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cifar10_conv.py
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import argparse
import os
import sys
##############################################
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--eps', type=float, default=1e-5)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--act', type=str, default='relu')
parser.add_argument('--bias', type=float, default=0.)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--dfa', type=int, default=0)
parser.add_argument('--sparse', type=int, default=0)
parser.add_argument('--rank', type=int, default=0)
parser.add_argument('--init', type=str, default="glorot_uniform")
parser.add_argument('--save', type=int, default=0)
parser.add_argument('--name', type=str, default="cifar10_conv")
parser.add_argument('--load', type=str, default=None)
args = parser.parse_args()
if args.gpu >= 0:
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]=str(args.gpu)
##############################################
import tensorflow as tf
import keras
import numpy as np
from lib.Model import Model
from lib.Layer import Layer
from lib.ConvToFullyConnected import ConvToFullyConnected
from lib.FullyConnected import FullyConnected
from lib.Convolution import Convolution
from lib.MaxPool import MaxPool
from lib.Dropout import Dropout
from lib.FeedbackFC import FeedbackFC
from lib.FeedbackConv import FeedbackConv
from lib.Activation import Relu
from lib.Activation import Tanh
##############################################
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
train_examples = 50000
test_examples = 10000
assert(np.shape(x_train) == (train_examples, 32, 32, 3))
x_train = x_train - np.mean(x_train, axis=0, keepdims=True)
x_train = x_train / np.std(x_train, axis=0, keepdims=True)
y_train = keras.utils.to_categorical(y_train, 10)
assert(np.shape(x_test) == (test_examples, 32, 32, 3))
x_test = x_test - np.mean(x_test, axis=0, keepdims=True)
x_test = x_test / np.std(x_test, axis=0, keepdims=True)
y_test = keras.utils.to_categorical(y_test, 10)
##############################################
if args.act == 'tanh':
act = Tanh()
elif args.act == 'relu':
act = Relu()
else:
assert(False)
##############################################
tf.set_random_seed(0)
tf.reset_default_graph()
batch_size = tf.placeholder(tf.int32, shape=())
dropout_rate = tf.placeholder(tf.float32, shape=())
lr = tf.placeholder(tf.float32, shape=())
X = tf.placeholder(tf.float32, [None, 32, 32, 3])
Y = tf.placeholder(tf.float32, [None, 10])
l0 = Convolution(input_shape=[batch_size, 32, 32, 3], filter_sizes=[5, 5, 3, 96], init=args.init, activation=act, bias=args.bias, name='conv1')
l1 = MaxPool(size=[batch_size, 32, 32, 96], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="SAME")
l2 = FeedbackConv(size=[batch_size, 16, 16, 96], num_classes=10, sparse=args.sparse, rank=args.rank, name='conv1_fb')
l3 = Convolution(input_shape=[batch_size, 16, 16, 96], filter_sizes=[5, 5, 96, 128], init=args.init, activation=act, bias=args.bias, name='conv2')
l4 = MaxPool(size=[batch_size, 16, 16, 128], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="SAME")
l5 = FeedbackConv(size=[batch_size, 8, 8, 128], num_classes=10, sparse=args.sparse, rank=args.rank, name='conv2_fb')
l6 = Convolution(input_shape=[batch_size, 8, 8, 128], filter_sizes=[5, 5, 128, 256], init=args.init, activation=act, bias=args.bias, name='conv3')
l7 = MaxPool(size=[batch_size, 8, 8, 256], ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="SAME")
l8 = FeedbackConv(size=[batch_size, 4, 4, 256], num_classes=10, sparse=args.sparse, rank=args.rank, name='conv3_fb')
l9 = ConvToFullyConnected(input_shape=[4, 4, 256])
l10 = FullyConnected(input_shape=4*4*256, size=2048, init=args.init, activation=act, bias=args.bias, name='fc1')
l11 = Dropout(rate=dropout_rate)
l12 = FeedbackFC(size=[4*4*256, 2048], num_classes=10, sparse=args.sparse, rank=args.rank, name='fc1_fb')
l13 = FullyConnected(input_shape=2048, size=2048, init=args.init, activation=act, bias=args.bias, name='fc2')
l14 = Dropout(rate=dropout_rate)
l15 = FeedbackFC(size=[2048, 2048], num_classes=10, sparse=args.sparse, rank=args.rank, name='fc2_fb')
l16 = FullyConnected(input_shape=2048, size=10, init=args.init, bias=args.bias, name='fc3')
##############################################
model = Model(layers=[l0, l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11, l12, l13, l14, l15, l16])
predict = model.predict(X=X)
weights = model.get_weights()
if args.dfa:
grads_and_vars = model.dfa_gvs(X=X, Y=Y)
else:
grads_and_vars = model.gvs(X=X, Y=Y)
train = tf.train.AdamOptimizer(learning_rate=lr, epsilon=args.eps).apply_gradients(grads_and_vars=grads_and_vars)
correct = tf.equal(tf.argmax(predict,1), tf.argmax(Y,1))
total_correct = tf.reduce_sum(tf.cast(correct, tf.float32))
##############################################
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
##############################################
filename = args.name + '.results'
f = open(filename, "w")
f.write(filename + "\n")
f.write("total params: " + str(model.num_params()) + "\n")
f.close()
##############################################
train_accs = []
test_accs = []
for ii in range(args.epochs):
#############################
_total_correct = 0
for jj in range(0, train_examples, args.batch_size):
s = jj
e = min(jj + args.batch_size, train_examples)
b = e - s
xs = x_train[s:e]
ys = y_train[s:e]
_correct, _ = sess.run([total_correct, train], feed_dict={batch_size: b, dropout_rate: args.dropout, lr: args.lr, X: xs, Y: ys})
_total_correct += _correct
train_acc = 1.0 * _total_correct / (train_examples - (train_examples % args.batch_size))
train_accs.append(train_acc)
#############################
_total_correct = 0
for jj in range(0, test_examples, args.batch_size):
s = jj
e = min(jj + args.batch_size, test_examples)
b = e - s
xs = x_test[s:e]
ys = y_test[s:e]
_correct = sess.run(total_correct, feed_dict={batch_size: b, dropout_rate: 0.0, lr: 0.0, X: xs, Y: ys})
_total_correct += _correct
test_acc = 1.0 * _total_correct / (test_examples - (test_examples % args.batch_size))
test_accs.append(test_acc)
#############################
p = "%d | train acc: %f | test acc: %f" % (ii, train_acc, test_acc)
print (p)
f = open(filename, "a")
f.write(p + "\n")
f.close()
##############################################
if args.save:
[w] = sess.run([weights], feed_dict={})
w['train_acc'] = train_accs
w['test_acc'] = test_accs
np.save(args.name, w)
##############################################