def __init__(self):
self.wordSet = set()
self.vocabGrowth = 0
self.vocabulary = {}
self.vocabulary_inv = []
# Build Vocab
with open('vocab.csv', 'rb') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
if len(row) > 0:
words = preprocess.clean(row[0])
for word in words:
self.addWord(word)
self.addWord(opts["sentence_padding_token"])
self.addWord(opts["unknown_word_token"])
self.vocabulary_size = len(self.wordSet)
store.log("Vocabulary Size: %s" % self.vocabulary_size)
self.embeddings = None
self.data_index = 0
self.data = []
def create():
sid = crypto_util.shash(session['codename'])
if os.path.exists(store.path(sid)):
# if this happens, we're not using very secure crypto
store.log("Got a duplicate ID '%s'" % sid)
else:
os.mkdir(store.path(sid))
session['logged_in'] = True
session['flagged'] = False
return redirect(url_for('lookup'))
def POST(self):
i = web.input('id', fh={}, msg=None, mid=None, action=None)
sid = crypto.shash(i.id)
if os.path.exists(store.path(sid)):
# if this happens, we're not using very secure crypto
store.log('Got a duplicate ID.')
else:
os.mkdir(store.path(sid))
return store_endpoint(i)
def POST(self):
i = web.input('id', fh={}, msg=None, mid=None, action=None)
sid = crypto.shash(i.id)
if os.path.exists(store.path(sid)):
# if this happens, we're not using very secure crypto
store.log('Got a duplicate ID.')
else:
os.mkdir(store.path(sid))
return store_endpoint(i)
def POST(self):
iid = crypto.genrandomid()
if os.path.exists(store.path(crypto.shash(iid))):
# if this happens, we're not using very secure crypto
store.log('Got a duplicate ID.')
else:
os.mkdir(store.path(crypto.shash(iid)))
web.header('Cache-Control', 'no-cache, no-store, must-revalidate')
web.header('Pragma', 'no-cache')
web.header('Expires', '-1')
return render.generate(iid)
def POST(self):
iid = crypto.genrandomid()
if os.path.exists(store.path(crypto.shash(iid))):
# if this happens, we're not using very secure crypto
store.log('Got a duplicate ID.')
else:
os.mkdir(store.path(crypto.shash(iid)))
web.header('Cache-Control', 'no-cache, no-store, must-revalidate')
web.header('Pragma', 'no-cache')
web.header('Expires', '-1')
return render.generate(iid)
def run(self):
for item in self.pubsub.listen():
if item["data"] == "KILL":
self.pubsub.unsubscribe()
print(self, "unsubscribe and finished")
sys.stdout.flush()
break
elif isinstance(item["data"], str):
data = json.loads(item["data"])
if (data[0] == "eval"):
print("eval: %s" % data[1])
sys.stdout.flush()
self.eval(data[1])
elif (data[0] == "reload"):
store.log("reload")
self.reload()
else:
print("bad data: %s" % item["data"])
sys.stdout.flush()
def train(self):
self.data = [ idx for word, idx in self.vocabulary.iteritems() ]
store.log('Sample data %s' % self.data[:10])
def generate_batch(batch_size, num_skips, skip_window):
assert batch_size % num_skips == 0
assert num_skips <= 2 * skip_window
batch = np.ndarray(shape=(batch_size), dtype=np.int32)
labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
span = 2 * skip_window + 1 # [ skip_window target skip_window ]
buffer = collections.deque(maxlen=span)
for _ in range(span):
buffer.append(self.data[self.data_index])
self.data_index = (self.data_index + 1) % len(self.data)
for i in range(batch_size // num_skips):
target = skip_window # target label at the center of the buffer
targets_to_avoid = [ skip_window ]
for j in range(num_skips):
while target in targets_to_avoid:
target = random.randint(0, span - 1)
targets_to_avoid.append(target)
batch[i * num_skips + j] = buffer[skip_window]
labels[i * num_skips + j, 0] = buffer[target]
buffer.append(self.data[self.data_index])
self.data_index = (self.data_index + 1) % len(self.data)
return batch, labels
batch, labels = generate_batch(batch_size=10, num_skips=10, skip_window=5)
for i in range(10):
store.log('%s -> %s' % (batch[i], labels[i, 0]))
store.log('%s -> %s' % (self.vocabulary_inv[batch[i]], self.vocabulary_inv[labels[i, 0]]))
batch_size = 20
embedding_size = opts["embedding_dim"] # Dimension of the embedding vector.
skip_window = 10 # How many words to consider left and right.
num_skips = 20 # How many times to reuse an input to generate a label.
# We pick a random validation set to sample nearest neighbors. Here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent.
valid_size = 16 # Random set of words to evaluate similarity on.
valid_window = 100 # Only pick dev samples in the head of the distribution.
valid_examples = np.array(random.sample(np.arange(valid_window), valid_size))
num_sampled = 64 # Number of negative examples to sample.
graph = tf.Graph()
with graph.as_default():
# Input da 4ta.
train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
# Ops and variables pinned to the CPU because of missing GPU implementation
with tf.device('/cpu:0'):
# Look up embeddings for inputs.
embeddings = tf.Variable(
tf.random_uniform([self.vocabulary_size, embedding_size], -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
# Construct the variables for the NCE loss
with tf.name_scope("nce_weights") as scope:
nce_weights = tf.Variable(
tf.truncated_normal([self.vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
nce_biases = tf.Variable(tf.zeros([self.vocabulary_size]))
nce_biases_hist = tf.histogram_summary("nce_biases", nce_biases)
# Compute the average NCE loss for the batch.
# tf.nce_loss automatically draws a new sample of the negative labels each
# time we evaluate the loss.
with tf.name_scope("loss") as scope:
loss = tf.reduce_mean(
tf.nn.nce_loss(nce_weights, nce_biases, embed, train_labels,
num_sampled, self.vocabulary_size))
# Construct the SGD optimizer using a learning rate of 1.0.
with tf.name_scope("train") as scope:
optimizer = tf.train.GradientDescentOptimizer(0.25).minimize(loss)
# Compute the cosine similarity between minibatch examples and all embeddings.
norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
normalized_embeddings = embeddings / norm
valid_embeddings = tf.nn.embedding_lookup(
normalized_embeddings, valid_dataset)
similarity = tf.matmul(
valid_embeddings, normalized_embeddings, transpose_b=True)
# Step 5: Begin training.
num_steps = 100001
with tf.Session(graph=graph) as session:
# We must initialize all variables before we use them.
merged = tf.merge_all_summaries()
writer = tf.train.SummaryWriter("/tmp/tensor_logs/expiriment_1", session.graph_def)
#Adds an op to initialize all variables
init_op = tf.initialize_all_variables()
# Begins running the init opp
init_op.run()
store.log("Initialized")
average_loss = 0
for step in xrange(num_steps):
batch_inputs, batch_labels = generate_batch(
batch_size, num_skips, skip_window)
feed_dict = {train_inputs : batch_inputs, train_labels : batch_labels}
# We perform one update step by evaluating the optimizer op (including it
# in the list of returned values for session.run()
summary_str, _, loss_val = session.run([merged, optimizer, loss], feed_dict=feed_dict)
writer.add_summary(summary_str, step)
average_loss += loss_val
if step % 2000 == 0:
if step > 0:
average_loss /= 2000
# The average loss is an estimate of the loss over the last 2000 batches.
store.log("Average loss at step %s: %s" % (step, average_loss))
average_loss = 0
# Note that this is expensive (~20% slowdown if computed every 500 steps)
if step % 5000 == 0:
sim = similarity.eval()
for i in xrange(valid_size):
valid_word = self.vocabulary_inv[valid_examples[i]]
top_k = 8 # number of nearest neighbors
nearest = (-sim[i, :]).argsort()[1:top_k+1]
log_str = "Nearest to %s:" % valid_word
for k in xrange(top_k):
close_word = self.vocabulary_inv[nearest[k]]
log_str = "%s %s" % (log_str, close_word)
store.log(log_str)
final_embeddings = normalized_embeddings.eval()
# save final embeddings for Expiriment #2
with open("word_embeddings.pkl", "wb") as f:
pickle.dump(final_embeddings, f)
self.embeddings = final_embeddings
def train():
sentences = []
labels = []
x = []
y = []
_y = []
with open('data.csv', 'rb') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
words = preprocess.clean(row[1])
sentences.append(words)
labels.append(([0, 1] if row[0] == "example" else [1, 0]))
_y.append(1 if row[0] == "example" else 0)
padded_sentences = [ preprocess.pad(sentence) for sentence in sentences ]
x = np.array([[vocab.getIdFromWord(word) for word in sentence] for sentence in padded_sentences])
embeddings = np.array(map(np.unique, ([ vocab.getEmbeddingFromWord(word) for word in sentence for sentence in padded_sentences])))
store.log(embeddings)
store.log(len(embeddings))
store.log(embeddings[0])
store.log(len(embeddings[0]))
y = np.array(labels)
# Split Dataset
# ==================================================
# Load data
print("Loading data...")
# Randomly shuffle data
sss = StratifiedShuffleSplit(_y, 1, test_size=0.1, random_state=0)
for train, test in sss:
x_train = x[train]
y_train = y[train]
x_dev = x[test]
y_dev = y[test]
# Training
# ==================================================
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=opts["allow_soft_placement"],
log_device_placement=opts["log_device_placement"])
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn = TextCNN(
sequence_length=x_train.shape[1],
num_classes=2,
vocab_size=len(embeddings),
embedding_size=opts["embedding_dim"],
embedding_tensor=embeddings,
filter_sizes=map(int, opts["filter_sizes"].split(",")),
num_filters=opts["num_filters"],
l2_reg_lambda=opts["l2_reg_lambda"])
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-4)
grads_and_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
saver = tf.train.Saver(tf.all_variables())
# Initialize all variables
sess.run(tf.initialize_all_variables())
def train_step(x_batch, y_batch):
"""
A single training step
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: opts["dropout_keep_prob"]
}
_, step, loss, accuracy = sess.run(
[train_op, global_step, cnn.loss, cnn.accuracy],
feed_dict)
def dev_step(x_batch, y_batch):
"""
Evaluates model on a dev set
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: 1.0
}
step, loss, accuracy = sess.run(
[global_step, cnn.loss, cnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
# Generate batches
batches = batch_iter(
zip(x_train, y_train), opts["batch_size"], opts["num_epochs"])
# Training loop. For each batch...
for batch in batches:
x_batch, y_batch = zip(*batch)
train_step(x_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % opts["evaluate_every"] == 0:
print("\nEvaluation:")
dev_step(x_dev, y_dev)
print("")
saver.save(sess, opts["model_location"] + "model.chpt")
def train():
sentences = []
labels = []
x = []
y = []
_y = []
with open('data.csv', 'rb') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
words = preprocess.clean(row[1])
sentences.append(words)
labels.append(([0, 1] if row[0] == "example" else [1, 0]))
_y.append(1 if row[0] == "example" else 0)
padded_sentences = [preprocess.pad(sentence) for sentence in sentences]
x = np.array([[vocab.getIdFromWord(word) for word in sentence]
for sentence in padded_sentences])
embeddings = np.array(
map(np.unique, ([
vocab.getEmbeddingFromWord(word) for word in sentence
for sentence in padded_sentences
])))
store.log(embeddings)
store.log(len(embeddings))
store.log(embeddings[0])
store.log(len(embeddings[0]))
y = np.array(labels)
# Split Dataset
# ==================================================
# Load data
print("Loading data...")
# Randomly shuffle data
sss = StratifiedShuffleSplit(_y, 1, test_size=0.1, random_state=0)
for train, test in sss:
x_train = x[train]
y_train = y[train]
x_dev = x[test]
y_dev = y[test]
# Training
# ==================================================
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=opts["allow_soft_placement"],
log_device_placement=opts["log_device_placement"])
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn = TextCNN(sequence_length=x_train.shape[1],
num_classes=2,
vocab_size=len(embeddings),
embedding_size=opts["embedding_dim"],
embedding_tensor=embeddings,
filter_sizes=map(int,
opts["filter_sizes"].split(",")),
num_filters=opts["num_filters"],
l2_reg_lambda=opts["l2_reg_lambda"])
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-4)
grads_and_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
saver = tf.train.Saver(tf.all_variables())
# Initialize all variables
sess.run(tf.initialize_all_variables())
def train_step(x_batch, y_batch):
"""
A single training step
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: opts["dropout_keep_prob"]
}
_, step, loss, accuracy = sess.run(
[train_op, global_step, cnn.loss, cnn.accuracy], feed_dict)
def dev_step(x_batch, y_batch):
"""
Evaluates model on a dev set
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: 1.0
}
step, loss, accuracy = sess.run(
[global_step, cnn.loss, cnn.accuracy], feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g}".format(
time_str, step, loss, accuracy))
# Generate batches
batches = batch_iter(zip(x_train, y_train), opts["batch_size"],
opts["num_epochs"])
# Training loop. For each batch...
for batch in batches:
x_batch, y_batch = zip(*batch)
train_step(x_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % opts["evaluate_every"] == 0:
print("\nEvaluation:")
dev_step(x_dev, y_dev)
print("")
saver.save(sess, opts["model_location"] + "model.chpt")
print()
printinfo(" Mapset for " + buildmap + " successful created")
print()
quit()
"""
zip the images, kml and log
"""
if args.zip_img:
store.zip_img()
store.kml()
if args.log:
store.log()
if os.path.exists(WORK_DIR + "o5m/bbox_map") == True:
os.remove(WORK_DIR + "o5m/bbox_map")
today = datetime.datetime.now()
DATE = today.strftime('%Y%m%d_%H%M')
print()
print()
print(" ----- " + (DATE) + " ----- " + (buildmap) + " ready! -----")
print()
print()
quit()
if config.get('mkgmap', 'logging') == "yes":
config.set('mkgmap', 'logging', 'no')
write_config()
"""
zip the images, kml and log
"""
import store
if (args.zip_img):
store.zip_img()
store.kml()
if config.get('mkgmap', 'logging') == "yes":
store.log()
"""
create the contourlines
"""
if (args.contourlines):
ExitCode = os.path.exists("mystyles/contourlines_style")
if ExitCode == True:
import contourlines
contourlines.create_cont()
else:
printwarning("dir mystyles/contourlines_style not found")
print("")
