# 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
from six.moves import range

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 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)

# 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 values 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(labels=tf_train_labels, logits=logits))
  
  # 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)

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 range(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 %d: %f‘ % (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))

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(labels=tf_train_labels, logits=logits))
  
  # 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)

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 = http://www.mamicode.com/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("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))

batch_size = 128
hidden_layer_size = 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)

    # Variables.
    
    # Hidden layer (RELU magic)
    
    weights_hidden = tf.Variable(
        tf.truncated_normal([image_size * image_size, hidden_layer_size]))
    biases_hidden = tf.Variable(tf.zeros([hidden_layer_size]))
    hidden = tf.nn.relu(tf.matmul(tf_train_dataset, weights_hidden) + biases_hidden)

    # Output layer

    weights_output = tf.Variable(
        tf.truncated_normal([hidden_layer_size, num_labels]))
    biases_output = tf.Variable(tf.zeros([num_labels]))
    
    # Training computation.
    
    logits = tf.matmul(hidden, weights_output) + biases_output
    
    loss = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))
  
    # Optimizer.

    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
  
    # Predictions for the training, validation, and test data.

    # Creation of hidden layer of RELU for the validation and testing process
    
    
    train_prediction = tf.nn.softmax(logits)
    
    hidden_validation = tf.nn.relu(tf.matmul(tf_valid_dataset, weights_hidden) + biases_hidden)
    valid_prediction = tf.nn.softmax(
    tf.matmul(hidden_validation, weights_output) + biases_output)
    
    hidden_prediction = tf.nn.relu(tf.matmul(tf_test_dataset, weights_hidden) + biases_hidden)
    test_prediction = tf.nn.softmax(tf.matmul(hidden_prediction, weights_output) + biases_output)

num_steps = 3001

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:
  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 = http://www.mamicode.com/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("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))

import math
batch_size = 128
hidden_layer_size = 1024 # Doubled because half of the results are discarded
regularization_beta = 5e-4

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.
    
    # Hidden layer (RELU magic)
    
    weights_hidden_1 = tf.Variable(
        tf.truncated_normal([image_size * image_size, hidden_layer_size], 
                           stddev=1 / math.sqrt(float(image_size * image_size))))
    biases_hidden_1 = tf.Variable(tf.zeros([hidden_layer_size]))
    hidden_1 = tf.nn.relu(tf.matmul(tf_train_dataset, weights_hidden_1) + biases_hidden_1)

    weights_hidden_2 = tf.Variable(tf.truncated_normal([hidden_layer_size, hidden_layer_size],
                                                      stddev=1 / math.sqrt(float(image_size * image_size))))
    biases_hidden_2 = tf.Variable(tf.zeros([hidden_layer_size]))
    hidden_2 = tf.nn.relu(tf.matmul(hidden_1, weights_hidden_2) + biases_hidden_2)
    
    # Output layer

    weights_output = tf.Variable(
        tf.truncated_normal([hidden_layer_size, num_labels],
                           stddev=1 / math.sqrt(float(image_size * image_size))))
    biases_output = tf.Variable(tf.zeros([num_labels]))
    
    # Training computation.
    
    logits = tf.matmul(hidden_2, weights_output) + biases_output
    
    loss = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))
  
    # L2 regularization on hidden and output weights and biases
    
    regularizers = (tf.nn.l2_loss(weights_hidden_1) + tf.nn.l2_loss(biases_hidden_1) + 
                    tf.nn.l2_loss(weights_hidden_2) + tf.nn.l2_loss(biases_hidden_2) +
                    tf.nn.l2_loss(weights_output) + tf.nn.l2_loss(biases_output))

    loss = loss + regularization_beta * regularizers
    
    # Optimizer.

    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
  
    # Predictions for the training, validation, and test data.

    # Creation of hidden layer of RELU for the validation and testing process
    
    
    train_prediction = tf.nn.softmax(logits)
    
    hidden_validation_1 = tf.nn.relu(tf.matmul(tf_valid_dataset, weights_hidden_1) + biases_hidden_1)
    hidden_validation_2 = tf.nn.relu(tf.matmul(hidden_validation_1, weights_hidden_2) + biases_hidden_2)
    valid_prediction = tf.nn.softmax(
    tf.matmul(hidden_validation_2, weights_output) + biases_output)
    
    hidden_prediction_1 = tf.nn.relu(tf.matmul(tf_test_dataset, weights_hidden_1) + biases_hidden_1)
    hidden_prediction_2 = tf.nn.relu(tf.matmul(hidden_prediction_1, weights_hidden_2) + biases_hidden_2)
    test_prediction = tf.nn.softmax(tf.matmul(hidden_prediction_2, weights_output) + biases_output)

num_steps = 3001

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:
  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 = http://www.mamicode.com/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("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))

import math
batch_size = 128
hidden_layer_size = 2048 # Doubled because half of the results are discarded
regularization_beta = 5e-4

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.
    
    # Hidden layer (RELU magic)
    
    weights_hidden_1 = tf.Variable(
        tf.truncated_normal([image_size * image_size, hidden_layer_size], 
                           stddev=1 / math.sqrt(float(image_size * image_size))))
    biases_hidden_1 = tf.Variable(tf.zeros([hidden_layer_size]))
    hidden_1 = tf.nn.relu(tf.matmul(tf_train_dataset, weights_hidden_1) + biases_hidden_1)

    weights_hidden_2 = tf.Variable(tf.truncated_normal([hidden_layer_size, hidden_layer_size],
                                                      stddev=1 / math.sqrt(float(image_size * image_size))))
    biases_hidden_2 = tf.Variable(tf.zeros([hidden_layer_size]))
    hidden_2 = tf.nn.relu(tf.matmul(tf.nn.dropout(hidden_1, 0.5), weights_hidden_2) + biases_hidden_2)
    
    # Output layer

    weights_output = tf.Variable(
        tf.truncated_normal([hidden_layer_size, num_labels],
                           stddev=1 / math.sqrt(float(image_size * image_size))))
    biases_output = tf.Variable(tf.zeros([num_labels]))
    
    # Training computation.
    
    logits = tf.matmul(tf.nn.dropout(hidden_2, 0.5), weights_output) + biases_output
    
    loss = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))
  
    # L2 regularization on hidden and output weights and biases
    
    regularizers = (tf.nn.l2_loss(weights_hidden_1) + tf.nn.l2_loss(biases_hidden_1) + 
                    tf.nn.l2_loss(weights_hidden_2) + tf.nn.l2_loss(biases_hidden_2) +
                    tf.nn.l2_loss(weights_output) + tf.nn.l2_loss(biases_output))

    loss = loss + regularization_beta * regularizers
    
    # Optimizer.

    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
  
    # Predictions for the training, validation, and test data.

    # Creation of hidden layer of RELU for the validation and testing process
    
    
    train_prediction = tf.nn.softmax(logits)
    
    hidden_validation_1 = tf.nn.relu(tf.matmul(tf_valid_dataset, weights_hidden_1) + biases_hidden_1)
    hidden_validation_2 = tf.nn.relu(tf.matmul(hidden_validation_1, weights_hidden_2) + biases_hidden_2)
    valid_prediction = tf.nn.softmax(
    tf.matmul(hidden_validation_2, weights_output) + biases_output)
    
    hidden_prediction_1 = tf.nn.relu(tf.matmul(tf_test_dataset, weights_hidden_1) + biases_hidden_1)
    hidden_prediction_2 = tf.nn.relu(tf.matmul(hidden_prediction_1, weights_hidden_2) + biases_hidden_2)
    test_prediction = tf.nn.softmax(tf.matmul(hidden_prediction_2, weights_output) + biases_output)

num_steps = 5001

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:
  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 = http://www.mamicode.com/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("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))