full_connect.py

from __future__ import print_function

import numpy as np
import tensorflow as tf

from not_mnist.img_pickle import load_pickle


def reformat(dataset, labels, image_size, num_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


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


def tf_logist():
    # 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 example: that's our loss.
        logits = tf.matmul(tf_train_dataset, weights) + biases
        loss = tf.reduce_mean(
            tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=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)

    num_steps = 801

    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))


def tf_sgd():
    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=logits, labels=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)

    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}
            _, 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))


def tf_sgd_relu_nn():
    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)

        hidden_node_count = 1024
        # Variables.
        weights1 = tf.Variable(
            tf.truncated_normal([image_size * image_size, hidden_node_count]))
        biases1 = tf.Variable(tf.zeros([hidden_node_count]))

        weights2 = tf.Variable(
            tf.truncated_normal([hidden_node_count, num_labels]))
        biases2 = tf.Variable(tf.zeros([num_labels]))

        # Training computation.
        ys = tf.matmul(tf_train_dataset, weights1) + biases1
        hidden = tf.nn.relu(ys)
        logits = tf.matmul(hidden, weights2) + biases2
        loss = tf.reduce_mean(
            tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=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.nn.relu(tf.matmul(tf_valid_dataset, weights1) + biases1), weights2) + biases2)
        test_prediction = tf.nn.softmax(
            tf.matmul(tf.nn.relu(tf.matmul(tf_test_dataset, weights1) + biases1), weights2) + biases2)

    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}
            _, 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))


def load_reformat_not_mnist(image_size, num_labels):
    pickle_file = '../not_mnist/notMNIST_clean.pickle'
    save = load_pickle(pickle_file)
    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)
    train_dataset, train_labels = reformat(train_dataset, train_labels, image_size, num_labels)
    valid_dataset, valid_labels = reformat(valid_dataset, valid_labels, image_size, num_labels)
    test_dataset, test_labels = reformat(test_dataset, test_labels, image_size, num_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)
    return train_dataset, train_labels, valid_dataset, valid_labels, test_dataset, test_labels

if __name__ == '__main__':
    # First reload the data we generated in 1_notmnist.ipynb.
    image_size = 28
    num_labels = 10
    train_dataset, train_labels, valid_dataset, valid_labels, test_dataset, test_labels = \
        load_reformat_not_mnist(image_size, num_labels)

    # tf_logist()
    # tf_sgd()
    tf_sgd_relu_nn()