def loadEmbeddings(filepath=DEFAULT_FILE_PATH, dimensions=50):
'''
Read the embedding mapping.
'''
count = 0
with open(filepath, "r") as fin:
for line in fin:
line = line.strip()
if not line:
continue
count += 1
vprint(True, "Vocabulary size = " + str(count), color="BLUE")
embeddings = np.zeros((count, dimensions))
tok2id = {}
id = 0
with open(filepath) as ifs:
for line in ifs:
line = line.strip()
if not line:
continue
row = line.split()
token = row[0]
tok2id[token] = id
data = [float(x) for x in row[1:]]
if len(data) != dimensions:
print len(data)
print dimensions
raise RuntimeError("wrong number of dimensions")
embeddings[id] = np.asarray(data)
id += 1
# with open("dictionary.txt", "w") as fout:
# for key in tok2id.keys():
# fout.write("%s %s\n" % (key, tok2id[key]))
return embeddings, tok2id
def main():
'''
This function is used for unit testing this module.
args.hidden_size (i.e. the size of hidden state)
args.num_layers (default = 1, i.e. no stacking),
args.input_seq_length,
args.target_seq_length,
args.input_embedding_size,
args.output_vocab_size,
args.batch_size (i.e. the number of sequences in each batch)
args.optimizer_choice (defualt = "rms", also could be "adam", "grad_desc")
args.learning_rate,
args.grad_clip
args.test
args.verbose
'''
parser = argparse.ArgumentParser()
# RNN cell hidden state's size
parser.add_argument('--hidden_size', type=int, default=96,
help='size of RNN cell hidden state')
# Number of stacked RNN layers. Only a single layer implemented
parser.add_argument('--num_layers', type=int, default=1,
help='number of stacked RNN layers')
# Larger than the max length of each input sequence
parser.add_argument('--input_seq_length', type=int, default=20,
help='maximum length of each input sequence or larger')
# Larger than the max of each target sequence
parser.add_argument('--target_seq_length', type=int, default=20,
help='maximum length of each target sequence or larger')
# Embedding size of input
parser.add_argument('--input_embedding_size', type=int, default=96,
help='embedding size of input vectors')
# Embedding size of output
parser.add_argument('--output_vocab_size', type=int, default=92,
help='size of output vocabulary')
# Batch size
parser.add_argument('--batch_size', type=int, default=100,
help='number of sequences in a batch')
# Choice of optimzier
parser.add_argument('--optimizer_choice', type=str, default='rms',
help='rms (defualt), adam, grad_desc')
# Learning rate
parser.add_argument('--learning_rate', type=float, default=0.002,
help='Learning rate')
# Gradient clip, i.e. maximum value of gradient amplitute allowed
parser.add_argument('--grad_clip', type=float, default=None,
help='gradient upbound, i.e. maximum value of gradient amplitute allowed')
# Model unit testing flag, default to False
parser.add_argument('-t','--test', action='store_true',
help='only set to true when performing unit test')
# Verbosity flag, default to False
parser.add_argument('-v','--verbose', action='store_true',
help='only set to true when you want verbosity')
# Parse the arguments, and construct the model
args = parser.parse_args()
#args.test = True
model = HierLSTMTransModel(args)
print "arguments:"
vprint(args.verbose, model.get_args().__dict__, color=None)
def __init__(self,
batch_size=10,
seq_lengths=[2, 3],
token_sizes=[20, 1],
usage="train",
if_testing=False):
'''
Initialization of a data loader.
Params:
batch_size: size of batch.
seq_lengths: a integer list, [input_seq_length, target_seq_length]
token_sizes: a integer list, [input_embedding_size, target_word_index_size], where target_word_index_size = 1
usage: what this data loader is used for: "train", "dev", test"
if_testing: if True, returns random data.
Returns:
None
'''
#train samples 84973
#dev samples 10614
#test samples 10617
self.batch_size = batch_size
self.seq_lengths = seq_lengths
self.complex_length = seq_lengths[0]
self.simple_length = seq_lengths[1]
self.token_sizes = token_sizes
self.embedding_size = token_sizes[0]
self.usage = usage
self.if_testing = if_testing
self.num_batches = int(10614 /
batch_size) # Changed from 102696 % batch_size
#self.if_first_batch = True
vprint(True, "Loading embeddings...", color="BLUE")
if self.usage == "train":
#self.data_file = open('../data/train_data.txt')
self.data_file = open('../data/dev_new_data.txt')
self.data_file_path = '../data/dev_new_data.txt'
elif self.usage == "test":
#self.data_file = open('../data/test_data.txt')
self.data_file = open('../data/dev_new_data.txt')
#self.data_file_path = '../data/test_data.txt'
self.data_file_path = '../data/dev_new_data.txt'
if self.if_testing == True:
# Testing code. Returns random numbers. Do not touch..
self.num_batches = 15
elif self.if_testing == False:
# Real work
self.embeddings, self.tok2id = loadEmbeddings(
filepath=DEFAULT_FILE_PATH, dimensions=self.embedding_size)
vprint(True, "Finished loading embeddings", color="BLUE")
def loadDict(filepath=DEFAULT_FILE_PATH, dimensions=50):
count = 0
with open(filepath, "r") as fin:
for line in fin:
line = line.strip()
if not line:
continue
count += 1
vprint(True, "Vocabulary size = " + str(count), color="BLUE")
id2tok = {}
id = 0
with open(filepath) as ifs:
for line in ifs:
line = line.strip()
if not line:
continue
row = line.split()
token = row[0]
id2tok[id] = token
id += 1
return id2tok
def __init__(self, args, training=True):
'''
Initialization function for the class Model.
Params:
args: contains arguments required for the Model creation --
args.hidden_size (i.e. the size of hidden state)
args.num_layers (default = 1, i.e. no stacking),
args.input_seq_length,
args.target_seq_length,
args.input_embedding_size,
args.output_vocab_size,
args.target_token_size (=1, target token is target word's index)
args.batch_size (i.e. the number of sequences in each batch),
args.optimizer_choice (defualt = "rms", also could be "adam", "grad_desc"),
args.learning_rate,
args.grad_clip
args.test
args.verbose
training: indicates whether this is a training session
Returns:
None
NOTE Each cell's input is batch_size x 1 x input_embedding_size
NOTE Each cell's output is batch_size x 1 x hidden_size (needs to be converted)
'''
#if training == False:
# args.batch_size = 2
# Store the arguments, and print the important argument values
self.args = args
verbose = self.args.verbose
print("VanillaLSTMTransModel initializer is called..\n" \
+ "Time: " + time.ctime() + "\n" \
+ " args.hidden_size (H) = " + str(self.args.hidden_size) + "\n" \
+ " args.input_embedding_size (Di) = " + str(self.args.input_embedding_size) + "\n" \
+ " args.output_vocab_size (Vo) = " + str(self.args.output_vocab_size) + "\n" \
+ " args.num_layers = " + str(self.args.num_layers) + "\n" \
+ " args.optimizer_choice = " + self.args.optimizer_choice + "\n" \
+ " args.learning_rate = " + str(self.args.learning_rate) + "\n" \
+ " args.grad_clip = " + str(self.args.grad_clip) + "\n")
if training:
print("This is a training session..")
print("Input batch size = " + str(self.args.batch_size) + "\n")
else:
print("This is a session other than training..")
print("Input batch size = " + str(self.args.batch_size) + "\n")
# initialize LSTM cell units, hidden_size is the dimension of hidden state
# encoder_cell = tf.nn.rnn_cell.BasicLSTMCell(self.args.hidden_size, state_is_tuple=True)
# decoder_cell = tf.nn.rnn_cell.BasicLSTMCell(self.args.hidden_size, state_is_tuple=True)
encoder_cell = tf.nn.rnn_cell.LSTMCell(
num_units=self.args.hidden_size,
initializer=tf.contrib.layers.xavier_initializer(),
state_is_tuple=True)
decoder_cell = tf.nn.rnn_cell.LSTMCell(
num_units=self.args.hidden_size,
initializer=tf.contrib.layers.xavier_initializer(),
state_is_tuple=True)
# convert cell's outputs (batch_size x hidden_size for each cell) to batch_size x output_vocab_size
# y_hat = softmax(tf.add(tf.matmul(cell_output, output_ws), output_bs)), output_bs = zeros, for now
with tf.variable_scope("vanLSTM_decoder/decoder_accessory"):
self.output_ws = tf.get_variable(
"output_ws",
[self.args.hidden_size, self.args.output_vocab_size])
output_affine_map_lambda = lambda cell_output_: tf.matmul(
cell_output_, self.output_ws)
output_converter_lambda = lambda cell_output_: tf.nn.softmax(
logits=output_affine_map_lambda(
cell_output_), dim=-1) # -1: last dimension
self.output_affine_map_lambda = output_affine_map_lambda
self.output_converter_lambda = output_converter_lambda
# Multi-layer RNN ocnstruction, if more than one layer
if self.args.num_layers <= 0 or isinstance(self.args.num_layers,
int) == False:
raise ValueError(
"Specified number of layers is non-positive or is not an integer."
)
elif self.args.num_layers >= 2:
vprint(
True,
"Stacked RNN: number of layers = " + str(self.args.num_layers))
encoder_cell = tf.nn.rnn_cell.MultiRNNCell([encoder_cell] *
self.args.num_layers,
state_is_tuple=True)
decoder_cell = tf.nn.rnn_cell.MultiRNNCell([decoder_cell] *
self.args.num_layers,
state_is_tuple=True)
# TODO: (improve) Dropout layer can be added here
# Store the recurrent unit
self.encoder_cell = encoder_cell
self.decoder_cell = decoder_cell
# Create encoder and decoder RNNChain instances
encoder = RNNChain(self.encoder_cell,
name="vanLSTM_decoder",
scope="vanLSTM_encoder")
decoder = RNNChain(self.decoder_cell,
name="vanLSTM_decoder",
scope="vanLSTM_decoder")
self.encoder = encoder
self.decoder = decoder
# Input data contains sequences of input tokens of input_embedding_size dimension
self.input_data = tf.placeholder(
tf.float32,
[None, self.args.input_seq_length, self.args.input_embedding_size])
# Target data contains sequences of output tokens of target_token_size dimension (=1)
self.target_data = tf.placeholder(
tf.int32,
[None, self.args.target_seq_length, self.args.target_token_size])
# Target lengths list contains numbers of non-padding input tokens in each sequence in this batch,
# each element is an integer, indicating the number of non-padding tokens of a sequence.
self.target_lens_list = tf.placeholder(tf.int32, [None])
# Learning rate
self.lr = tf.Variable(self.args.learning_rate,
trainable=False,
name="learning_rate")
# Initial cell state of LSTM (initialized with zeros)
self.initial_state = encoder_cell.zero_state(
batch_size=self.args.batch_size, dtype=tf.float32)
# Preprocessing the information got from placeholders.
# First, target_lens_list does not need any further actions.
target_lens_list = self.target_lens_list
# Second, input_data and target_data need reshaping.
# Split inputs and targets according to sequences: a 3D Tensor, num_of_seq x seq_length x Di/Vo
# -> list of size seq_length, each of whose element is of num_of_seq x 1 x Di/Vo
if tf.__version__[0:2] == '0.':
input_data_temp = tf.split(split_dim=1,
num_split=self.args.input_seq_length,
value=self.input_data)
target_data_temp = tf.split(split_dim=1,
num_split=self.args.target_seq_length,
value=self.target_data)
elif tf.__version__[0:2] == '1.':
input_data_temp = tf.split(value=self.input_data,
num_split=self.args.input_seq_length,
split_dim=1)
target_data_temp = tf.split(value=self.target_data,
num_split=self.args.target_seq_length,
split_dim=1)
# Squeeze: list of size seq_length, each of which is num_of_seq x 1 x Di/Vo
# -> list of size seq_length, each of which is num_of_seq x Di/Vo
input_data_list = [
tf.squeeze(input=list_member, axis=[1])
for list_member in input_data_temp
]
target_data_list = [
tf.squeeze(input=list_member, axis=[1])
for list_member in target_data_temp
]
del input_data_temp, target_data_temp
## This is where the LSTM models differ from each other in substance.
## The other code might also differ but they are not substantial.
# call the encoder
#print("[DEBUG] self.initial_state: " + str(self.initial_state))
#with tf.variable_scope("vanLSTM_encoder"):
vprint(True, "Building encoder...", color="MAG")
encoder_start_time = time.time()
_, self.encoder_final_state = encoder.run(
inputs=input_data_list,
chain_length=None,
cell_input_size=[
self.args.batch_size, self.args.input_embedding_size
],
initial_state=self.initial_state,
feed_previous=False,
verbose=self.args.verbose)
self.encoder_end_state = self.initial_state
encoder_end_time = time.time()
vprint(True,
" -- Encoder built. Time used: " +
str(encoder_end_time - encoder_start_time) + " s",
color="MAG")
# call the decoder
#with tf.variable_scope("vanLSTM_decoder"):
#print("[DEBUG] self.encoder_final_state: " + str(self.encoder_final_state))
#print("[DEBUG] self.decoder_inital_state: " + str(self.decoder_cell.zero_state(batch_size=self.args.batch_size, dtype=tf.float32)))
vprint(True, "Building decoder...", color="MAG")
decoder_start_time = time.time()
# cell_outputs is list of length target_seq_length, each element is batch_size x hidden_size
self.cell_outputs, _ = decoder.run(
inputs=input_data_list,
chain_length=self.args.target_seq_length,
cell_input_size=[
self.args.batch_size, self.args.output_vocab_size
],
initial_state=self.encoder_final_state,
feed_previous=True,
loop_func=self.output_converter_lambda,
verbose=self.args.verbose)
decoder_end_time = time.time()
vprint(True,
" -- Decoder built. Time used: " +
str(decoder_end_time - decoder_start_time) + " s",
color="MAG")
vprint(True, "Building output converter...", color="MAG")
converter_start_time = time.time()
# output_data is softmaxed. It is a list of length target_seq_length, each element is batch_size x output_vocab_size
self.output_data = [
output_converter_lambda(cell_output_)
for cell_output_ in self.cell_outputs
]
converter_end_time = time.time()
vprint(True,
" -- Converter built. Time used: " +
str(converter_end_time - converter_start_time) + " s",
color="MAG")
# Compute the cost scalar: specifically, the average cost per sequence
vprint(True, "Building cost calculator...", color="MAG")
sum_of_cost = self.get_sum_of_cost(cell_outputs=self.cell_outputs,
targets=target_data_list,
targets_lens=target_lens_list)
#self.cost = tf.Variable(0.)
self.cost = tf.div(sum_of_cost, self.args.batch_size)
print("\n[DEBUG] self.cost: ")
print self.cost
# We only deal with back-propagration during training phase.
if training == True:
# Get trainable_variables list and count them.
# Also clip the gradients if they are larger than self.args.grad_clip
vprint(True,
"\nAggregating all trainable variables...",
color="BLUE")
trainable_vars = tf.trainable_variables()
num_trainable_components = 0
vprint(True,
"\nNumber of trainable Tensors = " +
str(len(trainable_vars)),
color="GREEN")
for i, var in enumerate(trainable_vars):
num_trainable_components += np.product(
trainable_vars[i].get_shape().as_list())
vprint(True,
" " + str(trainable_vars[i].name) + \
"\t" + str(trainable_vars[i].get_shape()) + \
" x " + str(trainable_vars[i].dtype.name),
color="GREEN")
vprint(True,
"Number of trainable scalar components = " +
str(num_trainable_components),
color="GREEN")
if num_trainable_components >= 1e3 and num_trainable_components < 1e4:
vprint(True,
" -- that is in the order of 10e3: thousands\n",
color="GREEN")
elif num_trainable_components >= 1e4 and num_trainable_components < 1e5:
vprint(True,
" -- that is in the order of 10e4: tens of thousands\n",
color="GREEN")
elif num_trainable_components >= 1e5 and num_trainable_components < 1e6:
vprint(
True,
" -- that is in the order of 10e5: hundreds of thousands\n",
color="GREEN")
elif num_trainable_components >= 1e6 and num_trainable_components < 1e7:
vprint(True,
" -- that is in the order of 10e6: millions\n",
color="GREEN")
elif num_trainable_components >= 1e7 and num_trainable_components < 1e8:
vprint(True,
" -- that is in the order of 10e7: tens of millions\n",
color="GREEN")
elif num_trainable_components >= 1e8 and num_trainable_components < 1e9:
vprint(
True,
" -- that is in the order of 10e8: hundreds of millions\n",
color="GREEN")
elif num_trainable_components >= 1e9:
vprint(
True,
" -- that is in the order of 10e9 to 10e-Infinity: billions or higher",
color="GREEN")
self.num_of_trainable_components = num_trainable_components
# Compute the gradient of cost with respect of the trainable variables.
vprint(
True,
"Calculating gradient expressions for all trainable variables. Be patient...",
color="BLUE")
grad_start_time = time.time()
# self.gradients is a list of tuples of (grad_value, variable_name)
self.gradients = tf.gradients(self.cost, trainable_vars)
grad_end_time = time.time()
vprint(
True,
" -- Finished calculating gradient expressions. Time used: " +
str(grad_end_time - grad_start_time) + " s",
color="BLUE")
# A hack: when testing, elements in gradients may ALL be None, and it causes problems in clip_by_global_norm()
# This is just for validation of the code.
if self.args.test == True:
print("TESTING TESTING TESTING")
for i in xrange(len(self.gradients)):
if self.gradients[i] == None:
self.gradients[i] = tf.zeros(
shape=trainable_vars[i].get_shape(),
dtype=tf.float32)
if self.args.grad_clip != None:
clipped_grads, _ = tf.clip_by_global_norm(
self.gradients, self.args.grad_clip)
else:
clipped_grads = self.gradients
# Using RMSprop, inspired by the LSTM paper of Dr. Alahi, Prof. Saverese, and Prof. Fei-Fei Li
if self.args.optimizer_choice == "rms":
optimizer = tf.train.RMSPropOptimizer(self.lr)
elif self.args.optimizer_choice == "adam":
optimizer = tf.train.AdamOptimizer(self.lr)
elif self.args.optimizer_choice == "grad_desc":
optimizer = tf.train.GradientDescentOptimizer(self.lr)
else:
raise ValueError("Optimizer not supported: " +
self.args.optimizer_choice)
# Train operator. Apply gradients. If a gradient of a variable is None, it will be weeded out.
self.train_op = optimizer.apply_gradients(
zip(clipped_grads, trainable_vars))
def __init__(self, args, model_choice="V", if_testing=False):
'''
Instantiate a model, a save file, and a log text file.
Params:
args: contains arguments required for the model creation.
model_choice: specify the choice of model, default to "VanillaLSTMTransModel".
V: VanillaLSTMTransModel
H: HierLSTMTransModel, i.e. Hierarchical LSTM Model
A/AH: AttenHierLSTMTransModel, i.e. Hierarchical LSTM Model with Attention
Returns:
None
'''
# Save the args
self.args = args
self.if_testing = if_testing
# Instantiate a model
build_start_time = time.time()
if args.continue_training == False:
# First time training
vprint(True, "Trainer is called. First time training.")
vprint(True,"\033[1;m" + "Building computation graph for the model..." + "\033[0;m", color="CYAN")
if model_choice == "VanillaLSTMTransModel" or model_choice == "V":
model = VanillaLSTMTransModel(args)
model_abbr = "V"
elif model_chice == "AttenVanillaLSTMTransModel" or model_choice == "AV":
model = AttenVanillaLSTMTransModel(args)
model_abbr = "AV"
elif model_choice == "HierLSTMTransModel" or model_choice == "H":
model = HierLSTMTransModel(args)
model_abbr = "H"
elif model_choice + "AttenHierLSTMTransModel" or model_choice == "AH":
model = AttenHierLSTMTransModel(args)
model_abbr = "AH"
else:
raise ValueError("Model choice: " + str(model_choice) + " is not supported")
self.model = model
# Directory to save things
if args.test == True:
self.directory = "../RUN_" + model_abbr
#self.directory = "../TestRUN_" + model_abbr + time.strftime("_%b%d_%H-%M-%S")
else:
self.directory = "../RUN_" + model_abbr
os.mkdir(self.directory)
else:
# Continuing training
if model_choice == "VanillaLSTMTransModel" or model_choice == "V":
model_abbr = "V"
elif model_choice == "HierLSTMTransModel" or model_choice == "H":
model_abbr = "H"
elif model_choice + "AttenHierLSTMTransModel" or model_choice == "AH" or model_choice == "A":
model_abbr = "A"
else:
raise ValueError("Model choice: " + str(model_choice) + " is not supported")
self.directory = "../RUN_" + model_abbr
vprint(True, "Trainer is called. Continuing training.")
try:
with open(os.path.join(self.directory, 'args.pkl'), 'r+') as f:
saved_args = pickle.load(f)
self.args = saved_args
# Don't forget this line below
self.args.continue_training = True
except:
raise ValueError("The specified model is either not trained, damaged,\
or in a wrong path. It should be ../RUN_" + model_abbr + "/args.pkl")
vprint(True, "\033[1;m" + "Rebuilding computation graph for the model..." + "\033[0;m", color="CYAN")
if model_choice == "VanillaLSTMTransModel" or model_choice == "V":
model = VanillaLSTMTransModel(saved_args)
elif model_choice == "HierLSTMTransModel" or model_choice == "H":
model = HierLSTMTransModel(saved_args)
elif model_choice + "AttenHierLSTMTransModel" or model_choice == "AH" or model_choice == "A":
model = AttenHierLSTMTransModel(saved_args)
self.model = model
build_end_time = time.time()
vprint(True,"\033[1;m" + "Graph built. Time used: " + str(build_end_time - build_start_time) + " seconds" + "\033[0;m", color="CYAN")
# Create/open a save file to save things.
if args.continue_training == False:
with open(os.path.join(self.directory, 'args.pkl'), 'a') as f:
pickle.dump(args, f)
vprint(True, "Arguments saved to file: " + self.directory + "/args.pkl")
else:
# Continuing from previous traing, do not write the arguments again.
pass
log = open(os.path.join(self.directory, 'log.txt'), 'a') # append from EOF. Create file if not found.
log.write("Log file: " + self.directory + '\n')
log.close()
reduced_log = open(os.path.join(self.directory, 'reduced_log.txt'), 'a') # append from EOF. Create file if not found.
reduced_log.write("Log file: " + self.directory + '\n')
def train(self, num_epochs=100, save_every_batch=400):
'''
Train the model.
Params:
num_epochs: number of epochs, defualt to 100
save_every_batch: period of saving, epoch * data_loader.get_num_batches() + batch_index, defualt to 400.
NOTE in the current implementation, this argument is unused. I opted to save after each epoch.
Returns:
None
'''
args = self.args
decay_rate = 0.95 # You may modify it yourself. decay_rate in (0,1]
log = open(os.path.join(self.directory, 'log.txt'), 'a')
reduced_log = open(os.path.join(self.directory, 'reduced_log.txt'), 'a')
data_loader = Dataloader(batch_size=args.batch_size,
seq_lengths=[args.input_seq_length, args.target_seq_length],
token_sizes=[args.input_embedding_size, args.target_token_size],
if_testing=self.if_testing)
num_batches = data_loader.get_num_batches()
# Tic
train_start_time = time.time()
vprint(True, "")
with tf.Session() as sess:
if args.continue_training == False:
# Initialize all varaibles in the computational graph
# r0.11 or earlier: sess.run(tf.initialize_all_variables())
sess.run(tf.global_variables_initializer())
# Add all the variables to the registration list of variables to be saved
saver = tf.train.Saver(tf.global_variables(), max_to_keep=50)
else:
# Access the checkpoint file
ckpt = tf.train.get_checkpoint_state(checkpoint_dir=self.directory, latest_filename=None)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=50)
saver.restore(sess=sess, save_path=ckpt.model_checkpoint_path)
print ckpt.model_checkpoint_path
train_loss = 0.0
# For each epoch
for e in range(num_epochs):
# Reset data loader so that it reads from the beginning.
data_loader.reset()
print args.continue_training
vprint(args.continue_training, "\033[1;mContinued training\033[0;m", color="MAG")
vprint(True, "\033[1;mStepped in epoch e = " + str(e+1) + "\033[0;m", color="MAG")
# Assign the learning rate (decayed acceleration to the epoch number)
sess.run(tf.assign(self.model.lr, args.learning_rate * (decay_rate ** e)))
#Get the initial state of the encoder
state = sess.run(self.model.initial_state)
# For each batch in this epoch
for b in range(num_batches):
vprint(True, "Stepped in epoch = " + str(e+1) + ", batch b = " + str(b+1), color="MAG")
# Tic
batch_start_time = time.time()
# Get the input (x) and target (y) data of the current batch
vprint(True, "Getting batch.. b = " + str(b+1), color="MAG")
# x: input batch. It is a list of length batch_size, each element of which is of size input_seq_length x input_embedding_size
# y: target batch. It is a list of length batch_size, each element of which is of size target_seq_length x target_token_size (=1)
# yl: target sequences' lengths. It is a list of length batch_size, each element of which is an integer.
x, y, yl = data_loader.next_batch()
vprint(True, "Got batch. Run the session...", color="MAG")
# Feed the input and target data and the initial cell state
feed = {self.model.input_data: x, self.model.target_data: y, self.model.target_lens_list: yl, self.model.initial_state: state}
# Fetch the loss of the self.model on this batch
# output_data is softmaxed. It is a list of length target_seq_length, each element is batch_size x output_vocab_size
try:
_, train_loss = sess.run([self.model.train_op, self.model.cost], feed_dict=feed)
#print output_data[0]
except Exception as exception_msg:
vprint(True, "sess.run() runtime error.", color="RED")
print exception_msg
# Toc
batch_end_time = time.time()
# Print something and write to log
log_entry = "epoch {}/{}, global step number {}/{}, \n\
train_loss = {:.5f}, \n\
time/batch = {:.3f} s \n".format(e + 1,
num_epochs,
e * num_batches + b + 1,
num_epochs * num_batches,
train_loss,
batch_end_time - batch_start_time)
reduced_log_entry = "{} {} {} {} {:.5f}\n".format(e + 1,
num_epochs,
e * num_batches + b + 1,
num_epochs * num_batches,
train_loss)
# Print on screen
vprint(True, log_entry, color=None)
# Append to log.txt and reduced_log.text.
log.write(log_entry)
reduced_log.write(reduced_log_entry)
# Save the model after each epoch
checkpoint_path = os.path.join(self.directory, 'model.ckpt')
time_stamp_integer = int(time.time())
saver.save(sess, checkpoint_path, global_step=time_stamp_integer)
print("Saved to {}".format(checkpoint_path + "-" + str(time_stamp_integer)))
log.write("Saved to {}".format(checkpoint_path + "-" + str(time_stamp_integer)))
train_end_time = time.time()
vprint(True, "\033[1;m" + "\nTraining finished. Time used: " + str(train_end_time - train_start_time) + " seconds" + "\033[0;m", color="CYAN")
log.write("Training finished.\n")
log.close()
reduced_log.close()
def run(self,
inputs,
chain_length,
initial_state,
cell_input_size=None,
feed_previous=False,
loop_func=None,
verbose=False):
'''
RNN segment works.
Params:
inputs: list of Tensors, variable length, each element is of size
batch_size x input_vector_size. If feed_previous == True, then
inputs does nothing. inputs should not be None, because it has
other delicate uses later in this code.
chain_length: length of this RNN chain. If feed_previous=True, then
chain_length does nothing (can be None) because the length of
chain is determined by inputs' length; if False, then chain_length
determines the length of this RNN chain.
initial_state: the initial state, of size batch_size x hidden_size
cell_input_size: the size of each cell's input, can be either None, or
a 2-integer list [batch_size, input_vector_size]. If cell_input_size
is None or feed_previous=False, then cell_input_size assumes the
value of the size of the input acquired by the first cell, regardless
of what cell_input_size is.
feed_previous: if True, then a cell's input is the previous cell's
output processed by the loop affine function, except the first cell,
whose input is specified in code with size of cell_input_size (i.e.
[batch_size, input_vector_size] 2-integer list).
loop_func: a lambda function that converts a cell's output from
batch_size x hidden_size to batch_size x output_vocab_size
verbose: verbosity flag
Returns:
outputs: list of tensors, length equals to the inputs, each element is
of size batch_size x hidden_size. NOTE NOT converted to yhat
cell_state: the cell state in the end of this cell segment
'''
if inputs == None:
raise ValueError("RNNChain::run()'s inputs should not be None")
if feed_previous == True and loop_func == None:
raise ValueError(
"feed_previous is True, but loop_func is not given")
if feed_previous == True:
# This is a hack. Reassign inputs.
inputs = range(chain_length)
cell = self.cell
scope = self.scope
state = initial_state
outputs = []
# if verbose:
# print "\n\033[32m[INFO] an rnn_segement.run() is linked into the computational graph in scope " + scope + "\033[m"
# print "\n[INFO] an rnn_segement.run() is linked into the computational graph in scope " + scope
vprint(
verbose,
"\n[INFO] an rnn_segement.run() is linked into the computational graph in scope "
+ scope,
color="g")
with tf.variable_scope(scope):
cell_state = initial_state
vprint(verbose, self.get_info_str(cell_state))
outputs = []
if feed_previous == True:
if cell_input_size == None or isinstance(
cell_input_size,
list) == False or len(list(cell_input_size)) != 2:
raise ValueError(
"cell_input_size should be a two-integer list, [batch_size, input_vector_size]"
)
# TODO: (improve) assume the input to the first cell is a tensor of zero, for now
prev_cell_output_yhat = tf.zeros(list(cell_input_size))
for i, cell_input in enumerate(inputs):
if (feed_previous == True) and (prev_cell_output_yhat != None):
# in this case, ignore cell_input's value and reassign it
cell_input = prev_cell_output_yhat
if i > 0:
# if the cell is reused once, declare reusing since the second time you use it
tf.get_variable_scope().reuse_variables()
vprint(verbose,
"\n[INFO] BEFORE CELL " + scope + "\n" + str(i) +
" cell_input: Tensor " +
str(cell_input.get_shape().as_list()),
color="b")
vprint(verbose, self.get_info_str(cell_state), color="b")
# cell_output: batch_size x hidden_size, cell_state's dimension depends on cell's type
cell_output, cell_state = cell(cell_input, cell_state)
vprint(verbose,
"[INFO] AFTER CELL " + scope + "\n" + str(i) +
" cell_output: Tensor " +
str(cell_output.get_shape().as_list()),
color="cyan")
vprint(verbose, self.get_info_str(cell_state), color="cyan")
# append the cell's output to the output sequence
outputs.append(cell_output)
if feed_previous == True:
prev_cell_output_yhat = loop_func(cell_output)
vprint(verbose,
"\n[INFO] PREV_CELL_OUTPUT " + scope + "\n" +
str(i) + " prev_cell_output_yhat: " +
str(prev_cell_output_yhat.get_shape().as_list()),
color="YELLOW")
# the last cell's state is the final state
final_state = cell_state
# NOTE "outputs" are the cells' immediate outputs, not converted to yhat.
self.outputs = outputs
self.final_state = final_state
return outputs, final_state
'-v',
'--verbose',
action='store_true',
help='only set to true when you want verbosity') # Do NOT Touch
# Continuing training flag, default to False
parser.add_argument(
'-c',
'--continue_training',
action='store_true',
help='if set, then continue training from the previous checkpoint'
) # Do NOT Touch
# Parse the arguments, and construct the model
args = parser.parse_args()
# You may perform testing without actually loading data by calling: python run.py -t
vprint(True, "run.py -- arg.test = " + str(args.test), color="CYAN")
if args.test == True:
# Test the code using artificial numbers, without actually loading the data.
############################## RNN Configuration ##############################
args.hidden_size = 16
args.num_layers = 2
################################## Data Info ##################################
args.input_seq_length = 21
args.target_seq_length = 23
args.input_embedding_size = 16
args.output_vocab_size = 150
############################### Trainer Settings ##############################
args.epochs = 5
args.batch_size = 8
args.grad_clip = 15
args.learning_rate = 0.05
def __init__(self, args, training=True):
'''
Initialization function for the class Model.
Params:
args: contains arguments required for the Model creation --
args.hidden_size (i.e. the size of hidden state)
args.num_layers (default = 1, i.e. no stacking),
args.input_seq_length,
args.target_seq_length,
args.input_embedding_size,
args.output_vocab_size,
args.target_token_size (=1, target token is target word's index)
args.batch_size (i.e. the number of sequences in each batch),
args.optimizer_choice (defualt = "rms", also could be "adam", "grad_desc"),
args.learning_rate,
args.grad_clip
args.test
args.verbose
training: indicates whether this is a training session
Returns:
None
NOTE Each cell's input is batch_size x 1 x input_embedding_size
NOTE Each cell's output is batch_size x 1 x hidden_size (needs to be converted)
'''
if training == False:
args.batch_size = 1
# Store the arguments, and print the important argument values
self.args = args
verbose = self.args.verbose
print("VanillaLSTMTransModel initializer is called..\n" \
+ "Time: " + time.ctime() + "\n" \
+ " args.hidden_size (H) = " + str(self.args.hidden_size) + "\n" \
+ " args.input_embedding_size (Di) = " + str(self.args.input_embedding_size) + "\n" \
+ " args.output_vocab_size (Vo) = " + str(self.args.output_vocab_size) + "\n" \
+ " args.num_layers = " + str(self.args.num_layers) + "\n" \
+ " args.optimizer_choice = " + self.args.optimizer_choice + "\n" \
+ " args.learning_rate = " + str(self.args.learning_rate) + "\n" \
+ " args.grad_clip = " + str(self.args.grad_clip) + "\n")
if training:
print("This is a training session..")
print("Input batch size = " + str(self.args.batch_size) + "\n")
else:
print("This is a session other than training..")
print("Input batch size = " + str(self.args.batch_size) + "\n")
# initialize LSTM cell units, hidden_size is the dimension of hidden state
word_encoder_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=self.args.hidden_size, initializer=tf.contrib.layers.xavier_initializer(), sstate_is_tuple=True)
sent_encoder_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=self.args.hidden_size, initializer=tf.contrib.layers.xavier_initializer(), sstate_is_tuple=True)
sent_decoder_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=self.args.hidden_size, initializer=tf.contrib.layers.xavier_initializer(), sstate_is_tuple=True)
word_decoder_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=self.args.hidden_size, initializer=tf.contrib.layers.xavier_initializer(), sstate_is_tuple=True)
# convert cell's outputs (batch_size x hidden_size for each cell) to batch_size x output_vocab_size
# y_hat = softmax(tf.add(tf.matmul(cell_output, output_ws), output_bs)), output_bs = zeros, for now
with tf.variable_scope("vanLSTM_decoder/decoder_accessory"):
self.output_ws = tf.get_variable("output_ws", [self.args.hidden_size, self.args.output_vocab_size])
output_affine_map_lambda = lambda cell_output_: tf.matmul(cell_output_, self.output_ws)
output_converter_lambda = lambda cell_output_: tf.nn.softmax(logits=output_affine_map_lambda(cell_output_), dim=-1) # -1: last
self.output_affine_map_lambda = output_affine_map_lambda
self.output_converter_lambda = output_converter_lambda
# Multi-layer RNN ocnstruction, if more than one layer
if self.args.num_layers <= 0 or isinstance(self.args.num_layers, int) == False:
raise ValueError("Specified number of layers is non-positive or is not an integer.")
elif self.args.num_layers >= 2:
vprint(True, "Stacked RNN: number of layers = " + str(self.args.num_layers))
word_encoder_cell = tf.nn.rnn_cell.MultiRNNCell([word_encoder_cell] * self.args.num_layers, state_is_tuple=True)
sent_encoder_cell = tf.nn.rnn_cell.MultiRNNCell([sent_encoder_cell] * self.args.num_layers, state_is_tuple=True)
sent_decoder_cell = tf.nn.rnn_cell.MultiRNNCell([sent_decoder_cell] * self.args.num_layers, state_is_tuple=True)
word_decoder_cell = tf.nn.rnn_cell.MultiRNNCell([word_decoder_cell] * self.args.num_layers, state_is_tuple=True)
# TODO: (improve) Dropout layer can be added here
# Store the recurrent unit
self.word_encoder_cell = word_encoder_cell
self.sent_encoder_cell = sent_encoder_cell
self.sent_decoder_cell = sent_decoder_cell
self.word_decoder_cell = word_decoder_cell
# Create encoder and decoder RNNChain instances
word_encoder = RNNChain(self.word_encoder_cell, name="hierLSTM_word_encoder", scope="hierLSTM_word_encoder")
sent_encoder = RNNChain(self.sent_encoder_cell, name="hierLSTM_sent_encoder", scope="hierLSTM_sent_encoder")
sent_decoder = RNNChain(self.sent_decoder_cell, name="hierLSTM_sent_decoder", scope="heirLSTM_sent_decoder")
word_decoder = RNNChain(self.word_decoder_cell, name="hierLSTM_word_decoder", scope="hierLSTM_word_decoder")
self.word_encoder = word_encoder
self.sent_encoder = sent_encoder
self.sent_decoder = sent_decoder
self.word_decoder = word_decoder
# Input data contains sequences of input tokens of input_embedding_size dimension
self.input_data = tf.placeholder(tf.float32, [None, self.args.input_seq_length, self.args.input_embedding_size])
# Target data contains sequences of putput tokens of target_token_size dimension (=1)
self.target_data = tf.placeholder(tf.int32, [None, self.args.target_seq_length, self.args.target_token_size])
# Target lengths list contains numbers of non-padding input tokens in each sequence in this batch,
# each i-th element is a list of integers, indicating the number of non-padding tokens in each sentence, and the list's length indicating the number of non-padding sentences in this i-th sequence (which consists of one or more sentences).
self.target_lens_list = tf.placeholder(tf.int32, [None, self.args.input_num_sent])
# Learning rate
self.lr = tf.Variable(self.args.learning_rate, trainable=False, name="learning_rate")
# Initial cell state of LSTM (initialized with zeros)
# TODO: (improve) might use xavier initializer?
self.initial_word_state = word_encoder_cell.zero_state(batch_size=self.args.batch_size, dtype=tf.float32)
self.initial_sent_state = sent_encoder_cell.zero_state(batch_size=self.args.batch_size, dtype=tf.float32)
# Preprocessing the information got from placeholders.
# First, target_lens_list does not need any further actions.
target_lens_list = self.target_lens_list
def __init__(self, args, model_choice="V", test_batch_size=1):
'''
Instantiate a tester.
Params:
args: contains arguments required for the model rebuilding.
model_choice: specify the choice of model, default to "VanillaLSTMTransModel".
V: VanillaLSTMTransModel
H: HierLSTMTransModel, i.e. Hierarchical LSTM Model
A/AH: AttenHierLSTMTransModel, i.e. Hierarchical LSTM Model with Attention
test_batch_size: the size of batch in testing (not necessarily the same as training batch size), default to 1.
Returns:
None.
'''
self.args = args
self.args.model_choice = model_choice
self.test_batch_size = test_batch_size
if model_choice == "VanillaLSTMTransModel" or model_choice == "V":
model_abbr = "V"
elif model_choice == "AttenVanillaLSTMTransModel" or model_choice == "AV":
model_abbr = "AV"
elif model_choice == "HierLSTMTransModel" or model_choice == "H":
model_abbr = "H"
elif model_choice == "AttenHierLSTMTransModel" or model_choice == "AH":
model_abbr = "AH"
else:
raise ValueError("Model choice: " + str(model_choice) +
" is not supported")
self.directory = "../RUN_" + model_abbr
try:
with open(os.path.join(self.directory, 'args.pkl'), 'r+') as f:
saved_args = pickle.load(f)
saved_args.batch_size = self.test_batch_size
except:
raise ValueError(
"This model is either not trained, damaged, or in a wrong path, which should be ../RUN_"
+ model_abbr + "args.pkl")
# Instantiate a model with the saved args
vprint(True,
"\033[1;m" +
"Testing. Rebuilding computation graph for the model..." +
"\033[0;m",
color="CYAN")
if model_choice == "VanillaLSTMTransModel" or model_choice == "V":
model = VanillaLSTMTransModel(saved_args, training=False)
elif model_choice == "AttenVanillaLSTMTransModel" or model_choice == "AV":
model = AttenVanillaLSTMTransModel(saved_args, training=False)
elif model_choice == "HierLSTMTransModel" or model_choice == "H":
model = HierLSTMTransModel(saved_args, training=False)
elif model_choice + "AttenHierLSTMTransModel" or model_choice == "AH":
model = AttenHierLSTMTransModel(saved_args, training=False)
self.model = model
# Instantiate a TensorFlow interactive session
sess = tf.InteractiveSession()
# Initiate a TensorFlow saver
saver = tf.train.Saver(tf.global_variables())
# Get the checkpoint file to load the model
ckpt = tf.train.get_checkpoint_state(checkpoint_dir=self.directory,
latest_filename=None)
# Load the model parameters into session
vprint(True,
"\033[1;m" + "Loading the model parameters..." + "\033[0;m",
color="CYAN")
saver.restore(sess, ckpt.model_checkpoint_path)
# Link the session to Tester.
self.sess = sess
def test(self, printout=False, if_testing=False):
'''
Read in a sequence and output a sequence.
Params:
printout: if True, print the input, target, and output words.
if_testing: if True, use random number.
Returns:
None
'''
# For this testing session
self.total_loss = 0.0
self.loss_on_each_seq = []
rf = open(os.path.join(self.directory, 'test_results.txt'),
'w') # Rewrite
rf.write("Test log file: " + self.directory + '\n')
rf.close()
rtf = open(os.path.join(self.directory, 'test_true_label.txt'),
'w') # Rewrite
# Acquire the model and session
model = self.model
sess = self.sess
data_loader = Dataloader(batch_size=self.test_batch_size,
seq_lengths=[
self.args.input_seq_length,
self.args.target_seq_length
],
token_sizes=[
self.args.input_embedding_size,
self.args.target_token_size
],
usage="test",
if_testing=self.args.test)
# Reset the pointers in the data loader object
data_loader.reset()
#self.num_batches = data_loader.get_num_batches()
self.num_batches = 1
rf = open(os.path.join(self.directory, 'test_results.txt'),
'a') # append from EOF. Create file if not found.
lf = open(os.path.join(self.directory, 'test_log.txt'),
'a') # append from EOF. Create file if not found.
for b in range(self.num_batches):
test_start_time = time.time()
# First, Make predictions
vprint(True, "\nGetting batch.. b = " + str(b + 1), color="MAG")
# x: input batch. It is a list of length test_batch_size, each element of which is of size input_seq_length x input_embedding_size
# y: target batch. It is a list of length test_batch_size, each element of which is of size target_seq_length x target_token_size (=1)
# yl: target sequences' lengths. It is a list of length test_batch_size, each element of which is an integer.
x, y, yl = data_loader.next_batch()
# Feed into the model and get out the prediction
vprint(True,
"Got batch. Making a batch of prediction...",
color="MAG")
#sess.run(tf.assign(self.model.lr, 0.0))
feed = {
self.model.input_data: x,
self.model.target_data: y,
self.model.target_lens_list: yl
}
# output_data is softmaxed. It is a list of length target_seq_length, each element is test_batch_size x output_vocab_size
test_loss, output_data = sess.run(
[self.model.cost, self.model.output_data], feed_dict=feed)
# print "target batch y:"
# for k in xrange(self.test_batch_size):
# print data_loader.data_file_path
# print "-- the sequence # " + str(k+1) + " in this test batch"
# print [list(yki) for yki in y[k]]
# For k-th test batch, it is a sequence
for k in xrange(self.test_batch_size):
for i in xrange(len(y[k])):
yki = int(y[k][i])
rtf.write(str([yki]) + " ")
rtf.write("\n")
print("test_loss = " + str(test_loss))
self.loss_on_each_seq.append(test_loss)
self.total_loss += test_loss
# Second, document the predictions
# For k-th test batch
for k in xrange(self.test_batch_size):
# For i-th toekn position
for i in xrange(len(output_data)):
# If too long - I do not care what the output is beyond a certain length limit
# Allow the output to exceed a little bit - maybe 1.2 times
if i > yl[k] * 1.2:
break
# word_prob_b is the probability distribution over output_vocab_size. It is of size 1 x output_vocab_size
word_prob = output_data[i][k]
#print word_prob
word_index = tf.argmax(word_prob, axis=0)
word_index_singleton = [word_index.eval()]
rf.write(str(word_index_singleton) + " ")
rf.write("\n")
rf.write("\n")
test_end_time = time.time()
vprint(
True,
"time/batch = {:.3f}".format(test_end_time - test_start_time) +
" s",
color=None)
# NOTE that during testing, batch_size = 1. i.e. test the sequences one by one.
# output_data is a list of length target_seq_length, each element of which is of size test_batch_size x output_vocab_size
# For each token position i-th
# for i in xrange(len(output_data)):
# # Each output_data_item is NumPy ndarray, of size test_batch_size x output_vocab_size
# output_data_item = output_data[i]
# # Find the largest probability term as the prediction.
# output_data_item_index = tf.argmax(output_data_item, axis=1)
# # For each test batch 0-th in this token position
# if i >= yl[0]:
# break
# rf.write(str(output_data_item_index.eval()) + " ")
# rf.write("\n")
rf.close()
rtf.close()
vprint(
True, "Total loss of this model is " +
str(self.total_loss / self.num_batches))
lf.write("Total loss: " + str(self.total_loss / self.num_batches) +
"\n")
lf.write("Loss on each sequence: \n")
for i in xrange(len(self.loss_on_each_seq)):
lf.write(str(self.loss_on_each_seq[i]) + "\n")
lf.close()
