def __init__(self,
sess,
width=256,
height=256,
channels=3,
action_dim=9,
learning_rate=0.0001,
model_name=None,
graph=None,
export=False):
self.sess = sess
self.width = width
self.height = height
self.channels = channels
self.action_dim = action_dim
self.learning_rate = learning_rate
self.rgb, self.conf, self.vel, = self.create_network('visuonet')
if not export:
self.velocities = tf.placeholder(tf.float32, [None, 9])
self.loss = tf.reduce_mean(
tf.square(self.velocities - self.vel)
) #+ 0.001 * tf.reduce_mean(tf.square(self.gripper - self.vel[:,6]))
self.optimizer = tf.train.AdamOptimizer(
self.learning_rate).minimize(self.loss)
self.model = model(sess, MODELS_DIR, model_name=model_name)
self.summary = self.model.summary(graph=graph,
**dict(loss=self.loss))
self.eval_indice = len(
get_folders(pathname(RES_PATH, **dict(flag=1))))
def __init__(self,
sess,
width=256,
height=256,
channels=3,
learning_rate=0.0001,
z_shape=8,
j_shape=9,
model_name=None,
graph=None,
coeff=0.002):
self.sess = sess
self.width = width
self.height = height
self.channels = channels
self.learning_rate = learning_rate
self.z_shape = z_shape
self.j_shape = j_shape
self.img, self.p_t, self.p_t_1 = self.gen_network('generator')
# self.p_t = tf.placeholder(tf.float32, shape = (None, self.j_shape))
# self.std = tf.exp(self.logstds)
# self.var = self.std ** 2
# self.loss = 0.5 * tf.reduce_sum(tf.square((self.p_t - self.action_mean) / self.var)) \
# + 0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(self.p_t)[0]) \
# + tf.reduce_sum(self.logstds)
self.std = tf.exp(self.logstds)
# self.var = tf.square(self.std)
self.p_t_1_l = tf.placeholder(tf.float32, (None, 9))
self.mse_loss = tf.reduce_mean(tf.square(self.p_t_1 - self.p_t_1_l))
lh_loss = 0.5 * tf.reduce_sum(tf.square((self.p_t_1_l - self.p_t_1) / self.std), axis=-1) \
+ 0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(self.p_t_1)[-1]) + tf.reduce_sum(self.logstds, axis=-1)
self.lh_loss = tf.reduce_mean(lh_loss)
self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(
self.lh_loss)
self.model = model(sess, MODELS_DIR, model_name=model_name)
self.summary = self.model.summary(graph=graph,
**dict(mse_loss=self.mse_loss,
lh_loss=self.lh_loss))
self.eval_indice = len(get_folders(pathname(RES_PATH, **dict(flag=1))))
train_data = utils.HeadData(config.train_id_docs, np.arange(len(config.train_id_docs)))
test_data = utils.HeadData(config.test_id_docs, np.arange(len(config.test_id_docs)))
tf.reset_default_graph()
tf.set_random_seed(1)
utils.printParameters(config)
# ---- Training ----
config1 = tf.ConfigProto()
config1.gpu_options.per_process_gpu_memory_fraction = 0.85
with tf.Session(config=config1) as sess:
# saver = tf.train.import_meta_graph('model.ckpt.meta')
# saver.restore(sess, 'model.ckpt')
embedding_matrix = tf.get_variable('embedding_matrix', shape=config.wordvectors.shape, dtype=tf.float32,
trainable=False).assign(config.wordvectors)
emb_mtx = sess.run(embedding_matrix)
model = tf_utils.model(config, emb_mtx, sess)
obj, m_op, predicted_op_ner, actual_op_ner, predicted_op_rel, actual_op_rel, score_op_rel = model.run()
train_step = model.get_train_op(obj)
operations = tf_utils.operations(train_step, obj, m_op, predicted_op_ner, actual_op_ner, predicted_op_rel,
actual_op_rel, score_op_rel)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
best_score = 0
nepoch_no_imprv = 0 # for early stopping
for iter in range(config.nepochs + 1):
model.train(train_data, operations, iter)
save_path = saver.save(sess, "model.ckpt")
print("Model saved in path: %s" % save_path)
from cnn_utils import load_dataset, preprocess_data from tf_utils import model num_classes = 6 # Loading the data (signs) X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset() # Preprocess data X_train, X_test, Y_train, Y_test = preprocess_data(X_train_orig, X_test_orig, Y_train_orig, Y_test_orig, num_classes) # Training the parameters _, _, parameters = model(X_train, Y_train, X_test, Y_test)
import data_service, plot_service, tf_utils
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = data_service.load_dataset(
)
plot_service.plot_sample(X_train_orig, Y_train_orig)
X_train, Y_train, X_test, Y_test = data_service.preprocess_data(
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig)
parameters = tf_utils.model(X_train, Y_train, X_test, Y_test)