In [1]:
import os
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
print ("Packages loaded")
In [2]:
# Load them!
cwd = os.getcwd()
loadpath = cwd + "/data/custom_data.npz"
l = np.load(loadpath)
# See what's in here
print (l.files)
# Parse data
trainimg = l['trainimg']
trainlabel = l['trainlabel']
testimg = l['testimg']
testlabel = l['testlabel']
use_gray = l['use_gray']
ntrain = trainimg.shape[0]
nclass = trainlabel.shape[1]
dim = trainimg.shape[1]
ntest = testimg.shape[0]
print ("%d train images loaded" % (ntrain))
print ("%d test images loaded" % (ntest))
print ("%d dimensional input" % (dim))
print ("%d classes" % (nclass))
In [3]:
tf.set_random_seed(0)
# Parameters of Logistic Regression
learning_rate = 0.001
training_epochs = 1000
batch_size = 10
display_step = 100
# Create Graph for Logistic Regression
x = tf.placeholder("float", [None, dim])
y = tf.placeholder("float", [None, nclass])
W = tf.Variable(tf.zeros([dim, nclass]), name = 'weights')
b = tf.Variable(tf.zeros([nclass]))
In [4]:
WEIGHT_DECAY_FACTOR = 1 # 0.000001
l2_loss = tf.add_n([tf.nn.l2_loss(v)
for v in tf.trainable_variables()])
_pred = tf.nn.softmax(tf.matmul(x, W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(_pred)
, reduction_indices=1))
cost = cost + WEIGHT_DECAY_FACTOR*l2_loss
optm = tf.train.GradientDescentOptimizer(
learning_rate).minimize(cost)
_corr = tf.equal(tf.argmax(_pred, 1), tf.argmax(y, 1))
accr = tf.reduce_mean(tf.cast(_corr, tf.float32))
init = tf.initialize_all_variables()
print ("Functions ready")
In [5]:
# Launch the graph
sess = tf.Session()
sess.run(init)
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
num_batch = int(ntrain/batch_size)
# Loop over all batches
for i in range(num_batch):
randidx = np.random.randint(ntrain, size=batch_size)
batch_xs = trainimg[randidx, :]
batch_ys = trainlabel[randidx, :]
# Fit training using batch data
sess.run(optm, feed_dict={x: batch_xs, y: batch_ys})
# Compute average loss
avg_cost += sess.run(cost
, feed_dict={x: batch_xs, y: batch_ys})/num_batch
# Display logs per epoch step
if epoch % display_step == 0:
print ("Epoch: %03d/%03d cost: %.9f" %
(epoch, training_epochs, avg_cost))
train_acc = sess.run(accr, feed_dict={x: batch_xs, y: batch_ys})
print (" Training accuracy: %.3f" % (train_acc))
test_acc = sess.run(accr, feed_dict={x: testimg, y: testlabel})
print (" Test accuracy: %.3f" % (test_acc))
print ("Optimization Finished!")
In [6]:
sess.close()
print ("Session closed.")