def main(FLAGS):
(x_train, y_train, x_test, y_test) = load_mnist_data()
x = tf.compat.v1.placeholder(tf.float32, [None, 28, 28, 1])
y_ = tf.compat.v1.placeholder(tf.float32, [None, 10])
y_conv = model.cryptonets_relu_model(x, 'train')
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=y_, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope('adam_optimizer'):
train_step = tf.compat.v1.train.AdamOptimizer(1e-4).minimize(
cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
for i in range(FLAGS.train_loop_count):
x_batch, y_batch = get_train_batch(i, FLAGS.batch_size, x_train,
y_train)
if i % 100 == 0:
t = time.time()
train_accuracy = accuracy.eval(feed_dict={
x: x_batch,
y_: y_batch
})
print('step %d, training accuracy %g, %g msec to evaluate' %
(i, train_accuracy, 1000 * (time.time() - t)))
t = time.time()
_, loss = sess.run([train_step, cross_entropy],
feed_dict={
x: x_batch,
y_: y_batch
})
if i % 1000 == 999 or i == FLAGS.train_loop_count - 1:
test_accuracy = accuracy.eval(feed_dict={
x: x_test,
y_: y_test
})
print('test accuracy %g' % test_accuracy)
print("Training finished. Saving variables.")
for var in tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES):
weight = (sess.run([var]))[0].flatten().tolist()
filename = (str(var).split())[1].replace('/', '_')
filename = filename.replace("'", "").replace(':0', '') + '.txt'
print("saving", filename)
np.savetxt(str(filename), weight)
squash_layers()
def main(FLAGS):
(x_train, y_train, x_test, y_test) = load_mnist_data()
x = tf.compat.v1.placeholder(tf.float32, [None, 28, 28, 1], name='input')
y_ = tf.compat.v1.placeholder(tf.float32, [None, 10])
y_conv = model.mnist_mlp_model(x)
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=y_, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope('adam_optimizer'):
train_step = tf.compat.v1.train.AdamOptimizer(1e-4).minimize(
cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
for i in range(FLAGS.train_loop_count):
x_batch, y_batch = get_train_batch(i, FLAGS.batch_size, x_train,
y_train)
if i % 100 == 0:
t = time.time()
train_accuracy = accuracy.eval(feed_dict={
x: x_batch,
y_: y_batch
})
print('step %d, training accuracy %g, %g msec to evaluate' %
(i, train_accuracy, 1000 * (time.time() - t)))
t = time.time()
_, loss = sess.run([train_step, cross_entropy],
feed_dict={
x: x_batch,
y_: y_batch
})
if i % 1000 == 999 or i == FLAGS.train_loop_count - 1:
test_accuracy = accuracy.eval(feed_dict={
x: x_test,
y_: y_test
})
print('test accuracy %g' % test_accuracy)
print("Training finished. Saving model.")
save_model(sess, './model', 'model')