#!/usr/bin/env python
# coding: utf-8

# Deep Learning with TensorFlow
# =============
# 
# Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google
# 
# Setup
# ------------
# 
# Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).
# 
# Exercise 2
# ------------
# 
# Previously in `1_notmnist.ipynb`, we created a pickle with formatted datasets for training, development and testing on the [notMNIST dataset](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html).
# 
# The goal of this exercise is to progressively train deeper and more accurate models using TensorFlow.

# In[ ]:


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


# First reload the data we generated in `1_notmist.ipynb`.

# In[ ]:


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


# 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.

# In[ ]:


image_size = 28
num_labels = 10

def reformat(dataset, labels):
  dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)
  # Map 0 to [1.0, 0.0, 0.0 ...], 1 to [0.0, 1.0, 0.0 ...]
  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


# We're first going to train a multinomial logistic regression using simple gradient descent.
# 
# TensorFlow works like this:
# * First you describe the computation that you want to see performed: what the inputs, the variables, and the operations look like. These get created as nodes over a computation graph. This description is all contained within the block below:
# 
#       with graph.as_default():
#           ...
# 
# * Then you can run the operations on this graph as many times as you want by calling `session.run()`, providing it outputs to fetch from the graph that get returned. This runtime operation is all contained in the block below:
# 
#       with tf.Session(graph=graph) as session:
#           ...
# 
# Let's load all the data into TensorFlow and build the computation graph corresponding to our training:

# In[ ]:


# With gradient descent training, even this much data is prohibitive.
# Subset the training data for faster turnaround.
train_subset = 10000

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

  # Input data.
  # Load the training, validation and test data into constants that are
  # attached to the graph.
  tf_train_dataset = tf.constant(train_dataset[:train_subset, :])
  tf_train_labels = tf.constant(train_labels[:train_subset])
  tf_valid_dataset = tf.constant(valid_dataset)
  tf_test_dataset = tf.constant(test_dataset)
  
  # Variables.
  # These are the parameters that we are going to be training. The weight
  # matrix will be initialized using random valued following a (truncated)
  # normal distribution. The biases get initialized to zero.
  weights = tf.Variable(
    tf.truncated_normal([image_size * image_size, num_labels]))
  biases = tf.Variable(tf.zeros([num_labels]))
  
  # Training computation.
  # We multiply the inputs with the weight matrix, and add biases. We compute
  # the softmax and cross-entropy (it's one operation in TensorFlow, because
  # it's very common, and it can be optimized). We take the average of this
  # cross-entropy across all training examples: that's our loss.
  logits = tf.matmul(tf_train_dataset, weights) + biases
  loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))
  
  # Optimizer.
  # We are going to find the minimum of this loss using gradient descent.
  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
  
  # Predictions for the training, validation, and test data.
  # These are not part of training, but merely here so that we can report
  # accuracy figures as we train.
  train_prediction = tf.nn.softmax(logits)
  valid_prediction = tf.nn.softmax(
    tf.matmul(tf_valid_dataset, weights) + biases)
  test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)


# Let's run this computation and iterate:

# In[ ]:


num_steps = 801

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

with tf.Session(graph=graph) as session:
  # This is a one-time operation which ensures the parameters get initialized as
  # we described in the graph: random weights for the matrix, zeros for the
  # biases. 
 tf.global_variables_initializer().run()
  print 'Initialized'
  for step in xrange(num_steps):
    # Run the computations. We tell .run() that we want to run the optimizer,
    # and get the loss value and the training predictions returned as numpy
    # arrays.
    _, l, predictions = session.run([optimizer, loss, train_prediction])
    if (step % 100 == 0):
      print 'Loss at step', step, ':', l
      print 'Training accuracy: %.1f%%' % accuracy(
        predictions, train_labels[:train_subset, :])
      # Calling .eval() on valid_prediction is basically like calling run(), but
      # just to get that one numpy array. Note that it recomputes all its graph
      # dependencies.
      print 'Validation accuracy: %.1f%%' % accuracy(
        valid_prediction.eval(), valid_labels)
  print 'Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels)


# Let's now switch to stochastic gradient descent training instead, which is much faster.
# 
# The graph will be similar, except that instead of holding all the training data into a constant node, we create a `Placeholder` node which will be fed actual data at every call of `sesion.run()`.

# In[ ]:


batch_size = 128

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)
  
  # Variables.
  weights = tf.Variable(
    tf.truncated_normal([image_size * image_size, num_labels]))
  biases = tf.Variable(tf.zeros([num_labels]))
  
  # Training computation.
  logits = tf.matmul(tf_train_dataset, weights) + biases
  loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))
  
  # Optimizer.
  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
  
  # Predictions for the training, validation, and test data.
  train_prediction = tf.nn.softmax(logits)
  valid_prediction = tf.nn.softmax(
    tf.matmul(tf_valid_dataset, weights) + biases)
  test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)


# Let's run it:

# In[ ]:


num_steps = 3001

with tf.Session(graph=graph) as session:
  tf.global_variables_initializer().run()
  print "Initialized"
  for step in xrange(num_steps):
    # 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", step, ":", l
      print "Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels)
      print "Validation accuracy: %.1f%%" % accuracy(
        valid_prediction.eval(), valid_labels)
  print "Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels)


# ---
# Problem
# -------
# 
# Turn the logistic regression example with SGD into a 1-hidden layer neural network with rectified linear units (nn.relu()) and 1024 hidden nodes. This model should improve your validation / test accuracy.
# 
# ---