multi_conv.py

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

# TODO: Test out different hyperparameters

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  # Erases pickle file's contents from local memory

image_size = 28
num_labels = 10
num_channels = 1

def reformat(dataset, labels):
	dataset = dataset.reshape(
		(-1, image_size, image_size, num_channels)).astype(np.float32)
	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.mean(np.argmax(predictions, 1) == np.argmax(labels, 1))

batch_size = 16
patch_size = 5
depth = 16
num_hidden = 64
learning_rate = 0.05

graph = tf.Graph()

def conv_layer(input, size_in, size_out, name="conv"):
	with tf.name_scope(name):
		w = tf.Variable(tf.truncated_normal([5, 5, size_in, size_out], stddev=0.1), name="W")
		b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
		conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="SAME")
		act = tf.nn.relu(conv + b)
		tf.summary.histogram("weights", w)
		tf.summary.histogram("biases", b)
		tf.summary.histogram("activations", act)
		return {"logits": act, "weights": w, "biases": b}

with graph.as_default():

	# Input data.
	tf_train_dataset = tf.placeholder(
		tf.float32, shape=(batch_size, image_size, image_size, num_channels))
	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.
	layer1_weights = tf.Variable(tf.truncated_normal(
		[patch_size, patch_size, num_channels, depth], stddev=0.1))
	layer1_biases = tf.Variable(tf.zeros([depth]))
	layer2_weights = tf.Variable(tf.truncated_normal(
		[patch_size, patch_size, depth, depth], stddev=0.1))
	layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))
	layer3_weights = tf.Variable(tf.truncated_normal(
		[image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1))
	layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))
	layer4_weights = tf.Variable(tf.truncated_normal(
		[num_hidden, num_labels], stddev=0.1))
	layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))

	# Model.
	def model(data):
		conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME')
		hidden = tf.nn.relu(conv + layer1_biases)
		conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME')
		hidden = tf.nn.relu(conv + layer2_biases)
		shape = hidden.get_shape().as_list()
		reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])
		hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)
		return tf.matmul(hidden, layer4_weights) + layer4_biases

	# Training computation.
	logits = model(tf_train_dataset)
	loss = tf.reduce_mean(
		tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))

	# Optimizer.
	optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

	# Predictions for the training, validation, and test data.
	train_prediction = tf.nn.softmax(logits)
	valid_prediction = tf.nn.softmax(model(tf_valid_dataset))
	test_prediction = tf.nn.softmax(model(tf_test_dataset))

num_steps = 5001

with tf.Session(graph=graph) as session:
	tf.global_variables_initializer().run()
	print('Initialized')
	for step in range(num_steps):
		offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
		batch_data = train_dataset[offset:(offset + batch_size), :, :, :]
		batch_labels = train_labels[offset:(offset + batch_size), :]
		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))