train_labels)) + \
    tf_regularization_factor1*tf.nn.l2_loss(weights1) + \
    tf_regularization_factor2*tf.nn.l2_loss(weights2)
  
  # Optimizer.
  optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)
  
  # Predictions for the training, validation, and test data.
  undropped_train_logits2 = tf.matmul(train_activations1, weights2) + biases2
  train_prediction = tf.nn.softmax(undropped_train_logits2)
  
  valid_logits1 = tf.matmul(tf_valid_dataset, weights1) + biases1
  valid_activations1 = tf.nn.relu(valid_logits1)
  valid_logits2 = tf.matmul(valid_activations1, weights2) + biases2
  valid_prediction = tf.nn.softmax(valid_logits2)
  
  test_logits1 = tf.matmul(tf_test_dataset, weights1) + biases1
  test_activations1 = tf.nn.relu(test_logits1)
  test_logits2 = tf.matmul(test_activations1, weights2) + biases2
  test_prediction = tf.nn.softmax(test_logits2)

num_steps = 3000
furthest_training_example_idx = 1000

with tf.Session(graph=graph) as session:
  tf.initialize_all_variables().run()
  print("Initialized")
  for step in range(num_steps+1):
    # Pick an offset within the training data, which has been randomized.
    # Note: we could use better randomization across epochs.
    offset = (step * batch_size) % (furthest_training_example_idx - batch_size)
    # Generate a minibatch.
    batch_data = train_dataset[offset:(offset + batch_size), :]
    batch_labels = train_labels[offset:(offset + batch_size), :]
    # Prepare a dictionary telling the session where to feed the minibatch.
    # The key of the dictionary is the placeholder node of the graph to be fed,
    # and the value is the numpy array to feed to it.
    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
    _, l, predictions = session.run(
      [optimizer, loss, train_prediction], feed_dict=feed_dict)
    if (step % 500 == 0):
      print("Minibatch loss at step %d: %f" % (step, l))
      print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))
      print("Validation accuracy: %.1f%%" % accuracy(
        valid_prediction.eval(), valid_labels))
  print("Train accuracy: %.1f%%" % accuracy(train_prediction.eval(), train_labels))
  print("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))

"""
Explanation: Problem 3
Introduce Dropout on the hidden layer of the neural network. Remember: Dropout should only be introduced during training, not evaluation, otherwise your evaluation results would be stochastic as well. TensorFlow provides nn.dropout() for that, but you have to make sure it's only inserted during training.
What happens to our extreme overfitting case?
End of explanation
"""


