def testBuildDataset(self):
# See all the available problems
self.assertTrue(len(problems.available()) > 10)
# Retrieve a problem by name
print(problems.available())
exit(0)
problem = problems.problem("translate_ende_wmt8k")
# Access train and dev datasets through Problem
train_dataset = problem.dataset(MODES.TRAIN)
dev_dataset = problem.dataset(MODES.EVAL)
# Access vocab size and other info (e.g. the data encoders used to
# encode/decode data for the feature, used below) through feature_info.
feature_info = problem.feature_info
self.assertTrue(feature_info["inputs"].vocab_size > 0)
self.assertTrue(feature_info["targets"].vocab_size > 0)
train_example = train_dataset.make_one_shot_iterator().get_next()
dev_example = dev_dataset.make_one_shot_iterator().get_next()
with tf.Session() as sess:
train_ex_val, _ = sess.run([train_example, dev_example])
_ = feature_info["inputs"].encoder.decode(train_ex_val["inputs"])
_ = feature_info["targets"].encoder.decode(train_ex_val["targets"])
def testBuildDataset(self):
# See all the available problems
self.assertTrue(len(problems.available()) > 10)
# Retrieve a problem by name
problem = problems.problem("translate_ende_wmt8k")
# Access train and dev datasets through Problem
train_dataset = problem.dataset(MODES.TRAIN)
dev_dataset = problem.dataset(MODES.EVAL)
# Access vocab size and other info (e.g. the data encoders used to
# encode/decode data for the feature, used below) through feature_info.
feature_info = problem.feature_info
self.assertTrue(feature_info["inputs"].vocab_size > 0)
self.assertTrue(feature_info["targets"].vocab_size > 0)
train_example = train_dataset.make_one_shot_iterator().get_next()
dev_example = dev_dataset.make_one_shot_iterator().get_next()
with tf.Session() as sess:
train_ex_val, _ = sess.run([train_example, dev_example])
_ = feature_info["inputs"].encoder.decode(train_ex_val["inputs"])
_ = feature_info["targets"].encoder.decode(train_ex_val["targets"])
tf.gfile.MakeDirs(data_dir)
tf.gfile.MakeDirs(tmp_dir)
tf.gfile.MakeDirs(train_dir)
tf.gfile.MakeDirs(checkpoint_dir)
gs_data_dir = "./tensor2tensor-data"
gs_ckpt_dir = "./tensor2tensor-checkpoints/"
tf.gfile.MakeDirs(gs_data_dir)
tf.gfile.MakeDirs(gs_ckpt_dir)
"""# Download MNIST and inspect it"""
# A Problem is a dataset together with some fixed pre-processing.
# It could be a translation dataset with a specific tokenization,
# or an image dataset with a specific resolution.
#
# There are many problems available in Tensor2Tensor
problems.available()
# Fetch the MNIST problem
mnist_problem = problems.problem("image_mnist")
# The generate_data method of a problem will download data and process it into
# a standard format ready for training and evaluation.
mnist_problem.generate_data(data_dir, tmp_dir)
# Now let's see the training MNIST data as Tensors.
mnist_example = tfe.Iterator(mnist_problem.dataset(Modes.TRAIN,
data_dir)).next()
image = mnist_example["inputs"]
label = mnist_example["targets"]
plt.imshow(image.numpy()[:, :, 0].astype(np.float32),
cmap=plt.get_cmap('gray'))
# In[ ]:
PROBLEM = "translate_enfr_wmt32k" # We chose a problem translation English to French with 32.768 vocabulary
MODEL = "transformer" # Our model
HPARAMS = "transformer_big" # Hyperparameters for the model by default
# If you have a one gpu, use transformer_big_single_gpu
# In[ ]:
#Show all problems and models
from tensor2tensor.utils import registry
from tensor2tensor import problems
problems.available() #Show all problems
registry.list_models() #Show all registered models
#or
##
#Command line
# get_ipython().system('t2t-trainer --registry_help #Show all problems')
# get_ipython().system('t2t-trainer --problems_help #Show all models')
# # 2. Data generation
#
# Generate the data (download the dataset and generate the data).
#
# ---
#
# You can choose between command line or code.
TRAIN_DIR = os.path.expanduser("/data/t2t/train") # This folder contain the model
EXPORT_DIR = os.path.expanduser("/data/t2t/export") # This folder contain the exported model for production
TRANSLATIONS_DIR = os.path.expanduser("/data/t2t/translation") # This folder contain all translated sequence
EVENT_DIR = os.path.expanduser("/data/t2t/event") # Test the BLEU score
USR_DIR = os.path.expanduser("/data/t2t/user") # This folder contains our data that we want to add
tf.gfile.MakeDirs(DATA_DIR)
tf.gfile.MakeDirs(TMP_DIR)
tf.gfile.MakeDirs(TRAIN_DIR)
tf.gfile.MakeDirs(EXPORT_DIR)
tf.gfile.MakeDirs(TRANSLATIONS_DIR)
tf.gfile.MakeDirs(EVENT_DIR)
tf.gfile.MakeDirs(USR_DIR)
PROBLEM = "translate_enzh_wmt8k" # We chose a problem translation English to French with 32.768 vocabulary
MODEL = "transformer" # Our model
HPARAMS = "transformer_big" # Hyperparameters for the model by default
# If you have a one gpu, use transformer_big_single_gpu
from tensor2tensor.utils import registry
from tensor2tensor import problems
print(problems.available()) #Show all problems
t2t_problem = problems.problem(PROBLEM)
t2t_problem.generate_data(DATA_DIR, TMP_DIR)
import matplotlib.pyplot as plt
import numpy as np
import os
import collections
from tensor2tensor import models
from tensor2tensor import problems
from tensor2tensor.layers import common_layers
from tensor2tensor.utils import trainer_lib
from tensor2tensor.utils import t2t_model
from tensor2tensor.utils import registry
from tensor2tensor.utils import metrics
from invertible_UT_model import *
print(problems.available())
# Enable TF Eager execution
tfe = tf.contrib.eager
tfe.enable_eager_execution()
# Flags for Tensorflow 1.5 +
FLAGS = tf.flags
# Other setup
Modes = tf.estimator.ModeKeys
# Setup some directories
data_dir = os.path.expanduser("~/t2t/data/translate_ende_wmt32k")
tmp_dir = os.path.expanduser("~/t2t/tmp")
train_dir = os.path.expanduser("~/t2t/train/UT_invertible_test")
