def testListModels(self):
@registry.register_model
def m1():
pass
@registry.register_model
def m2():
pass
self.assertSetEqual(set(["m1", "m2"]), set(registry.list_models()))
def testListModels(self):
@registry.register_model
def m1():
pass
@registry.register_model
def m2():
pass
self.assertSetEqual(set(["m1", "m2"]), set(registry.list_models()))
# -*- coding: utf-8 -*-
"""
@author: 代码医生工作室
@公众号：xiangyuejiqiren （内有更多优秀文章及学习资料）
@来源: <深度学习之TensorFlow工程化项目实战>配套代码 （700+页）
@配套代码技术支持：bbs.aianaconda.com (有问必答)
"""
#6-19
import tensorflow as tf
from tensor2tensor import models
from tensor2tensor.utils import t2t_model
from tensor2tensor.utils import registry
print(len(registry.list_models()), registry.list_models())
print(registry.model('transformer'))
print(len(registry.list_hparams()), registry.list_hparams('transformer'))
print(registry.hparams('transformer_base_v1'))
# # Example inputs as int-tensor.
# print("Inputs, encoded:")
# print(inputs)
# print("Inputs, decoded:")
# # Example inputs as a sentence.
# print(decode(inputs))
# # Example targets as int-tensor.
# print("Targets, encoded:")
# print(targets)
# # Example targets as a sentence.
# print("Targets, decoded:")
# print(decode(targets))
# There are many models available in Tensor2Tensor
registry.list_models()
# Create hparams and the model
model_name = "transformer"
hparams_set = "transformer_base"
hparams = trainer_lib.create_hparams(hparams_set,
data_dir=data_dir,
problem_name="translate_ende_wmt32k")
# NOTE: Only create the model once when restoring from a checkpoint; it's a
# Layer and so subsequent instantiations will have different variable scopes
# that will not match the checkpoint.
translate_model = registry.model(model_name)(hparams, Modes.EVAL)
# Copy the pretrained checkpoint locally
def testModelsImported(self):
models = registry.list_models()
self.assertTrue("lstm_seq2seq" in models)
def testModelsImported(self):
models = registry.list_models()
self.assertTrue("baseline_lstm_seq2seq" in models)
# 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.
# Setup helper functions for encoding and decoding
def encode(input_str, output_str=None):
"""Input str to features dict, ready for inference"""
inputs = encoders["inputs"].encode(input_str) + [1] # add EOS id
batch_inputs = tf.reshape(inputs, [1, -1, 1]) # Make it 3D.
return {"inputs": batch_inputs}
def decode(integers):
"""List of ints to str"""
integers = list(np.squeeze(integers))
if 1 in integers:
integers = integers[:integers.index(1)]
return encoders["inputs"].decode(np.squeeze(integers))
# There are many models available in Tensor2Tensor
print(registry.list_models())
# Create hparams and the model
model_name = "invertible_ut"
hparams_set = "universal_transformer_tiny"
hparams = trainer_lib.create_hparams(hparams_set)
############
## CHANGE ##
############
# Adding parameter for determining whether the weights should be shared among the two layers for invertibility or not
hparams.add_hparam("invertible_share_layer_weights", False)
FLAGS.problems = problem_name
FLAGS.model = model_name
