def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
FLAGS = settings()
np.random.seed(FLAGS.seed)
tf.set_random_seed(FLAGS.seed)
# Slim dataset contains data sources, decoder, reader and other meta-information
dataset = mnist.get_split('train', FLAGS.dataset_dir)
iterations_per_epoch = dataset.num_samples // FLAGS.batch_size # 60,000/24 = 2500
# images: Tensor (?, 28, 28, 1)
# labels: Tensor (?)
images, labels = load_batch(dataset, FLAGS.batch_size)
# Tensor(?, 10)
one_hot_labels = slim.one_hot_encoding(labels, dataset.num_classes)
# poses: Tensor(?, 10, 4, 4) activations: (?, 10)
poses, activations = m_capsules.nets.capsules_net(
images,
num_classes=10,
iterations=3,
batch_size=FLAGS.batch_size,
name='capsules_em')
global_step = tf.train.get_or_create_global_step()
loss = m_capsules.nets.spread_loss(one_hot_labels,
activations,
iterations_per_epoch,
global_step,
name='spread_loss')
tf.summary.scalar('losses/spread_loss', loss)
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_tensor = slim.learning.create_train_op(loss,
optimizer,
global_step=global_step,
clip_gradient_norm=4.0)
slim.learning.train(
train_tensor,
logdir=FLAGS.log_dir,
log_every_n_steps=10,
save_summaries_secs=60,
saver=tf.train.Saver(max_to_keep=2),
save_interval_secs=600,
)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
FLAGS = settings()
np.random.seed(FLAGS.seed)
tf.set_random_seed(FLAGS.seed)
# Slim dataset contains data sources, decoder, reader and other meta-information
dataset = mnist.get_split('train', FLAGS.dataset_dir)
iterations_per_epoch = dataset.num_samples // FLAGS.batch_size # 60,000/24 = 2500
# images: Tensor (?, 28, 28, 1)
# labels: Tensor (?)
images, labels = load_batch(
dataset,
FLAGS.batch_size)
# Tensor(?, 10)
one_hot_labels = slim.one_hot_encoding(labels, dataset.num_classes)
# poses: Tensor(?, 10, 4, 4) activations: (?, 10)
poses, activations = m_capsules.nets.capsules_net(images, num_classes=10, iterations=3, batch_size=FLAGS.batch_size, name='capsules_em')
global_step = tf.train.get_or_create_global_step()
loss = m_capsules.nets.spread_loss(
one_hot_labels, activations, iterations_per_epoch, global_step, name='spread_loss'
)
tf.summary.scalar('losses/spread_loss', loss)
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
train_tensor = slim.learning.create_train_op(
loss, optimizer, global_step=global_step, clip_gradient_norm=4.0
)
slim.learning.train(
train_tensor,
logdir=FLAGS.log_dir,
log_every_n_steps=10,
save_summaries_secs=60,
saver=tf.train.Saver(max_to_keep=2),
save_interval_secs=600,
)
warnings.filterwarnings("ignore")
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
from cap_config import settings
from cap_datasets import mnist
from sklearn.metrics import classification_report,confusion_matrix,roc_auc_score,accuracy_score
import m_capsules
import time
FLAGS = None
tf.logging.set_verbosity(tf.logging.INFO)
# Importing datasets
X_train, Y_train, valX, valY, testX, testY = mnist.class_8()
FLAGS = settings()
X = tf.placeholder(shape=[None, 28, 28, 1], dtype=tf.float32, name="X")
y = tf.placeholder(shape=[None], dtype=tf.int64, name="y")
np.random.seed(FLAGS.seed)
tf.set_random_seed(FLAGS.seed)
"""iterations_per_epoch = dataset.num_samples // FLAGS.batch_size # 60,000/24 = 2500"""
iterations_per_epoch = len(X_train) // FLAGS.batch_size
# images: Tensor (?, 28, 28, 1)
# labels: Tensor (?)
one_hot_labels = slim.one_hot_encoding(y, 2)
# poses: Tensor(?, 10, 4, 4) activations: (?, 10)
poses, activations = m_capsules.nets.capsules_net(X, num_classes=2, iterations=3, batch_size=FLAGS.batch_size, name='capsules_em')
y_proba_argmax = tf.argmax(activations, axis=1, name="y_proba")
y_pred = y_proba_argmax #tf.squeeze(y_proba_argmax, axis=[1,2], name="y_pred")
correct = tf.equal(y, y_pred, name="correct")
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name="accuracy")