def train_generator():
n_iters = 100000
print_every = 500
plot_every = 500
all_losses = []
total_loss = 0
categories, examples = data.inputs()
n_categories = len(categories)
rnn = GeneratorNN(n_categories, data.N_CHARS, 128, data.N_CHARS)
start = time.time()
for i in range(1, n_iters + 1):
category, sample, category_tensor, sample_tensor = data.random_sample(
categories, examples, one_hot_categories=True)
target_tensor = autograd.Variable(data.target_encode(sample))
output, loss = rnn.train(category_tensor, sample_tensor, target_tensor)
total_loss += loss
if i % print_every == 0:
print('%s (%d %d%%) %.4f' %
(time_since(start), i, i / n_iters * 100, loss))
if i % plot_every == 0:
all_losses.append(total_loss / plot_every)
total_loss = 0
def evaluate(run_dir):
with tf.Graph().as_default() as g:
input_file = os.path.join(FLAGS.train_dir, 'md.json')
print(input_file)
with open(input_file, 'r') as f:
md = json.load(f)
eval_data = FLAGS.eval_data == 'valid'
num_eval = md['%s_counts' % FLAGS.eval_data]
model_fn = select_model(FLAGS.model_type)
with tf.device(FLAGS.device_id):
print('Executing on %s' % FLAGS.device_id)
images, labels, _ = inputs(FLAGS.train_dir, FLAGS.batch_size, FLAGS.image_size, train=not eval_data, num_preprocess_threads=FLAGS.num_preprocess_threads)
logits = model_fn(md['nlabels'], images, 1, False)
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(run_dir, g)
saver = tf.train.Saver()
if FLAGS.requested_step_seq:
sequence = FLAGS.requested_step_seq.split(',')
for requested_step in sequence:
print('Running %s' % sequence)
eval_once(saver, summary_writer, summary_op, logits, labels, num_eval, requested_step)
else:
while True:
print('Running loop')
eval_once(saver, summary_writer, summary_op, logits, labels, num_eval)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def evaluate(run_dir):
with tf.Graph().as_default() as g:
input_file = os.path.join(FLAGS.train_dir, 'md.json')
#print(input_file)
with open(input_file, 'r') as f:
md = json.load(f)
eval_data = FLAGS.eval_data == 'valid'
num_eval = md['%s_counts' % FLAGS.eval_data]
model_fn = select_model(FLAGS.model_type)
with tf.device(FLAGS.device_id):
#print('Executing on %s' % FLAGS.device_id)
images, labels, _ = inputs(FLAGS.train_dir, FLAGS.batch_size, FLAGS.image_size, train=not eval_data, num_preprocess_threads=FLAGS.num_preprocess_threads)
logits = model_fn(md['nlabels'], images, 1, False)
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(run_dir, g)
saver = tf.train.Saver()
if FLAGS.requested_step_seq:
sequence = FLAGS.requested_step_seq.split(',')
for requested_step in sequence:
#print('Running %s' % sequence)
eval_once(saver, summary_writer, summary_op, logits, labels, num_eval, requested_step)
else:
while True:
#print('Running loop')
eval_once(saver, summary_writer, summary_op, logits, labels, num_eval)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def setup_model(args):
"""Sets up the model.
Args:
args: The program args.
Returns:
The model train operation.
"""
model = getattr(models, args.model)
inputs, targets = data.inputs(args.datadir, args.dataset, args.batchsize)
return model(inputs, targets)
def train(dataset, network, checkpoint_dir, batch_size=BATCH_SIZE,
last_step=LAST_STEP, learning_rate=LEARNING_RATE, epsilon=EPSILON,
beta1=BETA_1, beta2=BETA_2, dropout=DROPOUT,
scale_inputs=SCALE_INPUTS, distort_inputs=DISTORT_INPUTS,
zero_mean_inputs=ZERO_MEAN_INPUTS, display_step=DISPLAY_STEP,
save_checkpoint_secs=SAVE_CHECKPOINT_SECS,
save_summaries_steps=SAVE_SUMMARIES_STEPS):
if not tf.gfile.Exists(checkpoint_dir):
tf.gfile.MakeDirs(checkpoint_dir)
with tf.Graph().as_default():
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
global_step_init = -1
if ckpt and ckpt.model_checkpoint_path:
global_step_init = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
global_step = tf.Variable(global_step_init, name='global_step',
dtype=tf.int64, trainable=False)
else:
global_step = tf.contrib.framework.get_or_create_global_step()
data, labels = inputs(dataset, False, batch_size, scale_inputs,
distort_inputs, zero_mean_inputs, shuffle=True)
keep_prob = tf.placeholder(tf.float32)
logits = inference(data, network, keep_prob)
loss = cal_loss(logits, labels)
acc = cal_accuracy(logits, labels)
train_op = train_step(
loss, global_step, learning_rate, beta1, beta2, epsilon)
try:
with tf.train.MonitoredTrainingSession(
checkpoint_dir=checkpoint_dir,
save_checkpoint_secs=save_checkpoint_secs,
save_summaries_steps=save_summaries_steps,
hooks=hooks(display_step, last_step, batch_size, loss, acc)
) as monitored_session:
while not monitored_session.should_stop():
monitored_session.run(train_op,
feed_dict={keep_prob: dropout})
except KeyboardInterrupt:
pass
def train_lstm():
n_hidden = 128
n_iters = 100000
print_every = 500
plot_every = 100
current_loss = 0
all_losses = []
categories, examples = data.inputs()
n_categories = len(categories)
lstm = LabellerLSTM(data.N_CHARS, 64, 64, n_categories, lr=0.1)
start = time.time()
for i in range(1, n_iters + 1):
category, sample, category_tensor, sample_tensor = data.random_sample(
categories, examples)
pred, loss = lstm.train(category_tensor, sample_tensor)
current_loss += loss
# Print iter number, loss, name and guess
if i % print_every == 0:
guess, guess_i = data.decode_prediction(categories, pred)
correct = '✓' if guess == category else '✗ (%s)' % category
print('%d %d%% (%s) %.4f %s / %s %s' %
(i, i / n_iters * 100, time_since(start), loss, sample,
guess, correct))
# Add current loss avg to list of losses
if i % plot_every == 0:
all_losses.append(current_loss / plot_every)
current_loss = 0
plot_losses(all_losses, save=True, fname='lstm_losses.png')
plot_confusion(lstm,
categories,
examples,
fname='lstm_confusion.png',
reshape_for_minibatch=True)
def inputs():
cmd = ""
while (cmd.lower() != "quit"):
cmd = raw_input (">>")
if cmd.lower() == "help":
print ("Available operators are: +, -, *, /, **, root, bin, mean, mode, median, factorial, quit")
elif cmd.lower() == '+':
add1 = raw_input("Enter first number ")
add2 = raw_input("Enter second number ")
print ("The sum of these two numbers is " + str(addition(add1, add2)))
elif cmd.lower() == "-":
sub1 = raw_input("Enter first number ")
sub2 = raw_input("Enter second number ")
print ("The difference of these two numbers is " + str(subtraction(sub1, sub2)))
elif cmd.lower() == "*":
multiply1 = raw_input("Enter first number ")
multiply2 = raw_input("Enter second number ")
print ("The product of these two numbers is " + str(multiplication(multiply1, multiply2)))
elif cmd.lower() == "/":
div1 = raw_input("Enter first number ")
div2 = raw_input("Enter second number ")
print ("The quotient of these two numbers is " + str(division(div1, div2)))
elif cmd.lower() == "**":
base = raw_input("Enter the base ")
exponent = raw_input("Please enter the exponent ")
print (str(power(base, exponent)))
elif cmd.lower() == "root":
root1 = raw_input("Enter a number ")
print ("The square root of that number is " + str(root(root1)))
elif cmd.lower() == "mean":
print data.mean(data.inputs())
elif cmd.lower() == "median":
print data.median(data.inputs())
elif cmd.lower() == "mode":
print data.mode(data.inputs())
elif cmd.lower() == "deviation":
print data.deviation(data.inputs())
elif cmd.lower() == "encrypt":
encrypt.main(cmd)
elif cmd.lower() == "decrypt":
encrypt.main(cmd)
elif cmd.lower() == "bin":
num1 = raw_input("Enter a number to convert to binary ")
print bin_convert(num1)
elif cmd.lower() == "factorial":
print factorial.factorial(factorial.inputs())
elif cmd.lower() == "quit":
pass
else:
print "input not valid, try again"
sys.exit()
def evaluate(dataset, network, checkpoint_dir, eval_dir, batch_size=BATCH_SIZE,
scale_inputs=SCALE_INPUTS, distort_inputs=DISTORT_INPUTS,
zero_mean_inputs=ZERO_MEAN_INPUTS, eval_data=EVAL_DATA):
if not tf.gfile.Exists(checkpoint_dir):
raise ValueError('Checkpoint directory {} doesn\'t exist.'
.format(checkpoint_dir))
if tf.gfile.Exists(eval_dir):
tf.gfile.DeleteRecursively(eval_dir)
tf.gfile.MakeDirs(eval_dir)
with tf.Graph().as_default() as g:
data, labels = inputs(dataset, eval_data, batch_size, scale_inputs,
distort_inputs, zero_mean_inputs, num_epochs=1,
shuffle=False)
keep_prob = tf.placeholder(tf.float32)
logits = inference(data, network, keep_prob)
top_k_op = tf.nn.in_top_k(logits, labels, 1)
variable_averages = tf.train.ExponentialMovingAverage(
MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
init_op = [tf.global_variables_initializer(),
tf.local_variables_initializer()]
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(eval_dir, g)
with tf.Session() as sess:
sess.run(init_op)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
path = ckpt.model_checkpoint_path
# Restore from checkpoint.
saver.restore(sess, path)
# Assuming model_checkpoint_path looks something like
# /my-favorite-path/model.ckpt-0, extract global step from it.
global_step = path.split('/')[-1].split('-')[-1]
else:
print('No checkpoint file found.')
return
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
true_count = 0
if eval_data:
total_count = dataset.num_examples_per_epoch_for_eval
else:
total_count = dataset.num_examples_per_epoch_for_train_eval
num_examples = 0
try:
while(True):
predictions = sess.run([top_k_op],
feed_dict={keep_prob: 1.0})
true_count += np.sum(predictions)
num_examples += batch_size
num_examples = min(num_examples, total_count)
percentage = 100.0 * num_examples / total_count
sys.stdout.write('\r>> Calculating accuracy {:.1f}%'
.format(percentage))
sys.stdout.flush()
except (KeyboardInterrupt, tf.errors.OutOfRangeError):
pass
finally:
precision = 100.0 * true_count / total_count
print('')
print('Accuracy: {:.2f}%'.format(precision))
summary = tf.Summary()
summary.ParseFromString(sess.run(summary_op))
summary.value.add(tag='Precision', simple_value=precision)
summary_writer.add_summary(summary, global_step)
coord.request_stop()
coord.join(threads)