def get_optimizer(learning_rate, hparams):
"""Get the tf.train.Optimizer for this optimizer string.
Args:
learning_rate: The learning_rate tensor.
hparams: tf.contrib.training.HParams object with the optimizer and
momentum values.
Returns:
optimizer: The tf.train.Optimizer based on the optimizer string.
"""
return {
"rmsprop":
tf.RMSPropOptimizer(learning_rate,
decay=0.95,
momentum=hparams.momentum,
epsilon=1e-4),
"adam":
tf.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-8),
"adagrad":
tf.AdagradOptimizer(learning_rate, initial_accumulator_value=1.0),
"mom":
tf.MomentumOptimizer(learning_rate, momentum=hparams.momentum),
"sgd":
tf.GradientDescentOptimizer(learning_rate)
}.get(hparams.optimizer)
def createGraph(a,b,lr,optimiser ):
tf.reset_default_graph()
w1 = tf.get_variable(dtype=tf.float32, shape=(), name="w1", initializer=tf.random_normal_initializer(0.0,1.0))
w2 = tf.get_variable(dtype=tf.float32, shape=(), name="w2", initializer=tf.random_normal_initializer(0.0,1.0))
a = tf.constant(a , name="a")
b = tf.constant(b, name="b")
costFunc = tf.add( tf.square( tf.subtract( a, w1, name='term1'),
name='term1_sq'),\
tf.multiply( b,\
tf.square( tf.subtract( \
w2,\
tf.square( w1,\
name='weight1_sq'),\
name='term2'),\
name='term2_sq'),\
name='term2_Sq_Mul_b'),\
name='costFunc')
print(a,b)
print(lr)
print(optimiser)
if 'gd'==optimiser:
train_step =tf.train.GradientDescentOptimizer(learning_rate=lr, name='GradientDescent').minimize(costFunc, name= 'train_step')
if 'gdm'==optimiser:
train_step =tf.MomentumOptimizer(learning_rate=lr,momentum=0.9, name='Momentum').minimize(costFunc, name= 'train_step')
if 'adam'==optimiser:
train_step =tf.AdamOptimizer(learning_rate=lr,name='Adam').minimize(costFunc, name= 'train_step')
init = tf.global_variables_initializer()
#once you build the graph, write to file
file_writer= tf.summary.FileWriter("./datasets/myTensorboardLogs/HW1_Oct11/", tf.get_default_graph())
sess = tf.Session()
sess.run(fetches=[init])
for x in range(10):
acc= sess.run(fetches=[train_step,w1,w2])
print('Accuracy at step %s: %s' % (x, acc))
import model
import numpy as np
import tensorflow as tf
IMAGE_SIZE = 32
IMAGE_CHANNELS = 3
image = tf.placeholder(tf.float32,
[None, IMAGE_SIZE, IMAGE_SIZE, IMAGE_CHANNELS])
label = tf.placeholder(tf.float32,
[None, IMAGE_SIZE, IMAGE_SIZE, IMAGE_CHNNELS])
learning_rate = tf.placeholder(tf.float32, [])
net = model.resnet(image, 1)
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=label, logits=net))
opt = tf.MomentumOptimizer(learning_rate, 0.9)
train_op = opt.minimize(cross_entropy)
correct_pre = tf.equal(tf.argmax(net, 1), tf.argmax(label, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pre, dtype=tf.float32))
saver = tf.train.saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
checkpoint = tf.train.latest_checkpoint('.')
if checkpoint is not None:
print('Restoring from checkpoint ' + checkpoint)
saver.restore(sess, ckeckpoint)
else:
print('Could not find the checkpoint to restore')
sess.close()