loss = ladder.cost_all_layers_train(targets)
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
pred_cost = -tf.reduce_mean(
tf.reduce_sum(targets * tf.log(ladder.activation),
1)) # cost used for prediction
correct_prediction = tf.equal(tf.argmax(ladder.activation, 1),
tf.argmax(targets,
1)) # no of correct predictions
accuracy = tf.reduce_mean(tf.cast(correct_prediction,
"float")) * tf.constant(100.0)
s.run(tf.initialize_all_variables())
ladder.set_all_deterministic(True)
print "acc", s.run([accuracy],
feed_dict={
inputs: data.test.features,
targets: data.test.labels
})
ladder.set_all_deterministic(False)
for i in range(num_iter):
images, labels = data.train.next_batch(batch_size)
_, loss_val = s.run([train_step, loss],
feed_dict={
inputs: images,
targets: labels
assert int(l2.z_est.get_shape()[-1]) == 500
assert int(l1.z_est.get_shape()[-1]) == 784
assert int(l1.mean_corrupted_unlabeled.get_shape()[0]) == 500
assert int(l2.mean_corrupted_unlabeled.get_shape()[0]) == 10
loss = ladder.cost_all_layers_train(targets)
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
pred_cost = -tf.reduce_mean(tf.reduce_sum(targets * tf.log(ladder.activation), 1)) # cost used for prediction
correct_prediction = tf.equal(tf.argmax(ladder.activation, 1), tf.argmax(targets, 1)) # no of correct predictions
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) * tf.constant(100.0)
s.run(tf.initialize_all_variables())
ladder.set_all_deterministic(True)
print "acc", s.run([accuracy], feed_dict={inputs: data.test.images, targets: data.test.labels})
ladder.set_all_deterministic(False)
for i in range(num_iter):
images, labels = data.train.next_batch(batch_size)
_, loss_val = s.run([train_step, loss], feed_dict={inputs: images, targets: labels})
print(i, loss_val)
# if i % 50 == 0:
# print "acc" + str(net.catagorical_accurasy(train_x, train_y))
print "acc", s.run([accuracy], feed_dict={inputs: data.test.images, targets: data.test.labels})
