def distorted_inputs(): """Construct distorted input for CIFAR training using the Reader ops. Returns: images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. labels: Labels. 1D tensor of [batch_size] size. Raises: ValueError: If no data_dir """ if not DATA_DIR: raise ValueError('Please supply a data_dir') data_dir = os.path.join(DATA_DIR, 'cifar-10-batches-bin') images, labels = cifar10_input.distorted_inputs(data_dir=data_dir, batch_size=BATCH_SIZE) return images, labels
def distorted_inputs(): """Construct distorted input for CIFAR training using the Reader ops. Returns: images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. labels: Labels. 1D tensor of [batch_size] size. Raises: ValueError: If no data_dir """ if not DATA_DIR: raise ValueError('Please supply a data_dir') data_dir = os.path.join(DATA_DIR, 'cifar-10-batches-bin') images, labels = cifar10_input.distorted_inputs( data_dir=data_dir, batch_size=BATCH_SIZE) return images, labels
max_steps = 3000 batch_size = 128 data_dir = 'cifar10_data/cifar-10-batches-bin' def variable_with_weight_loss(shape, stddev, wl): var = tf.Variable(tf.truncated_normal(shape, stddev=stddev)) if wl is not None: weight_loss = tf.multiply(tf.nn.l2_loss(var), wl, name='weight_loss') tf.add_to_collection('losses', weight_loss) return var # cifar10.maybe_download_and_extract() images_train, labels_train = cifar10_input.distorted_inputs( data_dir=data_dir, batch_size=batch_size) images_test, labels_test = cifar10_input.inputs( eval_data=True, data_dir=data_dir, batch_size=batch_size) image_holder = tf.placeholder(tf.float32, [batch_size, 24, 24, 3]) label_holder = tf.placeholder(tf.int32, [batch_size]) weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64], stddev=5e-2, wl=0.0) kernel1 = tf.nn.conv2d(image_holder, weight1, [1, 1, 1, 1], padding='SAME') bias1 = tf.Variable(tf.constant(0.0, shape=[64])) conv1 = tf.nn.relu(tf.nn.bias_add(kernel1, bias1)) pool1 = tf.nn.max_pool( conv1, ksize=[ 1, 3, 3, 1], strides=[ 1, 2, 2, 1], padding='SAME') norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)