DL_A3.py

# -*- coding: utf-8 -*-
"""
Created on Tue May 16 12:30:50 2017

@author: Seagle

"""
import os
os.chdir("C:/SeagleDLTrial")
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle

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)

image_size = 28
num_labels = 10

def reformat(dataset, labels):
  dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)
  # Map 1 to [0.0, 1.0, 0.0 ...], 2 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])
  
#%% Problem 1. The l2 penalty
batch_size = 128
lambda_l2 = 0.05

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(labels=tf_train_labels,
                                            logits=logits)) + lambda_l2 * tf.nn.l2_loss(weights)
  # Does not matter whether the loss is inside or outside.
  # 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)
  
#%% Test correctness.
num_steps = 3001

with tf.Session(graph=graph) as session:
  tf.global_variables_initializer().run()
  print("Initialized")
  for step in range(num_steps):
    # Pick an offset within the training data, which has been randomized.
    # Note: we could use better randomization across epochs.
    # This is not a real stochastic training batch.
    # It just prevents the batch from going beyond the range.
    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.
    # This is some weird Python way of specifying the data.
    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("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels)) 
#%% Online Version.
#%%
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)
  beta_regul = tf.placeholder(tf.float32)
  
  # 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)) + beta_regul * tf.nn.l2_loss(weights)
  
  # 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)
#%% First run
num_steps = 3001

with tf.Session(graph=graph) as session:
  tf.initialize_all_variables().run()
  print("Initialized")
  for step in range(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, beta_regul : 1e-3}
    _, 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("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))  
#%% Search for the lambda in a range.
num_steps = 3001
regul_val = [pow(10, i) for i in np.arange(-4, -2, 0.1)]
accuracy_val = []

for regul in regul_val:
  with tf.Session(graph=graph) as session:
    tf.initialize_all_variables().run()
    for step in range(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, 
                   beta_regul : regul}
      _, l, predictions = session.run(
        [optimizer, loss, train_prediction], feed_dict=feed_dict)
    accuracy_val.append(accuracy(valid_prediction.eval(), valid_labels))
#%% Plot accuracy
import matplotlib.pyplot as plt
plt.semilogx(regul_val, accuracy_val)
plt.grid(True)
plt.title('Test accuracy by regularization (logistic)')
plt.show()

#%% Apply the penalty to the neural network data.
batch_size = 128
num_hidden_nodes = 1024

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)
  beta_regul = tf.placeholder(tf.float32)
  
  # Variables.
  weights1 = tf.Variable(
    tf.truncated_normal([image_size * image_size, num_hidden_nodes]))
  biases1 = tf.Variable(tf.zeros([num_hidden_nodes]))
  weights2 = tf.Variable(
    tf.truncated_normal([num_hidden_nodes, num_labels]))
  biases2 = tf.Variable(tf.zeros([num_labels]))
  
  # Training computation.
  lay1_train = tf.nn.relu(tf.matmul(tf_train_dataset, weights1) + biases1)
  logits = tf.matmul(lay1_train, weights2) + biases2
  loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels)) + \
      beta_regul * (tf.nn.l2_loss(weights1) + tf.nn.l2_loss(weights2))
  
  # Optimizer.
  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
  
  # Predictions for the training, validation, and test data.
  train_prediction = tf.nn.softmax(logits)
  lay1_valid = tf.nn.relu(tf.matmul(tf_valid_dataset, weights1) + biases1)
  valid_prediction = tf.nn.softmax(tf.matmul(lay1_valid, weights2) + biases2)
  lay1_test = tf.nn.relu(tf.matmul(tf_test_dataset, weights1) + biases1)
  test_prediction = tf.nn.softmax(tf.matmul(lay1_test, weights2) + biases2)
#%% Trial run
num_steps = 3001

with tf.Session(graph=graph) as session:
  tf.global_variables_initializer().run()
  print("Initialized")
  for step in range(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, beta_regul : 1e-3}
    _, 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("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))  
#%% Search for beta
num_steps = 3001
regul_val = [pow(10, i) for i in np.arange(-4, -2, 0.1)]
accuracy_val = []

for regul in regul_val:    
  with tf.Session(graph=graph) as session:
    tf.global_variables_initializer().run()
    for step in range(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, beta_regul : regul}
      _, l, predictions = session.run(
        [optimizer, loss, train_prediction], feed_dict=feed_dict)
    accuracy_val.append(accuracy(valid_prediction.eval(), valid_labels))  
#%%
import matplotlib.pyplot as plt
plt.semilogx(regul_val, accuracy_val)
plt.grid(True)
plt.title('Test accuracy by regularization (logistic)')
plt.show()
# Cute function to find the maximum (this only returns the first one)
accuracy_val.index(max(accuracy_val))
#%%
aa = [0, 2, 5, 5, 4, -1, 3]
[i for i, j in enumerate(aa) if j == max(aa)]
#%% enumerate list both the index and element.
for i, j in enumerate(aa):
    print(i, j)
#%%