def define_training(model, args):
# define optimizer
input_lr = tf.placeholder(
tf.float32, shape=[]) # placeholder for dynamic learning rate
model.a('input_lr', input_lr)
if args.opt == 'sgd':
optimizer = tf.train.MomentumOptimizer(input_lr, args.mom)
elif args.opt == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(input_lr, momentum=args.mom)
elif args.opt == 'adam':
optimizer = tf.train.AdamOptimizer(input_lr)
model.a('optimizer', optimizer)
# This adds prob, cross_ent, loss_cross_ent, class_prediction,
# prediction_correct, accuracy, loss, (loss_reg) in tf_nets/losses.py
add_classification_losses(model, model.input_labels)
grads_and_vars = optimizer.compute_gradients(
model.loss,
model.trainable_weights,
gate_gradients=tf.train.Optimizer.GATE_GRAPH)
model.a('grads_to_compute', [grad for grad, _ in grads_and_vars])
model.a('train_step', optimizer.apply_gradients(grads_and_vars))
print('All model weights:')
summarize_weights(
model.trainable_weights) #print summaries for weights (from tfutil)
def define_training(model, args):
# define optimizer
input_lr = tf.placeholder(tf.float32, shape=[]) # placeholder for dynamic learning rate
model.a('input_lr', input_lr)
if args.opt == 'sgd':
optimizer = tf.train.MomentumOptimizer(input_lr, args.mom)
elif args.opt == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(input_lr, momentum=args.mom)
elif args.opt == 'adam':
optimizer = tf.train.AdamOptimizer(input_lr)
model.a('optimizer', optimizer)
# This adds prob, cross_ent, loss_cross_ent, class_prediction,
# prediction_correct, accuracy, loss, (loss_reg) in tf_nets/losses.py
add_classification_losses(model, model.input_labels)
model.a('train_step', optimizer.minimize(model.loss, var_list=model.trainable_weights))
print('All model weights:')
summarize_weights(model.trainable_weights)