def get_data_and_labels():
test_dataset, train_dataset, validation_dataset = load_test_train_validation_ds(
reshape=(-1, image_size, image_size, num_channels))
train_labels = convert_from_one_dim_labels(train_dataset.label, NUM_LABELS)
train_labels = train_labels.astype(np.float32)
train_data_resized = resize_all_img_in_dataset(
train_dataset.data.astype(np.float32), (32, 32))
validation_labels = convert_from_one_dim_labels(
validation_dataset.label, NUM_LABELS).astype(np.float32)
validation_data_resized = resize_all_img_in_dataset(
validation_dataset.data.astype(np.float32), (32, 32))
test_labels = convert_from_one_dim_labels(test_dataset.label, NUM_LABELS)
test_data_resized = resize_all_img_in_dataset(test_dataset.data, (32, 32))
return {
'test_labels': test_labels,
'train_labels': train_labels,
'validation_labels': validation_labels,
'validation_data_resized': validation_data_resized,
'test_data_resized': test_data_resized,
'train_data_resized': train_data_resized
}
def one_layer_relu(learning_rate, num_steps, train_subset):
# Load dataset:
test_dataset, train_dataset, validation_dataset = load_test_train_validation_ds()
# Variables
tf_train_data, tf_train_labels = construct_tf_placeholder(train_subset)
train_labels = convert_from_one_dim_labels(train_dataset.label, NUM_LABELS)
# Constants validation set
tf_valid_data, tf_valid_labels = construct_tf_constant(validation_dataset.data,
validation_dataset.label)
valid_labels = convert_from_one_dim_labels(validation_dataset.label, NUM_LABELS)
# Constant test set
tf_test_data, tf_test_label = construct_tf_constant(test_dataset.data, test_dataset.label)
test_labels = convert_from_one_dim_labels(test_dataset.label, NUM_LABELS)
# Construct architecture and get predictions:
logits = inference(tf_train_data)
# Get loss:
loss = loss_func(logits, tf_train_labels)
# loss += 0.01*regularizers
training_op = training(loss, learning_rate)
eval_op = evaluate(logits, tf_train_labels)
# Predictions:
# test_prediction = inference(tf_test_data)
summary_op = tf.merge_all_summaries()
saver = tf.train.Saver()
test_dict = {tf_train_data: test_dataset.data, tf_train_labels: test_labels}
with tf.Session() as session:
summary_writer = tf.train.SummaryWriter('logs/', graph_def=session.graph_def)
tf.global_variables_initializer().run()
logging.info('Initialization')
for step in range(num_steps):
random_indx = np.random.randint(1, train_dataset.data.shape[0], size=train_subset)
batch_data = train_dataset.data[random_indx]
batch_labels = train_labels[random_indx]
feed_dict = {tf_train_data: batch_data, tf_train_labels: batch_labels}
_, loss_value = session.run([training_op, loss], feed_dict=feed_dict)
if step % 500 == 0:
val_feed_dict = {
tf_train_data: validation_dataset.data,
tf_train_labels: valid_labels
}
simple_accuracy = session.run(eval_op, feed_dict=val_feed_dict)
logging.info('Loss at step %d: %f, accuracy: %f' % (step, loss_value, simple_accuracy))
summary_str = session.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
saver.save(session, 'logs/model-checkpoint', global_step=step)
simple_accuracy = session.run(eval_op, feed_dict=test_dict)
logging.info('Test accuracy: %.1f%%' % float(100*simple_accuracy))
def evaluate(label, y):
res = tf.equal(tf.argmax(label, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(res, tf.float32))
tf.summary.scalar('validation_error', (1.0 - accuracy))
return accuracy
def chunks(data, label, idx, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(data), n):
shuffled_index = idx[i:i + n]
yield data[shuffled_index], label[shuffled_index]
if __name__ == '__main__':
test_dataset, train_dataset, validation_dataset = load_test_train_validation_ds(
)
train_labels = convert_from_one_dim_labels(train_dataset.label, NUM_LABELS)
train_labels = train_labels.astype(np.float32)
train_data = train_dataset.data.astype(np.float32)
validation_labels = convert_from_one_dim_labels(validation_dataset.label,
NUM_LABELS)
validation_labels = validation_labels.astype(np.float32)
validation_data = validation_dataset.data.astype(np.float32)
test_labels = convert_from_one_dim_labels(test_dataset.label, NUM_LABELS)
sess = tf.Session()
x = tf.placeholder(shape=[None, 784], dtype='float32')
def accuracy(predictions, labels):
return 100.0 * np.sum(
np.argmax(predictions, 1) == np.argmax(labels, 1) /
predictions.shape[0])
def chunks(data, label, idx, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(data), n):
shuffled_index = idx[i:i + n]
yield data[shuffled_index], label[shuffled_index]
if __name__ == '__main__':
test_dataset, train_dataset, validation_dataset = load_test_train_validation_ds(
reshape=(-1, image_size, image_size, num_channels))
train_labels = convert_from_one_dim_labels(train_dataset.label, NUM_LABELS)
train_labels = train_labels.astype(np.float32)
train_data = train_dataset.data.astype(np.float32)
validation_labels = convert_from_one_dim_labels(validation_dataset.label,
NUM_LABELS)
validation_labels = validation_labels.astype(np.float32)
validation_data = validation_dataset.data.astype(np.float32)
test_labels = convert_from_one_dim_labels(test_dataset.label, NUM_LABELS)
sess = tf.Session()
tf_train_data = tf.placeholder(shape=(batch_size, image_size, image_size,