mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 20:20:46 +02:00
92 lines
2.9 KiB
Python
92 lines
2.9 KiB
Python
import os
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
import tensorflow.contrib.layers as layers
|
|
import far_ho as far
|
|
import far_ho.examples as far_ex
|
|
import matplotlib.pyplot as plt
|
|
|
|
sess = tf.InteractiveSession()
|
|
|
|
|
|
def get_data():
|
|
# load a small portion of mnist data
|
|
datasets = far_ex.mnist(data_root_folder=os.path.join(os.getcwd(), 'MNIST_DATA'), partitions=(.1, .1,))
|
|
return datasets.train, datasets.validation
|
|
|
|
|
|
def g_logits(x,y):
|
|
with tf.variable_scope('model'):
|
|
h1 = layers.fully_connected(x, 300)
|
|
logits = layers.fully_connected(h1, int(y.shape[1]))
|
|
return logits
|
|
|
|
|
|
x = tf.placeholder(tf.float32, shape=(None, 28**2), name='x')
|
|
y = tf.placeholder(tf.float32, shape=(None, 10), name='y')
|
|
logits = g_logits(x,y)
|
|
train_set, validation_set = get_data()
|
|
|
|
lambdas = far.get_hyperparameter('lambdas', tf.zeros(train_set.num_examples))
|
|
lr = far.get_hyperparameter('lr', initializer=0.01)
|
|
|
|
ce = tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=logits)
|
|
L = tf.reduce_mean(tf.sigmoid(lambdas)*ce)
|
|
E = tf.reduce_mean(ce)
|
|
|
|
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y, 1), tf.argmax(logits, 1)), tf.float32))
|
|
|
|
inner_optimizer = far.GradientDescentOptimizer(lr)
|
|
outer_optimizer = tf.train.AdamOptimizer()
|
|
rev_it =10
|
|
hyper_method = far.ReverseHG().truncated(reverse_iterations=rev_it)
|
|
hyper_step = far.HyperOptimizer(hyper_method).minimize(E, outer_optimizer, L, inner_optimizer)
|
|
|
|
T = 20 # Number of inner iterations
|
|
train_set_supplier = train_set.create_supplier(x, y)
|
|
validation_set_supplier = validation_set.create_supplier(x, y)
|
|
tf.global_variables_initializer().run()
|
|
|
|
print('inner:', L.eval(train_set_supplier()))
|
|
print('outer:', E.eval(validation_set_supplier()))
|
|
# print('-'*50)
|
|
n_hyper_iterations = 200
|
|
inner_losses = []
|
|
outer_losses = []
|
|
train_accs = []
|
|
val_accs = []
|
|
|
|
for _ in range(n_hyper_iterations):
|
|
hyper_step(T,
|
|
inner_objective_feed_dicts=train_set_supplier,
|
|
outer_objective_feed_dicts=validation_set_supplier)
|
|
|
|
inner_obj = L.eval(train_set_supplier())
|
|
outer_obj = E.eval(validation_set_supplier())
|
|
inner_losses.append(inner_obj)
|
|
outer_losses.append(outer_obj)
|
|
print('inner:', inner_obj)
|
|
print('outer:', outer_obj)
|
|
|
|
train_acc = accuracy.eval(train_set_supplier())
|
|
val_acc = accuracy.eval(validation_set_supplier())
|
|
train_accs.append(train_acc)
|
|
val_accs.append(val_acc)
|
|
print('training accuracy', train_acc)
|
|
print('validation accuracy', val_acc)
|
|
|
|
print('learning rate', lr.eval())
|
|
print('norm of examples weight', tf.norm(lambdas).eval())
|
|
print('-'*50)
|
|
|
|
plt.subplot(211)
|
|
plt.plot(inner_losses, label='training loss')
|
|
plt.plot(outer_losses, label='validation loss')
|
|
plt.legend(loc=0, frameon=True)
|
|
#plt.xlim(0, 19)
|
|
plt.subplot(212)
|
|
plt.plot(train_accs, label='training accuracy')
|
|
plt.plot(val_accs, label='validation accuracy')
|
|
plt.legend(loc=0, frameon=True)
|
|
|
|
plt.savefig('H%d - I%d - R%d'%(n_hyper_iterations,T,rev_it))
|