2_fullyconnected.py

# coding: utf-8

# Deep Learning
# =============
# 
# Assignment 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 assignment 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.
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

# First reload the data we generated in `1_notmnist.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 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(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.initialize_all_variables().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))

# 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 `session.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.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}
        _, 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))

# ---
# Problem
# -------
#
# Turn the logistic regression example with SGD into a 1-hidden layer neural network
# with rectified linear units [nn.relu()](https://www.tensorflow.org/versions/r0.7/api_docs/python/nn.html#relu)
# and 1024 hidden nodes. This model should improve your validation / test accuracy.
#
# ---

batch_size = 128
hidden_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.
    W1 = tf.Variable(tf.truncated_normal([image_size * image_size, hidden_size]))
    b1 = tf.Variable(tf.zeros([hidden_size]))

    W2 = tf.Variable(tf.truncated_normal([hidden_size, num_labels]))
    b2 = tf.Variable(tf.zeros([num_labels]))

    # Training computation.
    y1 = tf.nn.relu(tf.matmul(tf_train_dataset, W1) + b1)
    logits = tf.matmul(y1, W2) + b2

    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)

    y1_valid = tf.nn.relu(tf.matmul(tf_valid_dataset, W1) + b1)
    valid_logits = tf.matmul(y1_valid, W2) + b2
    valid_prediction = tf.nn.softmax(valid_logits)

    y1_test = tf.nn.relu(tf.matmul(tf_test_dataset, W1) + b1)
    test_logits = tf.matmul(y1_test, W2) + b2
    test_prediction = tf.nn.softmax(test_logits)

# Let's run it:
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}
        _, 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))